The functioning of different beetle (Coleoptera) sampling methods across altitudinal gradients in Peninsular Malaysia.

Biodiversity research relies largely on knowledge about species responses to environmental gradients, assessed using some commonly applied sampling method. However, the consistency of detected responses using different sampling methods, and thus the generality of findings, has seldom been assessed in tropical ecosystems. Hence, we studied the response consistency and indicator functioning of beetle assemblages in altitudinal gradients from two mountains in Malaysia, using Malaise, light, and pitfall traps. The data were analyzed using generalized linear mixed-effects models (GLMM), non-metric multidimensional scaling (NMDS), multivariate regression trees (MRT), and indicator species analysis (IndVal). We collected 198 morpho-species of beetles representing 32 families, with a total number of 3,052 individual beetles. The richness measures generally declined with increasing altitude. The mountains differed little in terms of light and Malaise trap data but differed remarkably in pitfall-trap data. Only light traps (but not the other trap types) distinguished high from middle or low altitudes in terms of beetle richness and assemblage composition. The lower altitudes hosted about twice as many indicators as middle or high altitudes, and many species were trap-type specific in our data. These results suggest that the three sampling methods reflected the altitudinal gradient in different ways and the detection of community variation in the environment thus depends on the chosen sampling method. However, also the analytical approach appeared important, further underlining the need to use multiple methods in environmental assessments.

Introduction

Most hotspots for global biodiversity can be found in tropical regions [1] which are thus of central importance for conservation. Tropical species often have limited distributions, particularly those species that occupy higher altitudes at mountain slopes [2]. Such mountain species are often endemic to these regions, and also sometimes such that may not be able to move elsewhere if conditions turn unfavorable for them due to, for example, habitat destruction or climate change [3, 4]. Hence, studies on mountain species communities provide crucial information for global conservation efforts, in addition to these environments being particularly vulnerable to habitat degradation and alterations in landscape structure [5, 6]. Our research contributes to the knowledge on biodiversity in Malaysian mountain forests, a little studied biome.

Community composition is largely determined by the relative abundances of and interactions among its species, which contribute to the general biodiversity response to environmental variation. Communities vary according to, among other aspects, latitude and altitude [7-9]. Our understanding regarding this variation relies on some commonly assessed taxonomic groups and associated sampling methods. Similarly, land-use planning or conservation decisions may be based on only a handful of well-known species or a single sampling method. Insects, for instance, are often sampled using light, Malaise, or pitfall traps (e.g., [10]). Environmental assessments, however, are often based on just one sampling method, which is assumed to produce a general biodiversity response, but this assumption has seldom been challenged.

Earlier research indicates that the three mentioned trap types differ in their efficiency in capturing specimens and species (e.g., [11, 12] though the efficiency could vary with the composition of the local species community determined by, for example, altitude or habitat type. As different methods are known to capture partly different species (e.g., [13]), their use may, at least theoretically, result in different conclusions and hence applications in land use or conservation.

The objective of the present study is to evaluate the consistency of light, Malaise and pitfall trap samples in reflecting an altitudinal gradient in tropical Malaysia. More specifically, we compare the three sampling methods across the altitudinal gradient from four viewpoints:

Assemblage species richness;

Assemblage species-compositional turnover;

Assemblage composition; and

Species associated with different combinations of sampling method and altitude

Materials and methods

Study sites

The Titiwangsa mountain range dominates the landscape of Peninsular Malaysia. Within this range, we sampled beetles at Fraser’s Hill (3°43’ N, 101°45’ E) and Genting Highland (3°25’ N, 101°47’ E) that are 95 km apart (Fig 1). Fraser’s Hill mountain tops peak at between 1,000 and 1,800 m a.s.l., whereas the Genting Highland peaks at about 1,800 m a.s.l. In this region, wet and dry seasons cannot be differentiated, as the annual rainfall of 1,800–3,500 mm is distributed throughout the year [14, 15]. Temperature, humidity and luminosity at our study sites, collected for another manuscript by the author MMM, correlated weakly with altitude; correlation coefficients were -0.12 (p = 0.296), -0.27 (p = 0.022) and 0.27 (p = 0.0219) for temperature, humidity and luminosity, respectively.–Collection permits for Fraser’s Hill and Genting Highlands were granted by the Forest Department of Malaysia.

Fig 1

Study location in Malaysia.

Right: map of Malaysia, with altitudes indicated with different colors, from blue (low) to green, yellow, orange and red (high). Left-hand graphs show locations of ten study stands in each study mountain (denoted with letters A-J; compare Table 1); Altitudes are indicated with colors, as in the overall Malaysian map. Map copyright: free open-source maps at https://en-gb.topographic-map.com/legal/; altitudinal data from [69]. We have modified the original maps by adding compass arrows, scale bars and stand codes.

Table 1

Sampling effort at Fraser’s Hill (FH) and Genting Highland (GH) mountains.

Mountain	Altitude	Stand ID	Light	Malaise	Pitfall
FH	500	A	-	-	1
FH	500	B	-	-	4
FH	500	C	1	-	-
FH	1,000	D	1	2	5
FH	1,000	E	1	-	-
FH	1,500	F	1	-	2
FH	1,500	G	-	1	1
FH	1,500	H	1	1	2
FH	1,800	I	1	2	1
FH	1,800	J	1	-	4
GH	500	A	-	1	-
GH	500	B	-	1	-
GH	500	C	2	-	5
GH	1,000	D	-	-	2
GH	1,000	E	2	2	3
GH	1,500	F	-	2	4
GH	1,500	G	-	-	1
GH	1,500	H	2	-	-
GH	1,800	I	2	1	4
GH	1,800	J	-	1	1

In both mountains, traps were set at four Altitudes: 500, 1,000, 1,500 and 1,800 m a.s.l., in ten stands in both mountains; stands are indicated with letters A-J (compare Fig 1). The three right-hand columns show the number of traps (or groups of five pitfall traps) in each stand.

Fraser’s Hill, locally known as Bukit Fraser, is a well-preserved permanently-protected nature reserve located at the Raub district of Pahang state. Fraser’s Hill has been developed as a hill station dating back in 1919 [16], where 90% of 2,800-ha land area is covered by forests [14, 17]. Fraser’s Hill comprises residential areas, commercial complexes, community services, and recreational facilities. The forests mainly consist of tropical montane cloud forests. Lower montane forests–at about 500–1,200 m a.s.l.–are dominated by Fagaceae and Lauraceae trees, whereas upper montane forests–from about 1,200 to 1,800 m a.s.l.–are dominated by Coniferae, Ericaceae, and Myrtaceae trees [18].

Genting Highland is perhaps the most disturbed cloud forest in the Malaysian mountains. The summit is covered by amusement parks, casinos, and hotels [19]. The highest peaks at Genting Highland reach 1,800 m a.s.l, where 96% of the total of 3,965 ha of land is still covered with mostly primary forests [14]. Before Genting Highland became an entertainment site, it was an undisturbed forest that could be reached only via jungle trekking [20, 21].

Sampling methods

We sampled beetles using two Malaise traps, two light traps, and 10 (5 groups of 2) pitfall traps at each altitude (500 m, 1,000 m, 1,500 m, and 1,800 m a.s.l.) at both mountain slopes. All the traps (or trap groups for pitfall traps) were set at least 150 m apart at a given altitude and were placed at least 150 m from the nearest main road. These roads and a river split the sampled forests into a total of twenty distinctive stands; each of these had at least one type of trap (Fig 1, Table 1).

Malaise traps consisted of a nylon net connected to a collection jar, half filled with 70% ethanol and attached to a tree branch about one meter above the ground. Light traps had a mosquito net with attached 160-watt mercury bulb connected to a portable Honda EU10i generator. Pitfall traps were transparent, colourless plastic cups (diameter 65 mm, depth 95 mm) partly filled with 70% ethanol and dug into the ground with the rim flush with the soil surface. We placed large dry leaves above each pitfall trap to protect them from litter and rain.

We sampled beetles once per month in October 2014, and March, June, and September 2015. At each collecting date, Malaise and pitfall traps were set for 24 hours, starting at 08:00 AM, and light traps operated from 18:00 to 23:30. In the latter, beetles were obtained manually from the traps using collection bottles and aspirators. We occasionally continued to use the light traps until the next morning at 06:00 AM but did not capture additional beetles.

In statistical analyses, we pooled the four periods for each trap (or trap group for pitfall traps) and consider each trap (or trap group for pitfall traps) as a replicate (initially 72; 4 altitudes and 2 mountains, each with 2 light and 2 Malaise, and 5 groups of pitfall traps).

Identification of specimens

We sorted, counted, and cross-checked all beetle specimens using available keys [22-36]. We confirmed the identification of difficult specimens at the collections of the Wildlife Department of Malaysia, University of Malaya, National University of Malaysia, and Forestry Department of Malaysia. The identification of beetles to species is hampered by the lack of experts and species compilations. In the present study, the samples contained at least five species new to science, which will be described in later papers. Clearly, the often broad taxonomic levels and the shortage of knowledge about the ecological traits of species must be acknowledged while interpreting results, as similar-looking species might be different in terms of, for example, life cycles, diets, abilities to disperse, and habitat requirements.

