| Literature DB >> 21961041 |
Heidi Paltto1, Anna Nordberg, Björn Nordén, Tord Snäll.
Abstract
Wooded pastures with ancient trees were formerly abundant throughout Europe, but during the last century, grazing has largely been abandoned often resulting in dense forests. Ancient trees constitute habitat for many declining and threatened species, but the effects of secondary woodland on the biodiversity associated with these trees are largely unknown. We tested for difference in species richness, occurrence, and abundance of a set of nationally and regionally red-listed epiphytic lichens between ancient oaks located in secondary woodland and ancient oaks located in open conditions. We refined the test of the effect of secondary woodland by also including other explanatory variables. Species occurrence and abundance were modelled jointly using overdispersed zero-inflated Poisson models. The richness of the red-listed lichens on ancient oaks in secondary woodland was half of that compared with oaks growing in open conditions. The species-level analyses revealed that this was mainly the result of lower occupancy of two of the study species. The tree-level abundance of one species was also lower in secondary woodland. Potential explanations for this pattern are that the study lichens are adapted to desiccating conditions enhancing their population persistence by low competition or that open, windy conditions enhance their colonisation rate. This means that the development of secondary woodland is a threat to red-listed epiphytic lichens. We therefore suggest that woody vegetation is cleared and grazing resumed in abandoned oak pastures. Importantly, this will also benefit the vitality of the oaks.Entities:
Mesh:
Year: 2011 PMID: 21961041 PMCID: PMC3178531 DOI: 10.1371/journal.pone.0024675
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Figure 1The location of the study oaks.
Red-list category, number of trees occupied and abundance of the study lichens on 52 ancient oaks in open conditions or in secondary woodland.
| Number of occupied trees | Abundance (min and max no of grid cells) | ||||
| Species | Red-list category | Secondary woodland | Open conditions | Secondary woodland | Open conditions |
|
| regionally | 10 (48%) | 24 (77%) | 1–53 | 7–111 |
|
| NT | 2 (10%) | 11 (26%) | 15–44 | 1–94 |
|
| NT | 3 (14%) | 8 (26%) | 7–12 | 2–47 |
|
| NT | 0 | 7 (23%) | 1–78 | |
|
| regionally | 3 (14%) | 4 (13%) | 14–17 | 1–55 |
|
| NT | 1 (5%) | 2 (6%) | 1–1 | 2–11 |
|
| VU | 0 | 2 (6%) | 3–5 | |
|
| NT | 0 | 2 (6%) | 3–5 | |
|
| NT | 2 (10%) | 0 | 1–4 | |
|
| VU | 0 | 1 (3%) | 2 | |
*Nationally red-listed according to IUCN (VU = vulnerable, NT = near threatened [18]), or regionally red-listed [29]).
Characteristics of variables included in regression models explaining richness, occurrence and abundance of red-listed lichens on 52 ancient oaks.
| Oaks in secondary woodland (n = 31) | Oaks in open conditions (n = 21) | |||||||
| Median | Average±SD | Min | Max | Median | Average±SD | Min | Max | |
|
| ||||||||
| Canopy cover (%) | 54 | 56±8 | 45 | 75 | 69 | 70±9 | 55 | 86 |
| Bryophyte abundance | 19 | 18±11 | 0 | 50 | 31 | 33±20 | 1 | 71 |
| (% of grid cells) | ||||||||
| Bark pH | 4.6 | 4.7±0.6 | 3.7 | 6.8 | 4.6 | 4.7±0.5 | 3.6 | 5.4 |
| Max bark crevice depth (mm) | 43 | 44±9 | 28 | 70 | 50 | 48±15 | 27 | 85 |
|
| ||||||||
| Oak density ≥100 cm 5 km | 1.5 | 2.4±2.3 | 0.5 | 10.9 | 1.6 | 2.0±1.5 | 0.4 | 6.2 |
| (no. trees/km2) | ||||||||
| Oak density ≥160 cm 0.5 km | 0 | 0.6±0.9 | 0 | 2.5 | 0 | 0.9±2.8 | 0 | 12.7 |
| (no. trees/km2) | ||||||||
| Oak density ≥160 cm 2 km | 0.1 | 0.2±0.4 | 0 | 2.0 | 0.2 | 0.3±0.6 | 0 | 2.8 |
| (no. trees/km2) | ||||||||
Included in the model for Chaenotheca phaeocephala occurrence.
Included in the model for species richness, and in the models for Cliostomum corrugatum, Buellia violaceofusca and Calicium adspersum occurrences.
Included in the model for Ramalina baltica occurrence.