Statistical analysis

We had initially 72 samples (16 for light, 16 for Malaise, and 40 for pitfall traps [5 sets * 2 * 4 altitudes}). Three samples, however, produced only 1–2 species and were excluded due to the difficulty in calculating pair-wise dissimilarities and richness estimates (one light and two Malaise trap samples). Thus, we ran all analyses with 69 samples (Table 1). We ran the following analyses for the four viewpoints proposed above.

Assemblage richness

To assess sampling-method dependent variation in species richness, we subjected the beetle data to a generalized linear mixed-effects model (GLMM; [37] to quantify variation according to altitude (500, 1,000, 1,500, or 1,800 m a.s.l.). We ran the model separately for each sampling method (light, Malaise, or pitfall traps; hereafter “Method” for brevity) to be able to compare their similarity in responses to altitude. We subjected the observed number of species, richness estimates based on rarefaction standardization to 5, 10, and 20 individuals, and asymptotic values of coverage-based rarefaction estimates (hereafter coverage-based asymptotic richness; [38, 39] to a GLMM with Altitude as a fixed factor and Mountain (Fraser’s Hill or Genting Highland; Fig 1, Table 1) as a random factor (to account for spatial autocorrelation), using lme4 [40]. For full model outputs, see S1 Table.

For linear models it is important to assess the normality and homoskedasticity of model residuals, which we did in two ways. Firstly, we inspected Q-Q plots of GLMM residuals visually (car package; [41], confirmed by Wilk-Shapiro test for residual normality (S1 Fig and S2 Table). These did not indicate major departures from normality, except in the pitfall-trap data. Secondly, we ran Wilk-Shapiro test to check the normality, and Breusch-Pagan test to assess homoskedasticity, of ANOVA residuals (model Mountain + Altitude; S3 Table). These checks indicated the following issues: (i) heteroskedasticity in the number of species and non-normality in the rarefied richness to 5 individuals in the light-trap data; (ii) heteroskedasticity in the number of species in the Malaise-trap data; and (iii) non-normality in the rarefied richness standardized to 5 and 10 individuals and coverage-based asymptotic richness in the pitfall-trap data (as for GLMM residuals above). Due to these deviations from normality, we reran the GLMM models (as in Table 2) using Robust LMM (robustlmm package; [42]. Models run using biological data often contain outlier samples that may render residual distributions non-normal or heteroskedastic. Estimates from Robust LMM are little affected by such outliers, if the tuning parameter (k) is set at a low value; values approaching ∞ produce results similar to a normal GLMM. Here, we applied the smoothed Huber (k = 1.345, s = 10) function for fitting random effects (Mountain) and variance component (Altitude), as recommended by [42] (S4 Table). As the relative magnitudes and directions of estimates in Robust LMM were very similar to the initial GLMM (with the exception of coverage-based asymptotic richness for pitfall traps), we conclude that our GLMM results shown in Table 2 are robust.

Table 2

GLMM summary for altitude, using mountain as a random factor, for the number of species and rarefaction standardized number of species for 5, 10 or 20 individuals.

	LIGHT TRAPS		MALAISE TRAPS		PITFALL TRAPS
Variable/Category	%var	Effect	%var	Effect	%var	Effect
Number of species
Mountain	7		4		53
Altitude	41		78		11
* 1,000 m a.s.l.		ns		neg		(pos)
* 1,500 m a.s.l.		ns		neg		ns
* 1,800 m a.s.l.		neg		neg		ns
Residuals	52		19		35
Rarefied richness to 5 individuals
Mountain	7		6		17
Altitude	33		70		16
* 1,000 m a.s.l.		ns		ns		pos
* 1,500 m a.s.l.		ns		ns		ns
* 1,800 m a.s.l.		(neg)		neg		ns
Residuals	60		24		67
Rarefied richness to 10 individuals
Mountain	15		7		26
Altitude	45		72		11
* 1,000 m a.s.l.		ns		ns		pos
* 1,500 m a.s.l.		ns		ns		ns
* 1,800 m a.s.l.		neg		neg		ns
Residuals	40		21		63
Rarefied richness to 20 individuals
Mountain	4		7		28
Altitude	61		72		12
* 1,000 m a.s.l.		ns		(neg)		pos
* 1,500 m a.s.l.		ns		neg		ns
* 1,800 m a.s.l.		neg		neg		ns
Residuals	35		21		59
Coverage-based asymptotic richness
Mountain	17		25		35
Altitude	41		57		3
* 1,000 m a.s.l.		ns		neg		ns
* 1,500 m a.s.l.		ns		neg		ns
* 1,800 m a.s.l.		neg		(neg)		ns
Residuals	42		18		61

Results are shown for light, Malaise and pitfall trap data. Numbers in “%var” columns are percentages explained by a given variable; letters in”Effect” columns show whether a given Altitude differed significantly (p < 0.05) and positively (pos) or negatively (neg) from the lowest Altitude (500 m a.s.l.) (in parentheses if marginally significant, i.e., p < 0.1) or whether this comparison was non-significant (ns; p >0.1). For full output, see S1 Table.

Assemblage turnover

To examine possible species turnover according to altitude and Method, we calculated averages and standard errors for 20 most abundant species, separately for each Altitude and Method. We plotted these values according to Altitude, using the rank order of the abundances of species captured using each Method. We evaluated the community turnover, or distinctiveness of samples, between Altitudes using permutational multivariate ANOVA (the adonis function in R package vegan, with Mountain [Fraser’s Hill or Genting Highland] as strata; [43]. Here, we only considered single or combinations of subsequent Altitudes. Thus, for example, a combination 500 + 1000 m was considered but not 500 + 1800 m. As a simple measure of turnover between Methods, we indicated in these plots species that were unique for a given Method, and those species that were shared with 1–2 other Methods. We confirmed this comparison of Methods using permutational multivariate ANOVA, as described above.

Assemblage composition

To examine beetle assemblage composition across Altitudes and Methods, we used two analyses. Firstly, we used non-metric multidimensional scaling (NMDS; [44]) to assess variation in community composition, using the vegan package [43]. We used Method-specific data sets by applying a Bray-Curtis dissimilarity matrix. We used the above-described permutational multivariate ANOVA for Altitudes as a confirmation of the NMDS result. Secondly, we subjected the Method-specific beetle data sets to multivariate regression tree analysis (MRT; [45]) based on the Bray-Curtis dissimilarity matrix, using the mvpart package [46]. We used altitude and, as our earlier research has indicated differences in beetle faunas between Fraser’s Hill and Genting Highland [47], Mountain as explanatory variables. MRT identifies groups of samples as defined by explanatory variables (Mountain and Altitude) and is not restricted by non-linearities [45]. We present the result as a tree of dichotomies, where each dichotomy is based on minimizing the dissimilarity of samples within each tree branch. We report the tree with the lowest cross-validated relative error, following the 1-SE rule [48]. Cross-validated relative error provides a better estimate than relative error for the predictive accuracy of the MRT for a new dataset [45].

Species associations with altitude and method

To detect species characteristic to particular combinations of Method and Altitude, we calculated an indicator value (IndVal; [49, 50] for each species, based on all logical combinations of Method and Altitude. Here, we used the indicspecies package [51] and allowed each Method to appear singly or jointly with 1–2 other Methods, whereas for Altitude we only considered single, or combinations of subsequent, Altitudes, as described above. We restricted the IndVal to species with a total sample of at least five individuals.

Results

Beetle richness according to mountain and altitude

We collected 198 morpho-species of beetles representing 32 families with a total number of 3052 individuals (S5 Table). Nine taxa were identified to species, 143 to genus, and 43 to higher taxonomic levels. We refer to all these as “species” below for convenience. Regarding Method, we collected 107 species using light traps, 127 using Malaise, and 45 using pitfall traps. A total of 135 species were represented by only one Method, whereas 45 had been captured with two and 18 with all three sampling methods. Altogether 98 species were singletons or doubletons, whereas 84 were represented by at least five individuals (S5 Table). Species accumulation curves based on rarefaction suggested that pitfall traps had captured nearly all potential species at about 200 individuals at both Mountains, whereas the accumulation curves for light and Malaise traps were still very steep at about 400–500 individuals (Fig 2).

Fig 2

Rarefaction curves for the three trap types and the pooled sample.

The end point of each curve indicates the trap-type specific or pooled (total) number of individuals.

The GLMM performed rather similarly in terms of the four measures of richness, but the three Methods produced partly different results (Table 2, Fig 3; S1 Table). The Mountains differed little in terms of light and Malaise trap data but remarkably in pitfall-trap data. The richness measures based on light traps declined with Altitude but only 1,800 m differed significantly from 500 m. The decline with Altitude occurred also in Malaise traps so that all higher Altitudes differed significantly from 500 m. Richness measures based on pitfall traps, on the other hand, peaked at 1,000 m (Table 2, Fig 3).

Fig 3

Average samples + SE of four measures of species richness for three sampling methods according to altitude.

Number of species, and rarefaction standardized richness based on 5, 10 and 20 individuals are shown. Note that the X axis length for pitfall trap samples is different.

Species turnover

The rank-abundance plots of twenty most abundant species (Fig 4) reflected relatively high similarity between light and Malaise traps, with nine of the 20 most numerous species being shared (grey columns), whereas only one of the 20 dominant species–Pityogenes sp1 –in pitfall-trap samples was shared with light- and Malaise-trap samples (white columns). For species identities in this graph, see S6 Table. Permutational multivariate ANOVA indicated that also light and Malaise traps were compositionally different; for light vs. Malaise traps, F = 3.21, R2 = 0.11, p = 0.0010; for light vs. pitfall traps F = 9.41, R2 = 0.15, p = 0.0010; and for Malaise vs. pitfall traps F = 10.48, R2 = 0.17, p = 0.0010. Moreover, the plots suggested more abundance or occurrence changes with Altitude in Malaise and light traps, whereas the plots varied less and more erratically in pitfall-trap samples (Fig 4). Another striking pattern in light-trap samples (Fig 4, left) was the lack of the 12 most abundant species at 1,800 m. Only 9% of light-trap species and 17% of Malaise-trap species were found in both 500 m and 1,800 m, whereas the percentage was 63 for pitfall-trap samples.

Fig 4

Rank abundance (mean + SE) of twenty most abundant species in each trap type at different altitudes (500, 1,000, 1,500 and 1,800 m a.s.l.).

Note that species are mostly different for each trap type, each sorted according to the rank order of the total number of individuals in that trap type. Grey columns show species that were shared between light and Malaise traps, and white columns show the shared species between pitfall, light and pitfall traps (one species). For species identities, see S5 Table.

Permutational multivariate ANOVA confirmed compositional changes with Altitude, particularly at 1,800 m, but the three Methods showed different patterns in this respect (Table 3). In light traps, only 1,800 m differed significantly from the other Altitudes; however, all except one combination of 2–3 Altitudes also differed significantly from the rest of the Altitudes. The patterns were similar for Malaise traps, except that different combinations of 2–3 Altitudes differed less commonly from the rest of the Altitudes. Regarding pitfall traps, then, all Altitudes and their combinations (except for 1,000–1,500 m) had distinctive assemblages (Table 3).

Table 3

Permutational multivariate ANOVA to assess the distinctiveness of beetle communities at different altitudes (Alt 500, 1,000, 1,500 or 1,800 m a.s.l., or their logical combinations) as reflected by using three sampling methods (compare Fig 2).

	LIGHT TRAPS						MALAISE						PITFALL
Variable	df	SS	MS	F	R2	p	df	SS	MS	F	R2	p	df	SS	MS	F	R2	p
Alt 500	1	0.47	0.47	1.28	0.09	0.0659	1	0.40	0.40	1.17	0.09	0.2717	1	0.48	0.48	1.58	0.04	0.0300
Residuals	13	4.75	0.37	0.91			12	4.12	0.34	0.91			38	11.55	0.30	0.96
Alt 1000	1	0.47	0.47	1.29	0.09	0.1548	1	0.42	0.42	1.23	0.09	0.1528	1	0.97	0.97	3.34	0.08	0.0010
Residuals	13	4.75	0.37	0.91			12	4.10	0.34	0.91			38	11.06	0.29	0.92
Alt 1500	1	0.53	0.53	1.47	0.10	0.0939	1	0.42	0.42	1.24	0.09	0.1299	1	0.46	0.46	1.51	0.04	0.0390
Residuals	13	4.69	0.36	0.90			12	4.10	0.34	0.91			38	11.57	0.30	0.96
Total	14	5.22	1.00				13	4.52	1.00				39	12.03	1.00
Alt 1800	1	1.36	1.36	4.56	0.26	0.0020	1	0.70	0.70	2.21	0.16	0.0030	1	0.76	0.76	2.56	0.06	0.0020
Residuals	13	3.87	0.30	0.74			12	3.82	0.32	0.84			38	11.27	0.30	0.94
Alt 500–1000	1	0.67	0.67	1.93	0.13	0.0290	1	0.47	0.47	1.40	0.10	0.0699	1	0.97	0.97	3.33	0.08	0.0010
Residuals	13	4.55	0.35	0.87			12	4.05	0.34	0.90			38	11.06	0.29	0.92
Alt 1000–1500	1	0.98	0.98	3.01	0.19	0.0010	1	0.68	0.68	2.13	0.15	0.0420	1	0.55	0.55	1.82	0.05	0.0070
Residuals	13	4.24	0.33	0.81			12	3.84	0.32	0.85			38	11.48	0.30	0.95
Alt 1500–1800	1	0.67	0.67	1.93	0.13	0.0380	1	0.47	0.47	1.40	0.10	0.0619	1	0.97	0.97	3.33	0.08	0.0010
Residuals	13	4.55	0.35	0.87			12	4.05	0.34	0.90			38	11.06	0.29	0.92
Alt 500–1500	1	1.36	1.36	4.56	0.26	0.0020	1	0.70	0.70	2.21	0.16	0.0050	1	0.76	0.76	2.56	0.06	0.0020
Residuals	13	3.87	0.30	0.74			12	3.82	0.32	0.84			38	11.27	0.30	0.94
Alt 1000–1800	1	0.47	0.47	1.28	0.09	0.0699	1	0.40	0.40	1.17	0.09	0.2408	1	0.48	0.48	1.58	0.04	0.0210
Residuals	13	4.75	0.37	0.91			12	4.12	0.34	0.91			38	11.55	0.30	0.96

Assemblage composition according to Method and Altitude

According to NMDS, samples in light and Malaise traps distinguished 1,800 m from the three lower altitudes, whereas pitfall trap samples formed two sample clusters, of which only the other distinguished clearly between Altitudes (Fig 6). Thus, in pitfall-trap samples, Altitude was reflected in Fraser’s Hill but not in Genting Highland (on the left and right, respectively, in Fig 5).

Fig 6

MRT for sampling-method specific beetle data, using mountain (Fraser’s Hill or Genting Highland) and altitude (500, 1,000, 1,500 or 1,800 m a.s.l.) as explanatory variables.

The column plots show the relative abundance of each species captured using a given trap type, sorted according to the rank order of abundance of the total trap-type specific sample. Numbers in parentheses below each end branch show the number of samples falling into that branch.

Fig 5

NMDS plots for Malaysian beetle data, separate runs for three sampling methods (light, Malaise or pitfall traps) at different altitudes (500 m = black squares, 1,000 m = red circles, 1,500 m = green up-triangles and 1,800 m = blue down-triangles).

For statistical comparisons between Altitudes, see Table 3.

MRT for light-trap data consistently resulted in a one-dichotomy tree (Fig 6). The only division, where 1,800 m was split from the rest of the Altitudes, explained 47% of the variation in light-trap data, the former being species poor as is evident from rank-abundance plots in the end branches of MRT (Fig 6). Malaise-trap data, on the other hand, suggested that the same dichotomy, based on Altitude, was apparent in Fraser’s Hill but not in Genting Highland; again, 1,800 m had fewer species though this was based on only two samples. This two-dichotomy tree explained 81% of the variation in the Malaise trap data (Fig 6). Also, pitfall trap data suggested that Altitudes could be distinguished at Fraser’s Hill but not at Genting highland; here, 1,500 and 1,800 m diverged from the two lower Altitudes that, in turn, were split in the third dichotomy (Fig 6).

Species associated with different combinations of Method and Altitude

We found 83 indicator species for different combinations of Method and Altitude (Table 4). Thirteen species were commonly caught with two sampling methods (12 for light and Malaise traps, and one with Malaise and pitfall traps). Moreover, eight species indicated pitfall traps across the full altitudinal range (500–1,800 m), whereas we did not detect such altitude-independent indicators for the two other sampling methods. Light traps produced 12 indicators of low (500–1,000 m), six of the middle (1,000–1,500 m) and seven of high (1,500–1,800 m) Altitudes, whereas the respective numbers were 16, four, and six for Malaise and ten, eleven and five for pitfall traps (Table 4). Moreover, three species occurred in pitfall traps across a wider Altitudinal range: two species were found at 500–1,500 m and one at 1,000–1,800 m.

Table 4

Significant (p < 0.05) indicators with n > 4 in the beetle data.