Count regression models explaining species richness (an averaged model based on three plausible models) of red-listed epiphytic lichens on 52 ancient oaks.
| Response variable | Parameter estimatea,b | Lower 95% CI | Upper 95% CI |
| Explanatory variables | |||
|
| |||
| Intercept | 0.0049 | 0.0038 | 0.0064 |
| Bark pH | 1.66 | 1.17 | 2.35 |
| Secondary woodland | 0.47 | 0.23 | 0.95 |
| Max bark crevice depth (mm) | 1.056 | 1.029 | 1.083 |
| Canopy cover (%) | 1.002 | 0.973 | 1.032 |
| Bryophyte abundance (%) | 1.005 | 0.991 | 1.018 |
| Oaks >160 cm in diameter within 0.5 km | 1.015 | 0.955 | 1.079 |
| Max bark crevice depth: Canopy cover | 0.945 | 0.904 | 0.987 |
*The parameter estimates and confidence limits of the models are back-transformed: estimated values express the proportional change in species abundance per unit increase in the explanatory variable. For example. 1.05 and 0.95 express 5% increase and 5% decrease, respectively, in species abundance per unit increase in the explanatory variable.
Zero-inflated count regression models explaining abundance and non-occurrence of red-listed epiphytic lichens on 52 ancient oaks.
| Type of model | Response variableExplanatory variables | Parameter estimate | Lower 95% CI | Upper 95% CI | Test statistica (z-values) | p |
|
| ||||||
| Count | Intercept | 0.105 | 0.080 | 0.138 | −16.21 | <0.001 |
| Secondary woodland | 0.36 | 0.19 | 0.68 | −3.10 | 0.002 | |
| Bryophyte abundance (%) | 0.98 | 0.96 | 1.00 | −2.35 | 0.019 | |
| Theta | 1.79 | 1.04 | 3.08 | 2.11 | 0.035 | |
| Binom | Intercept | −1.65 | −2.77 | −0.53 | −2.88 | 0.004 |
| Max bark crevice depth (mm) | −0.16 | −0.28 | −0.05 | −2.77 | 0.006 | |
| ,Canopy cover | 0.19 | 0.01 | 0.36 | 2.08 | 0.037 | |
| Max bark crevice depth:Canopy cover | 0.023 | 0.004 | 0.042 | 2.37 | 0.018 | |
|
| ||||||
| Count | Intercept | 0.06 | 0.03 | 0.12 | −7.26 | <0.002 |
| Bryophyte abundance (%) | 0.94 | 0.89 | 1.00 | −1.94 | 0.053 | |
| Theta | 1.12 | 0.39 | 3.19 | 0.21 | 0.830 | |
| Binom | Intercept | 1.93 | 0.64 | 3.23 | 2.92 | 0.003 |
| Max bark crevice depth (mm) | −0.15 | −0.27 | −0.03 | −2.39 | 0.017 | |
| Secondary woodland | 3.49 | 0.42 | 6.55 | 2.23 | 0.026 | |
| Bark pH | −1.91 | −3.64 | −0.18 | −2.17 | 0.030 | |
|
| ||||||
| Count | Intercept | 0.05 | 0.03 | 0.08 | −11.14 | <0.002 |
| Canopy cover (%) | 0.95 | 0.92 | 1.00 | −2.17 | 0.030 | |
| Theta | 1.42 | 0.45 | 4.44 | 0.60 | 0.550 | |
| Binom | Intercept | 1.98 | 0.83 | 3.12 | 3.38 | 0.001 |
| Max bark crevice depth (mm) | −0.14 | −0.26 | −0.03 | −2.50 | 0.012 | |
| Bark pH | −1.40 | −2.90 | 0.10 | 1.64 | 0.068 | |
| Secondary woodland | 2.07 | −0.41 | 4.56 | −1.83 | 0.102 | |
|
| ||||||
| Count | Intercept | 0.04 | 0.02 | 0.07 | −11.08 | <0.002 |
| Max bark crevice depth (mm) | 1.06 | 1.01 | 1.11 | 2.37 | 0.018 | |
| Theta | 2.42 | 0.48 | 12.06 | 1.08 | 0.282 | |
| Binom | Intercept | 2.16 | 1.11 | 3.21 | 4.03 | <0.001 |
| Bark pH | 1.85 | −0.09 | 3.8 | 1.87 | 0.062 | |
The probability of non-occurrence. Hence, the interpretation of the signs of the estimates is the opposite of typical binary models.
z-values for non-occurrence models and associated p-values.
Theta is a parameter of the negative binomial variance function [67].