Category	Species	IndVal	Category	Species	IndVal
Single-method indicators			Single-method indicators continued
LT 500	Anomala sp2	0.81	PT	Harpalus sp1	0.84
LT 500	Anomala sp4	0.76	PT	Harpalus sp2	0.91
LT 500	Anomala sp6	0.57	PT	Hiletus sp1	0.44
LT 500	Apogonia sp3	0.79	PT	Inopeplus sp1	0.63
LT 500	Apogonia sp5	0.79	PT	Lebia sp1	0.57
LT 500	Cicindela sp2	0.79	PT	Omonadus sp1	0.52
LT 500	Mordellidae A	0.80	PT	Pentagonica sp1	0.54
LT 500	Scarabeidae M	0.80	PT	Staphylinidae M	0.63
LT 500	Scarabeidae P	0.58	PT 500	Pterostichus sp2	0.87
LT 500–1000	Epepeotes lateralis	0.52	PT 500–1500	Spinolyprops A	0.53
LT 1000	Apogonia sp1	0.90	PT 500–1500	Staphylinidae C	0.61
LT 1000	Cicindela sp1	0.69	PT 1000	Actiastes sp1	0.71
LT 1000–1500	Anomala sp1	0.94	PT 1000	Anotylus sp2	0.86
LT 1000–1500	Apogonia sp2	0.96	PT 1000	Bledius sp1	0.78
LT 1500	Lampyridae E	0.70	PT 1000	Lispinus sp1	0.84
LT 1500	Luciola sp1	0.71	PT 1000	Orphnebius sp1	0.84
LT 1500–1800	Altica sp1	0.69	PT 1000	Orphnebius sp2	0.92
LT 1800	Cleorina sp	0.71	PT 1000	Passalidae A	0.44
LT 1800	Hoplocerambyx spinicornis	0.87	PT 1000	Pityogenes sp1	0.74
LT 1800	Hydrovatus enigmaticus	0.97	PT 1000	Sunius sp1	0.71
LT 1800	Illeis sp2	0.70	PT 1000–1800	Oxylatus sp1	0.57
MA 500	Aleocharinae sp1	0.84	PT 1500	Hiletus sp2	0.58
MA 500	Anotylus sp1	0.94	PT 1500	Pterostichus sp1	0.79
MA 500	Aphthona sp1	0.98	PT 1500–1800	Paedarus sp1	0.69
MA 500	Brachypeplus sp1	0.92	PT 1800	Lebia sp2	0.57
MA 500	Brachypeplus sp3	0.98	PT 1800	Pterostichus sp3	0.79
MA 500	Bradymerus sp2	0.99
MA 500	Epuraea sp1	0.70	Multiple-method indicators
MA 500	Galerucinae sp1	0.97	MA+LT 500	Mulsanteus sp1	0.60
MA 500	Ischnosoma sp1	0.95	MA+LT 500	Paederinae sp3	0.68
MA 500	Lymantor sp1	0.94	MA+LT 1000	Sarmydus sp1	0.71
MA 500	Lymantor sp2	0.91	MA+LT 1000–1500	Strotocera sp1	0.71
MA 500	Lymantor sp3	0.94	MA+LT 1500	Alticinae sp2	0.69
MA 500	Sinoxylon sp1	0.99	MA+LT 1500	Anisandrus sp1	0.68
MA 500	Xyleborus sp1	0.98	MA+LT 1500	Brachypeplus sp2	0.65
MA 500–1000	Aleocharinae sp2	0.58	MA+LT 1500	Colaspoma sp2	0.71
MA 1000	Anomala sp3	0.82	MA+LT 1500	Curculionidae A	0.70
MA 1000–1500	Alticinae sp1	0.70	MA+LT 1500	Meloidae A	0.71
MA 1500	Paederinae sp2	0.66	MA+LT 1500	Nisotra sp2	0.71
MA 1500–1800	Xyleborus sp2	0.71	MA+LT 1500	Pityogenes sp2	0.48
MA 1800	Anomala sp5	0.66	MA+PT 500	Aleocharinae sp3	0.62
MA 1800	Apogonia sp4	0.69
MA 1800	Lymantor sp4	0.90
MA 1800	Xylothrips sp1	0.68

LT = indicator of light traps; MA = indicator of Malaise traps; PT = indicator of pitfall traps; different Altitudes or ranges are shown with numbers (500, 1,000, 1,500, 1,800 m a. s. l.). Column IndVal shows indicator value for each taxon.

Discussion

Beetle species turnover between methods and across the altitudinal gradient

We found that light and Malaise trap samples shared about half of the abundant species, whereas pitfall trap samples were distinctive in this respect. Light and Malaise traps may have attracted a shared pool of species that flies actively, whereas pitfall traps capture mostly ground dwellers that seldom fall into the two other types of traps (e.g., [13]). This finding suggests that, at least in the study region, the variation in beetle communities caused by altitude or associated climatic factors may be better captured if pitfall traps are used together with either light or Malaise traps, whereas a combination of light and Malaise traps may not be equally efficient.

At 1,800 m light (but not Malaise or pitfall) trap samples lost all 12 most abundant species of the total light-trap sample. This loss might have resulted from the captured species pool being more sensitive to altitude-associated changes in abiotic or biotic conditions. However, some of the 12 “lost” species were still captured at 1,800 m using Malaise traps, so another explanation may be trap functioning. Perhaps light or wind conditions–or some other factors related to, for example, vegetation or moisture–were different at the highest altitudes, which might, in turn, have impacted the visibility of light traps or flight activity of many species [52-56]. It would be important to continue beetle monitoring at these mountain tops to see whether the altitudinal distributions of species indeed change with climate and whether the currently dominant mountain-top species persist or disappear.

The relative similarity of rank-abundance plots at different altitudes, particularly for pitfall-trap samples, may have occurred because many dominant species are adapted to a wide range of altitudes (e.g., [57] and/or temperature or moisture conditions [58, 59]. Pitfall traps capture mostly ground dwellers, and field-layer vegetation or soil conditions may thus have kept conditions relatively constant across the altitudinal gradient for these species (see also Materials and methods). These factors might decrease variation in micro-climate [60], perhaps through the sheltering effect of forest trees [61].

Beetle community structure across the altitudinal gradient using different methods

Our community analyses suggest that the overall beetle community varied remarkably according to altitude, but the magnitude of this response depended on geographic location (the two mountains) and captured species subset (sampling method). Thus, according to NMDS and MRT, light traps distinguished 1,800 m from lower altitudes, whereas Malaise and pitfall traps reflected primarily differences between the two mountains and only secondarily altitudinal variation. Moreover, and only at Fraser’s Hill, Malaise traps distinguished 1,800 m from the lower altitudes, and pitfall traps distinguished 500, 1,000, and 1,500–1,800 m.

Indicator species for sampling methods and altitudes

Pitfall traps produced eight indicator species that were common at all altitudes, whereas the other sampling methods did not produce such altitude generalists (for identities of these species, see Table 4). This finding is in line with our rank-abundance plots (Fig 4). Moreover, all sampling methods produced at least some indicators of low (500–1,000 m), intermediate (1,000–1,500 m), and/or high altitudes (1,500–1,800 m), suggesting potential for each method to reflect altitude and land use. Important from a conservation perspective, we found nine significant indicators of 1,800 m (four using light, four using Malaise, and one using pitfall traps). Species adapted to high altitudes face risks posed by intensifying land use, as exemplified by pollinators [62, 63], but with predicted climate warming, poorly-dispersing species occupying mountain tops have limited chances to spread (e.g., [64]). These sorts of phenomena may also act in concert, which would warrant follow-ups of sampling in the studied mountains.

In our study area, the two mountains hosted very different beetle communities, which perhaps partly reflects different intensities of land-use. The studied sampling methods differ in costs, required labor, relative capturing efficiency, and captured species pool. The differences reported here suggest that the use of more than one location and several sampling methods are desirable in environmental assessments [12, 65, 66].

Research caveats

Taxonomic constraints may have affected our results to some extent, as most taxa had been identified to family or genus levels only. Thus, two individuals of a given taxon being captured using two methods or at different altitudes might in reality be two different species. However, this possibility rather masks than exaggerates the true community responses. We are therefore confident that the sampling methods and altitudes truly differed in beetle communities, but the differences would have been more pronounced had we been able to identify everything to species.

Conclusions

The location of the present study, Malaysia, belongs to the global biodiversity hotspot of Sundaland, yet little is known about the invertebrate diversity of its mountains [67, 68]. Results of our study are applicable to tropical species conservation: they provide evidence for adaptations of many species to particular altitudes and, more importantly, differences in beetle samples between collecting methods. The light and Malaise traps showed little difference in terms of species composition but differed remarkably from pitfall-trap data. The three sampling methods also reflected the altitudinal gradient in different ways, and many species were trap-type specific. Clearly, caution is required while interpreting environmental impact on biodiversity based on one sampling method only. Whenever possible, we strongly recommend multiple collecting methods in environmental impact assessments on biodiversity. This is particularly important in land-use or political decision making, which should ideally be based on a holistic picture of biodiversity to avoid unwanted species losses or changes in ecosystem functioning.

GLMM outputs for data collected using different sampling methods.

(DOCX)

Click here for additional data file.

Wilk-Shapiro test for GLMM residuals plotted in S1 Fig.

(DOCX)

Click here for additional data file.

Wilk-Shapiro (W) and Breusch-Pagan (BP; df = 4) tests for ANOVA residuals; model Mountain + Altitude.

(DOCX)

Click here for additional data file.

Robust LMM output for the number of species, rarefied richness standardized to 5, 10 or 20 individuals and coverage-based asymptotic richness for three trapping methods.

(DOCX)

Click here for additional data file.

Species captured using three collecting methods at the two study mountains; numbers are total catches.

(DOCX)

Click here for additional data file.

Twenty most abundantly captured species in our data, sorted according to trap-type specific sample sizes; n indicates number of individuals in the data.

Light gray: species shared between light and Malaise traps; dark gray: species shared with pitfall, light and Malaise traps.

(DOCX)

Click here for additional data file.

Q-Q plots for GLMM residuals, shown separately for three sampling methods (compare S1 Table).

Different rows show plots for the number of species, rarefaction standardized richness based on 5, 10 or 20 individuals, and coverage-based (CB) rarefied asymptotic richness. Solid line shows perfect fit, dash lines show 95% confidence intervals. Ideally, all sample residuals should fall between confidence intervals.

(DOCX)

Click here for additional data file.

16 Mar 2021

PONE-D-21-03456

Community composition and indicator functioning of beetles across environmental gradients in Peninsular Malaysia

PLOS ONE

Dear Dr. Musthafa,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by 15th June 2021. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Daniel de Paiva Silva, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. In your Methods section, please provide additional information regarding the permits you obtained for the work. Please ensure you have included the full name of the authority that approved the field site access and, if no permits were required, a brief statement explaining why.

3. In your Data Availability statement, you have not specified where the minimal data set underlying the results described in your manuscript can be found. PLOS defines a study's minimal data set as the underlying data used to reach the conclusions drawn in the manuscript and any additional data required to replicate the reported study findings in their entirety. All PLOS journals require that the minimal data set be made fully available. For more information about our data policy, please see http://journals.plos.org/plosone/s/data-availability.

Upon re-submitting your revised manuscript, please upload your study’s minimal underlying data set as either Supporting Information files or to a stable, public repository and include the relevant URLs, DOIs, or accession numbers within your revised cover letter. For a list of acceptable repositories, please see http://journals.plos.org/plosone/s/data-availability#loc-recommended-repositories. Any potentially identifying patient information must be fully anonymized.

Important: If there are ethical or legal restrictions to sharing your data publicly, please explain these restrictions in detail. Please see our guidelines for more information on what we consider unacceptable restrictions to publicly sharing data: http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access.

We will update your Data Availability statement to reflect the information you provide in your cover letter.

4. We note that Figure 1 in your submission contain map images which may be copyrighted. All PLOS content is published under the Creative Commons Attribution License (CC BY 4.0), which means that the manuscript, images, and Supporting Information files will be freely available online, and any third party is permitted to access, download, copy, distribute, and use these materials in any way, even commercially, with proper attribution. For these reasons, we cannot publish previously copyrighted maps or satellite images created using proprietary data, such as Google software (Google Maps, Street View, and Earth). For more information, see our copyright guidelines: http://journals.plos.org/plosone/s/licenses-and-copyright.

We require you to either (1) present written permission from the copyright holder to publish these figures specifically under the CC BY 4.0 license, or (2) remove the figures from your submission:

(1) You may seek permission from the original copyright holder of Figure 1 to publish the content specifically under the CC BY 4.0 license.

We recommend that you contact the original copyright holder with the Content Permission Form (http://journals.plos.org/plosone/s/file?id=7c09/content-permission-form.pdf) and the following text:

“I request permission for the open-access journal PLOS ONE to publish XXX under the Creative Commons Attribution License (CCAL) CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). Please be aware that this license allows unrestricted use and distribution, even commercially, by third parties. Please reply and provide explicit written permission to publish XXX under a CC BY license and complete the attached form.”

Please upload the completed Content Permission Form or other proof of granted permissions as an "Other" file with your submission.

In the figure caption of the copyrighted figure, please include the following text: “Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”

(2) If you are unable to obtain permission from the original copyright holder to publish these figures under the CC BY 4.0 license or if the copyright holder’s requirements are incompatible with the CC BY 4.0 license, please either i) remove the figure or ii) supply a replacement figure that complies with the CC BY 4.0 license. Please check copyright information on all replacement figures and update the figure caption with source information. If applicable, please specify in the figure caption text when a figure is similar but not identical to the original image and is therefore for illustrative purposes only.

The following resources for replacing copyrighted map figures may be helpful:

USGS National Map Viewer (public domain): http://viewer.nationalmap.gov/viewer/

The Gateway to Astronaut Photography of Earth (public domain): http://eol.jsc.nasa.gov/sseop/clickmap/

Maps at the CIA (public domain): https://www.cia.gov/library/publications/the-world-factbook/index.html and https://www.cia.gov/library/publications/cia-maps-publications/index.html

NASA Earth Observatory (public domain): http://earthobservatory.nasa.gov/

Landsat: http://landsat.visibleearth.nasa.gov/

USGS EROS (Earth Resources Observatory and Science (EROS) Center) (public domain): http://eros.usgs.gov/#

Natural Earth (public domain): http://www.naturalearthdata.com/

Additional Editor Comments:

Dear Musthafa et al.,

after the full consideration of your manuscript by two independent reviewers, I believe your manuscript could be accepted for publication in PLoS One after you perform a major review to your text. Both reviewers raised significant changes to be made in your text, that I believe will significantly improve its final version of the text. Considering the current COVID-19 pandemic scenario, I believe that a three months period will be more than enough for you to perform the necessary changes to your manuscript. Therefore, please resubmit your text up to June 15th 2021. Please, do not forget to prepare a rebuttal letter, informing the reviewers the changes you agreed on and those you disagreed. In this rebuttal letter, you will explain each and every improvement you did, considering the reviewers' suggestions and also the reasons why you did not do some of the changes (in case it happens).

If you are able to finish the new version of the text before the due date specified above, do not hesitate to resubmit earlier. Still, in case the three months period is not enough. Please let me or PLoS One's staff know, so we can find a better due date that fits your needs. Finally, in case you have any other doubts, please contact me without hesitation.

Sincerely,

Daniel Silva

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: No

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: • TITLE

o The title does not reflect the main idea of the research. In my view, the main objective is to test different trap methodologies.

o The functioning and environmental gradient do not add up to the work. The study is only with altitudes gradients.

• INTRODUCTION

o I missed in the introduction a better explanation about the methods. Why do you expect that the traps will have different outcomes? How beetle biology may affect these differences?

o The Climate change sentence is lost in the text, and don’t add up to your objective, you can use it in the discussion and conclusion part, but it does not make any difference in the introduction.

o need to make it clearer in the introduction that the goal in the introduction is to compare the methods.

• METHODS

o Could you show the species accumulation curve as supplemental information, with the hole composition as well.

o What is the letters A, B, C, D, E, F, G, H? => If they are Stands you have to explain better.

o Get the GLMM model more explicit.

o Figure 2 has some problems. Standardize the axes so you can better compare the results. It would be good to add a table with richness values by the mountain, altitude, and trap.

o Figure 3 identifies the species, by name in the legends.

• RESULTS

o NMDS- The 1800 altitude separation becomes evident only in the permanova.

• DISCUSSION

o Discuss more the implications of the differences of the methods.

o What is the bias towards decision-making using only a method and possible vantages to use more than one method?

o If you are talking about trap efficiency, since pitfall with a lesser richness capture differences in all elevations, and combinations I would assume this is the most efficient method (if you emphasize more the different biologies related to each trap you can sell better the idea of complementary survey methods).

o Line 263: wind direction is duplicated.

o

• CONCLUSION

o The recommendation part needs to be better, talk about the biology of beetles and how this would affect the methodology choice of your research. Remember that methodology should adequate to the question made.

o Improve the text so it is not a resume of the paper, you can make a more thorough recommendation, a guide for decision making. As it is now the recommendation is too subtle.

• FIGURES

o Figura 1 – Put the names of the mountains in the first image. Identify the images A, B e C.

o Figure 4: Use different colors for elevation and different symbols for mountains.

• TABLES

o Why only 500m in this table? Explain this better in the methods.

• GENERAL

o Text organization of methods and results is really good and with easy understanding.

Reviewer #2: The paper titled “Community composition and indicator functioning of beetles across environmental gradients in Peninsular Malaysia” has important contributions for studies on environmental gradients. The comparison of methods is interesting and this is the main objective of the work. The authors did a huge sampling effort and made a good analysis proposal. However, the paper has some issues and some improvements are needed. Most issues are related to the organization of ideas. Also, the paper has some writing issues. The English is good, but they need to improve the writing. I made several comments along the text trying to highlight some critical points, but I recommend a full text revision. Two specific things that were hard to understand were the sampling design and the analysis descriptions, for which they need to provide more information. Moreover, they need to improve the model diagnostics. I made some suggestions in the Material and Methods. The figures, specially the map, need improvements. Therefore, I recommend major revision and suggest that the authors incorporate the suggestions made throughout the text.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Marcelo Bruno Pessôa

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Submitted filename: PONE-D-21-03456_reviewer.pdf

Click here for additional data file.

6 May 2021

• TITLE

o The title does not reflect the main idea of the research. In my view, the main objective is to test different trap methodologies.

o The functioning and environmental gradient do not add up to the work. The study is only with altitudes gradients.

Response: this is true. We rewrote the title accordingly.

• INTRODUCTION

o I missed in the introduction a better explanation about the methods. Why do you expect that the traps will have different outcomes? How beetle biology may affect these differences?

o The Climate change sentence is lost in the text, and don’t add up to your objective, you can use it in the discussion and conclusion part, but it does not make any difference in the introduction.

o need to make it clearer in the introduction that the goal in the introduction is to compare the methods.

Response:

1. We dedicated one paragraph to method outcomes. Beetle biology indeed impacts this, but as with many tropical regions, including the studied one, their fauna, species and ecologies are extremely poorly understood. Hence we can mostly only speculate about species biology here.

2. We see the reviewer's point here, and moved climate texts to Discussion.

3. We clarified the end of introduction to underscore that the aim is to compare methods.

• METHODS

o Could you show the species accumulation curve as supplemental information, with the hole composition as well.

Response:

1. We added rarefaction curves for each trap type and pooled sample (new Fig. 2). This shows that only pitfall trap catches saturated, whereas light and Malaise traps continued to trap new species, which was reflected in the total sample as well. We omitted the initial accumulation analysis as it contained a calculus error.

2. We also omitted the PERMANOVA approach as it was done with the pooled sample and intended to compare elevations only, whereas our aim was, following the reviewers’ advice, to compare methods (and elevation is a "side product"). Additionally, this analysis did not produce anything new as compared to the other analyses: elevations are compared in much more detailed ways using GLMM, NMDS, MRT and IndVal. So we prefer to leave out also this whole-data curve.

o What is the letters A, B, C, D, E, F, G, H? => If they are Stands you have to explain better.

Response: Thanks for pointing out this lack of clarity. These are indeed study stands, as we now say in the figure text (stands denoted with letters A-J), and cite Table 1 where these are shown.

o Get the GLMM model more explicit.

Response: The full GLMM output is shown in the Supplementary materials. We believe that an average reader will appreciate a simple, at-glance presentation of initially quite diverse analysis outputs. However we are ready to present the big GLMM tables in the manuscript (and not in Supplementary) if the reviewer and editors require that.

o Figure 2 has some problems. Standardize the axes so you can better compare the results. It would be good to add a table with richness values by the mountain, altitude, and trap.

Response:

1. While axis standardization is generally a valid point, in our case it would lead to many of the pitfall-trap columns to become too short to be distinguishable. Therefore, we did not change the figure but added a note to figure legend that the X axis scales are different.

2. The suggested table would be our initial data set, which we will provide in the journal's open access database if our manuscript gets accepted.

o Figure 3 identifies the species, by name in the legends.

Response: we assume that the reviewer would like us to show the species identities for each column. We added an Appendix table that shows these species in rank order per method, with shared species highlighted.

• RESULTS

o NMDS- The 1800 altitude separation becomes evident only in the permanova.

Response: it is indeed common that differences are not that clear in a 2-dimensional graph, whereas permanova considers also other dimensions in the data. The separation is very clear in Table 3, and particularly regarding light traps, the result is extremely clear even without these, as the 1800-m and the other scores do not overlap (Fig. 4). We believe that, after rearranging (and partly rewriting) the methods and statistical outputs, the impact of elevation is now considerably more clearly demonstrated.

• DISCUSSION

o Discuss more the implications of the differences of the methods.

Response: We added notes on practical aspects of using these methods to Discussion, and organized the text better to highlight the use. However we need to underscore that quite little is known about the captured species, which limits conservation implications. We highlight this too in Discussion.

o What is the bias towards decision-making using only a method and possible vantages to use more than one method?

Response: this is an important point. We clarify the end of Discussion as follows: "This is an important aspect as land-use or political decision making should ideally be based on as holistic picture as possible about biodiversity to avoid species losses or unwanted changes in ecosystem functioning."

o If you are talking about trap efficiency, since pitfall with a lesser richness capture differences in all elevations, and combinations I would assume this is the most efficient method (if you emphasize more the different biologies related to each trap you can sell better the idea of complementary survey methods).

Response: this may indeed be the case, and/or many species captured using pitfall traps may be too generalistic in their life histories that they may not be that relevant for conservation, after all, which would make pitfall traps less optimal for studying elevational or climate gradients. But of course, to really determine conservation relevance would require knowledge about species identities, and population sizes, locations and long-term trends. But the main reason for recommending the use of multiple methods is based on complementarity, as explained above.

Few sentences on species ecology has been added with the details of generalists and specialist species.

o Line 263: wind direction is duplicated.

Response: thanks for pointing this out. The other was deleted.

• CONCLUSION

o The recommendation part needs to be better, talk about the biology of beetles and how this would affect the methodology choice of your research. Remember that methodology should adequate to the question made.

Response: this is a valid criticism. We added discussion about links between species life histories and collecting methods (see Conclusions in the end of Discussion).

o Improve the text so it is not a resume of the paper, you can make a more thorough recommendation, a guide for decision making. As it is now the recommendation is too subtle.

Response: this is valid criticism. We tried to improve this with a stronger statement. See our proposed addition to your comment regarding decision making.

• FIGURES

o Figura 1 – Put the names of the mountains in the first image. Identify the images A, B e C.

Done accordingly and new figure has been added.

o Figure 4: Use different colors for elevation and different symbols for mountains.

Response: we prefer not to add more information to this already quite complex graph, plus mountains were not really of interest as our focus was on methods and how they reflect altitudes.

• TABLES

o Why only 500m in this table? Explain this better in the methods.

This shows a distinction between 500 m and the other Altitudes. The other altitudes are compared to the rest below the 500 row. We clarify this in Methods.

• GENERAL

o Text organization of methods and results is really good and with easy understanding.

Response: thank you for this comment.

Reviewer #2

The paper titled “Community composition and indicator functioning of beetles across environmental gradients in Peninsular Malaysia” has important contributions for studies on environmental gradients. The comparison of methods is interesting and this is the main objective of the work. The authors did a huge sampling effort and made a good analysis proposal. However, the paper has some issues and some improvements are needed. Most issues are related to the organization of ideas. Also, the paper has some writing issues. The English is good, but they need to improve the writing. I made several comments along the text trying to highlight some critical points, but I recommend a full text revision. Two specific things that were hard to understand were the sampling design and the analysis descriptions, for which they need to provide more information. Moreover, they need to improve the model diagnostics. I made some suggestions in the Material and Methods. The figures, specially the map, need improvements. Therefore, I recommend major revision and suggest that the authors incorporate the suggestions made throughout the text.

Response: thank you for these comments, which are all important. We attempted to clarify the text and reorganize the structure according to both referees’ comments (see also responses to referee 1). We went through all comments placed in the manuscript pdf, thank you for such thorough job. Specifically, we made the following major changes (linguistic or clarity-related suggestions were mostly accepted as such).

Abstract

The indicator hypothesis was removed while strengthening the focus on methods (see reviewer #1). We now use altitude, not elevation, throughout the text, and name the compared traps in Abstract. PERMANOVA was removed (see reviewer #1). From results in Abstract, we removed analysis names and clarified the tested characteristics of beetle assemblages.

Introduction

Thank you for pointing out Rahbek et al. 2019, which we now cite in Introduction.

We rewrote the global hotspot sentence as suggested.

Request for clarifying altitude and climate: this part was moved to Discussion, where we rewrote it to suggest that changes in temperature, etc. with altitude might have some similar effects as global climate change, giving a reason to study mountain slopes.

The messy paragraph 2: we agree in that we had packed too much scattered information into this. We therefore rewrote the paragraph to be better linked with our aim, which is methodological comparison, whereas altitude is a side effect.

We kept the word "largely" as the determination concerns most but not all responses. However, we went through the whole text to omit similar, unwarranted words. We too think they should be avoided.

The "three mentioned trap types": we now mention only the three studied trap types in the previous paragraph. For some reason the initial text included flight intercept traps, which is now deleted.

We moved the sentences that the reviewer indicated to appropriate sections.

Our intention is not to assess sampling efficiency but rather the consistency of methods in reflecting altitude. Hence, we did not change the initial sentence.

With these edits we believe that the text logic is now much clearer.

Methods

It is an excellent idea to reorganize this section according to study questions. So we rearranged Methods, Results and Discussion. Also, as we noted that the analyses actually describe four (instead of three) viewpoints, we adjusted the research questions accordingly.

We adopted all linguistic suggestions by the reviewer.

Regarding criticism on the trap section containing messy details, we reorganized the presentation as follows: (1) general design (mountains, altitudes), (2) trap types and their numbers, (3) how traps were placed per altitude, and (4) what were the trapping periods. We clarified the trapping-period length (one day per month), samples from different periods were pooled, and that each trap, at least 150 m from the nearest other trap, is considered a replicate in analysis. We also clarified that we had two mountains, each with four altitudes, and each such combination had 2 light, 2 Malaise and 5 (groups of 5) pitfall traps, making up initially 72 samples.

We restructured and clarified the trapping protocol to follow the reviewer's advice.

We ran all statistical analyses using 69 samples without any exceptions, as we say in the beginning of statistics descriptions. We also refer to Table 1 where these samples are linked with the ten stands shown in Fig. 1. Rarefaction is a standard method nowadays, and was calculated for each of the 69 samples (note: no exceptions).

GLMM: we see the reviewer's point in adding more explanatory variables. It is a common problem in mountain research that many things change in concert. Regrettably we do not have additional data, as no forest inventory, for example, was done. It is also worth noting that more complex models result in poorer generalizations. Nevertheless, we clearly acknowledge this shortcoming and propose alternative or, rather, complementary explanations in Discussion.

Thank you for suggesting an alternative GLMM tool. However, our approach appeared robust with alternative approaches, and we also used a well-established assessment of data normality and model robustness, so we did not change this analysis.

Turnover: The important thing is that light and Malaise traps shared about half of their abundant species, whereas pitfall traps produced rather unique data. It also captures clear changes with elevation, and is easy to understand. However, statistical power for this aspect comes from permanova for methods and elevations. We reorganized the text to better underline these results, and link them clearer to the turnover analysis.

Results:

The initial accumulation calculi had an error, plus our intention is really to compare trap types, so we recalculated accumulation curves for each trap type for each of the two mountains. These are now shown as a figure, Appendix 1.

Assemblage structure and turnover: we have now clarified and restructured Methods and Results as there was apparently some lack of clarity. The statements regarding similarity are partly based on the rank-abundance plots, but partly also on permutational multivariate ANOVA (see rewritten Results and Table 3).

Discussion:

Abiotic versus biotic factors affecting species: we do not have data for these; therefore we say "might". At a general level, of ecology, biotic and abiotic factors impact species.

The referee has often commented our speculations and suggested us to provide data. We would be happy to do so, had we some. So neither do we have such data nor large libraries on the ecology of the sampled species, which we highlight in Discussion. For these reasons we express us cautiously. But we believe that even speculative sections may function as seeds for future research.

We did not use the general forest type in analyses because technically it would be the same thing as using altitude as a factor. But of course, as we say clearly in Discussion and in our above comments, altitude probably reflects many things and not just height from sea level; one is habitat type for sure, others are temperature, windiness, etc.

We now refer to Table 4 to show which species indicated what, according to the referee's suggestion.

We moved the statement about altitudinal variation related to mountain and method to the beginning of the paragraph. Thanks for pointing out one of the key findings!

We see the point that our discussion regarding taxonomic level may sound repeating. However, we need to make a point here: specimen of genus A at two different locations may in reality be two different species, whereas if we consider them to be the same (as technically we do so in our analysis), we will probably miss some true location effects.

5 Aug 2021

PONE-D-21-03456R1

The functioning of different beetle (Coleoptera) sampling methods across altitudinal gradients in Peninsular Malaysia

PLOS ONE

Dear Dr. Musthafa,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by November 5th, 2021 If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see:  http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols . Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at  https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols .

We look forward to receiving your revised manuscript.

Kind regards,

Daniel de Paiva Silva, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

Additional Editor Comments (if provided):

Dear Musthafa et al,

After the second round of reviews, I need to say that I agree with reviewer #2, who indicated that several improvements are still necessary to make the study acceptable for publication.

Considering the extent of improvements that are needed to be done, I will grant you with a three-months period to resubmit. Please prepare a point-by-point rebuttal letter, explaining all the improvements that were done, and all of those which you did not agreed with. Please resubmit by November 5th, 2021. In case you need more time, please let me know. Nonetheless, do not hesitate to resubmit earlier if you are able to.

Sincerely,

Daniel Silva, Ph.D.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

Reviewer #3: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Partly

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: No

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: No

Reviewer #3: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: Dear Authors,

I appreciate the opportunity to review your paper again. I really like of your manuscript. Your main find is a great contribution for future environment studies. However, I did not see many of the previous suggestions in the paper. I said "suggestion", because I believe I am not in position to say what you have to do. I can just point out the weaknesses of your manuscript and suggest solutions. If you have a better solution for the problem, this is up to you to do. Thus, there are many weaknesses points in your manuscript that I saw again in this version, mainly related to:

- Map - see the comments in the text;

- Analysis - You must appoint how did you diagnostic the model? How did you find the best distribution? How did you support the normality of your data? At least, you must diagnose the normality of residuals and homogeneity of variance. I do not recommend transform your data. You must try to fit your data in some distribution (there are many);

The information of your manuscript are confuse. You must find a better way to organize and describe your MM and results. I did many suggestions about this in the previous review. I saw many changes in your discussion, but it seems very speculative (see the comments in the text). I suggest a double-check before to submit the manuscript again, there are many problems of Supplementary, Tables, Figures, commas and some sentences are without context.

Best regards!

Reviewer #3: This article is a welcome contribution to improve the efficiency of sampling methods in tropical environments. The positive outcome of the study, but also its limitations, are properly assessed.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #3: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Submitted filename: PONE-D-21-03456_R1_reviewed.pdf

Click here for additional data file.

28 Oct 2021

• If you want to keep this format to describe your results, I recommend rewrite something like this: "Our finds were..."

This sentence had been rewritten as suggested

• …. only light traps...

Corrected as suggested

• It seems a little vague, please be more specific describing your results in the abstract. It is your time to show a compilation of your paper. Which is considerable? Which is less so?

This sentence is sharpened as follows: “Only light traps (but not the other trap types) distinguished high from middle or

low altitudes in terms of beetle richness and assemblage composition.”

• Please, rewrite! It seems little confuse this sentence as well as I specified in the first review:

Community composition – species, their relative shares, and interactions (biotic responses) –largely determines the general biodiversity response to environmental variation. The Climate change sentence is lost in the text, and don’t add up to your objective, you can use it in the discussion and conclusion part, but it does not make any difference in the introduction.

This sentence was indeed unclearly written so we rewrote it as follows: “Community composition is largely determined by

the relative abundances of, and interactions among, its species, which contribute to the general biodiversity response to

environmental variation.” We also omitted most of the climate notes from the manuscript as the reviewer is right in that

we did not directly assess climatic issues.

• Please, rewrite this sentence.

I suggest: "The objective of the present study is evalute the consistency of light, Malaise and pitfall trap samples in

relation to the altitudinal gradient".

Following the reviewers advise, we modified the sentence as: “The objective of the present study is to evaluate the

consistency of light, Malaise and pitfall trap samples in reflecting an altitudinal gradient in tropical Malaysia.”

• Exclude this comma: We removed the extra comma.

• Please, see the comment in the map:

Please, try to improve this map. Your map has several problems. I can't see which group of points represent each altitude

(maybe is the map resolution, but it is better represent this with some color in the legend). Some points are overlap

(there are many function in QGIS that you can correct this problem - for example see in points

proprieties/Labels/Callouts). You need to use a better layer or some shapefile, add maps coordinates, add scale, add the

north arrow and structure better the overview. The previous map was better than this map.

• I still don't understand how did you arrange 25 pitfall traps in four altitudes. The map show to me only 20 pitfall traps.

Please, check it.

Thank you for pointing out an error in the text! We had 40 pitfall traps so the text has been clarified as follows: “We

sampled beetles using two Malaise traps, two light traps, and 10 (5 groups of 2) pitfall traps at each altitude (500 m,

1,000 m, 1,500 m, and 1,800 m a.s.l.) at both mountain slopes.”

• Why 40 pitfall traps if you describe above 25 pitfall traps in each mountain? Please, check it:

See previous point: we had 40 traps (5 sets of 2 locations at 4 elevations)

• I'm still not convinced with your analysis. You assumed Gaussian distribution, because you evaluated your data using only

Q-Q plots and transform your data. I don't recommend transform your data. It is better to find a better distribution that

fits your data. You should use some analysis which you can test the normality such as shapiro test. Quasi-poisson is not a

distribution. You use Quasi-poisson if you found overdispersion in Poisson model. Did you evaluate your model with

Poisson? There is overdispersion?

Did you perform some diagnostics after to run your model? Did you test the normality of residuals and homogeneity of

variance? Which R package did you perform your analysis?

Please, rewrite your data analysis and check all these things.

We followed the reviewer's advice by adding assessments of GLMM residual normality and heteroskedasticity. Moreover,

as these and the QQ plots sometimes suggested slight departures from normality, we reran the models using a novel

“robust LMM” approach, which is tailored for data with outliers while being technically quite similar to lme4. As these

reruns produced similar results to our initial GLMM, we conclude that the initial GLMM produced reliable results, so we

therefore maintain the initial GLMM results in the main document (but present the robust LMM results in the

Supplementary materials). We think it is also important to realize that the QQ plots in Supplementary files measure

residual normality, which is a commonly used way to assess the appropriateness of GLMM, and that none of the presented

analyses are or were done with transformed data (we used transformed data in some trials in the supplementary files of

the previous manuscript version, which are now not presented or referred to). We were apparently not very clear with

these things earlier on. We thus removed the various GLMM trials from the previous version of Supplementary files and

now show the normality, heteroskedasticity and robust LMM results (for comparisons with the initial GLMM) in the

Supplementary files.

The rest of the information can be found in the manuscript.

• How did you analyze the species accumulation curves? Which package? Please, describe this in MM:

We restructured Material and Methods to clarify these. (They were initially in the end of Material and Methods.) We now

mention each package in text sections where the analysis in question is described.

• I didn't see any significant improvement in relation to the previous version. Please, detail more your finds.

We have now restructured and rewritten sections in Results to improve the text flow. We are unsure what the reviewer

means with detailing findings, but hope that the rewritten parts better fulfil this request too.

• I didn't find this Fig: This figure is referred to as Supplementary Table S6, as it is really a table not figure. Apologies for

the lack of clarity here!

• This seems very speculative. Please, remove: Removed as suggested

• Again, this seems very speculative. It is obvious that there are climact changes along the altitudinal gradient, but you

don't have this data. You don't test this data with climact factors. Many of your explanations are related with climatic

factors. So, why you didn't test the climatic factors? I suggest do less emphasis to climact factors, trying to explain your

data and focus in your finds:

We agree with the reviewer in that we did not really deal with climate, but many things that change in concert with

altitude. This part too was removed as suggested.

• So, you can't discuss a half of what you discussed in the previous topic: We agree with the reviewer in that we did not

really deal with climate, but many things that change in concert with altitude. This part too was removed as suggested.

• The reader will judge this. Please, remove: We see the point here and removed the section as suggested

• Whenever possible, we recommend the use of multiple collecting methods in environmental impact assessments on

biodiversity: We rewrote the recommendation as suggested by the reviewer.

• Figure 1 – Put the names of the mountains in the first image. Identify the images A, B e C: Figure 1 has been re-drawn with all the details.

Submitted filename: Response to Reviewers PLOS ONE.docx

Click here for additional data file.

27 Dec 2021

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Daniel de Paiva Silva, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (if provided):

Dear Dr. Musthafa,

After a new review round, both reviewers raised a series of issues that need to be taken care of before publication. Considering the amount of changes you need to prepare, I will provide you a major review to be submitted up to Feb 10, 2022. When you prepare the the new version of your study, please do not forget to prepare a rebuttal letter, informing of all the improvements you did to your study. Please do not hesitate to contact me in case you need more time to review your MS, still, if you are able to resubmit earlier, please do.

Sincerely and Happy 2022,

Daniel Silva, PhD

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #2: (No Response)

Reviewer #4: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #2: Yes

Reviewer #4: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #2: Yes

Reviewer #4: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #2: No

Reviewer #4: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #2: Yes

Reviewer #4: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #2: Dear authors, I appreciated to see many improvements of your manuscript. Only for a few things, but serious things, I cannot make the manuscript acceptable for publication. I did some comments along the attached review, but I will make some main stands here.

1 - The Figure 1 still not good. I saw some differences from previous version, but I still cannot see the sampling points and the localities. I managed to open the sampling points using the S5 file in QGIS and it is possible to make a better map.

2 - I did not find Figures 3, 4, 5 and 6 in this version of the manuscript.

3 - In the previous review I asked to avoid talk about climate change in the discussion. This subject in your manuscript it seems speculative. You can talk about conservation issues and in the final sentence, use as example land use and global climate changes. Remember, your manuscript tested the differences among sample methods along a altitude gradient and did not test any hypothesis about climate change.

Best Wishes!

Reviewer #4: PONE-D-21-03456R2: The functioning of different beetle (Coleoptera) sampling methods across altitudinal gradients in Peninsular Malaysia

This study analyses the sampling potential of three different but a priori complementary types of sampling methods for insects, specifically the study focuses on beetles.

The authors have substantially improved the manuscript since its first version, however it still can be improved in some aspects. The conclusions obtained, from my point of view, are very obvious and not very novel. However, I highlight the importance of the work as it was carried out in a geographic area with few studies on Coleoptera biodiversity in altitudinal gradients. One of my most important criticisms is the lack of an analysis of the efficiency of the sampling by the sample coverage estimator (Chao & Jost 2012: Ecology 93(12):2533–2547) that would have allowed first to check the quality of the samplings and secondly a comparison of the richness of species between types of sampling methods much more valid. These analyses could be complementary to the GLMM since the corrected Species Richness values would be used in the latter (if this were the case).

I also consider it important to add information on the temperature and humidity gradient (at least) in the Material and methods section since part of the results and the discussion focus on the importance of the environmental gradient in a climate change scenario.

Lastly, I have observed some minor mistakes that need to be considered:

Line 31: “altitude”

Line 71: Delete “.”

Line 109: Include the colour of the pitfall traps.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #2: No

Reviewer #4: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Submitted filename: PONE-D-21-03456_R2_reviewer.pdf

Click here for additional data file.

16 Feb 2022

Editors of PLOS ONE

Dear Sir/Madam,

We thank you for a chance for letting us to improve our manuscript “The functioning of different beetle (Coleoptera) sampling methods across altitudinal gradients in Peninsular Malaysia” and submit a revised version to be considered for publication.

We have now made the following changes according to the reviewers’ advice. If no changes were made, we justify this decision below. We hope that the manuscript now satisfies the PLOS ONE editors.

On behalf of all authors,

Muneeb M. Musthafa

Reviewer 2:

1. The Figure 1 still not good.

We have now used a freely-available terrain map as a basis for this figure, and added scale bars and compass arrows. On these maps we have plotted the locations of the ten sites per mountain (referred to as letters A-J). Additions of precise locations of every trap would have made the map unreadable (realistically, this would be a maximum of 10-15 cm wide in a journal page); however, should anyone be interested in these, coordinates are given in Supplementary materials.

2. I did not find Figures 3, 4, 5 and 6 in this version of the manuscript.

Apologies for this, we hope this was not an error of ours. We nevertheless pay attention in including all figures (1-6), tables and supplementary materials. The data are added as another supplementary file in text format.

3. In the previous review I asked to avoid talk about climate change in the discussion. This subject in your manuscript it seems speculative. You can talk about conservation issues and in the final sentence, use as example land use and global climate changes. Remember, your manuscript tested the differences among sample methods along a altitude gradient and did not test any hypothesis about climate change.

We agree with this statement and have omitted climate discussions, except for one point in Discussion where additional follow-ups are recommended (for climate reasons).

Reviewer 4:

1. I highlight the importance of the work as it was carried out in a geographic area with few studies on Coleoptera biodiversity in altitudinal gradients.

We agree with this note and highlighted this on lines 53-54.

2. One of my most important criticisms is the lack of an analysis of the efficiency of the sampling by the sample coverage estimator (Chao & Jost 2012: Ecology 93(12):2533–2547) that would have allowed first to check the quality of the samplings and secondly a comparison of the richness of species between types of sampling methods much more valid. These analyses could be complementary to the GLMM since the corrected Species Richness values would be used in the latter (if this were the case).

Thank you for this note, and pointing out this richness approach. We applied the Chao & Jost paper, used iNEXT package to calculate the coverage estimator asymptotes for each sample, and subjected these to the same GLMM approaches as for the rest of the richness estimates. The model performances are added to respective Tables and supplementary materials. Regrettably the pitfall data performed poorly due to a few apparent outliers – these could have been collected on richer than average soils, but we do not have data on this – and the robust LMM did not shed much light on this, except for confirming that pitfall data indeed largely reflected differences between mountains and altitudinal effect was much smaller.

3. I also consider it important to add information on the temperature and humidity gradient (at least) in the Material and methods section since part of the results and the discussion focus on the importance of the environmental gradient in a climate change scenario.

While we agree in that these measures are of importance or interest, the author MMM is currently preparing another manuscript on these variables. Therefore, we would not like to include these here. However, we refer to the general patterns of temperature, humidity and luminosity according to altitude (lines 87-90). Also, as the altitudinal effects generally reflected this to be quite small, we point out these generalities once again in Discussion (line 317).

4. Lastly, I have observed some minor mistakes that need to be considered

Thank you for pointing these out – these are all corrected now.

Submitted filename: Musthafa et al PLOS ONE R3 responses for reviewers.docx

Click here for additional data file.

15 Mar 2022

The functioning of different beetle (Coleoptera) sampling methods across altitudinal gradients in Peninsular Malaysia

PONE-D-21-03456R3

Dear Dr. Musthafa,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Stephanie S. Romanach, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments:

In your Acknowledgements you thank "an anonymous reviewer". For your records, PLOS has provided reviews from four peer reviewers on the first three versions (original, R1, R2) of your manuscript.

21 Mar 2022

PONE-D-21-03456R3

The functioning of different beetle (Coleoptera) sampling methods across altitudinal gradients in Peninsular Malaysia

Dear Dr. Musthafa:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Stephanie S. Romanach

Academic Editor

PLOS ONE