| Literature DB >> 34306629 |
Jianchao Liang1,2, Huijian Hu2, Zhifeng Ding2, Ganwen Lie3, Zhixin Zhou2, Paras Bikram Singh2,4, Zhixiang Zhang1,5, Shengnan Ji6.
Abstract
A fundamental yet controversial topic in biogeography is how and why species range sizes vary along spatial gradients. To advance our understanding of these questions and to provide insights into biological conservation, we assessed elevational variations in the range sizes of vascular plants with different life forms and biogeographical affinities and explored the main drivers underlying these variations in the longest valley in China's Himalayas, the Gyirong Valley. Elevational range sizes of vascular plants were documented in 96 sampling plots along an elevational gradient ranging from 1,800 to 5,400 m above sea level. We assessed the elevational variations in range size by averaging the range sizes of all recorded species within each sampling plot. We then related the range size to climate, disturbance, and the mid-domain effect and explored the relative importance of these factors in explaining the range size variations using the Random Forest model. A total of 545 vascular plants were recorded in the sampling plots along the elevational gradient. Of these, 158, 387, 337, and 112 were woody, herbaceous, temperate, and tropical species, respectively. The range size of each group of vascular plants exhibited uniform increasing trends along the elevational gradient, which was consistent with the prediction of Rapoport's rule. Climate was the main driver of the increasing trends of vascular plant range sizes in the Gyirong Valley. The climate variability hypothesis and mean climate condition hypothesis could both explain the elevation-range size relationships. Our results reinforce the previous notion that Rapoport's rule applies to regions where the influence of climate is the most pronounced, and call for close attention to the impact of climate change to prevent species range contraction and even extinction due to global warming.Entities:
Keywords: Himalayas; Rapoport's rule; climate variability; elevational gradient; species range size; vascular plants
Year: 2021 PMID: 34306629 PMCID: PMC8293715 DOI: 10.1002/ece3.7744
Source DB: PubMed Journal: Ecol Evol ISSN: 2045-7758 Impact factor: 2.912
FIGURE 1Map of the study area showing the locations of the 96 sampling plots and 6 mini weather stations along the Gyirong Valley. The letters correspond to the vegetation zones shown in the lower left corner of the map: (a) evergreen broadleaf forest; (b) subalpine coniferous forest; (c) alpine bush and coryphilum; (d) alpine tundra with sparse herbs
FIGURE 2Elevational variations in (a) MAT, mean annual temperature; (b) MAP, mean annual precipitation; (c) MATR, mean annual temperature range; (d) TS, temperature seasonality; (e) TC, temperature change between the present and the Last Glacial Maximum; (f) PC, precipitation change between the present and the Last Glacial Maximum; (g) MDE, mid‐domain effect; (h) POP, human population
FIGURE 3Elevational trends in the range size of (a) overall species, (b) woody species, (c) herbaceous species, (d) temperate species, and (e) tropical species in the Gyirong Valley
The ordinary least squares (OLS) models for each environmental variable and species range size of all groups of vascular plants
| Overall species | Woody species | Herbaceous species | Temperate species | Tropical species | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Coef |
| Adj | Coef |
| Adj | Coef |
| Adj | Coef |
| Adj | Coef |
| Adj | |
| MAT | −0.734 | 0.079 | 0.601 | −0.724 | 0.089 | 0.605 | −0.705 | 0.089 | 0.584 | −0.675 | 0.096 | 0.559 | −0.665 | 0.099 | 0.589 |
| MAP | −0.720 | 0.080 | 0.585 | −0.719 | 0.091 | 0.591 | −0.681 | 0.087 | 0.569 | −0.670 | 0.097 | 0.551 | −0.673 | 0.098 | 0.591 |
| MATR | 0.770 | 0.078 | 0.619 | 0.696 | 0.090 | 0.576 | 0.733 | 0.091 | 0.598 | 0.702 | 0.096 | 0.579 | 0.682 | 0.101 | 0.570 |
| TS | 0.789 | 0.076 | 0.630 | 0.700 | 0.089 | 0.574 | 0.767 | 0.091 | 0.617 | 0.686 | 0.095 | 0.562 | 0.688 | 0.100 | 0.576 |
| TC | −0.539 | 0.085 | 0.482 | −0.489 | 0.090 | 0.437 | −0.513 | 0.094 | 0.463 | −0.465 | 0.099 | 0.418 | −0.485 | 0.134 | 0.436 |
| PC | −0.487 | 0.090 | 0.430 | −0.557 | 0.095 | 0.490 | −0.565 | 0.098 | 0.498 | −0.501 | 0.101 | 0.452 | −0.491 | 0.126 | 0.442 |
| MDE | 0.376 | 0.105 | 0.371 | 0.384 | 0.093 | 0.388 | 0.375 | 0.094 | 0.366 | 0.285 | 0.102 | 0.301 | 0.288 | 0.102 | 0.314 |
| POP | 0.298 | 0.085 | 0.320 | 0.284 | 0.093 | 0.309 | 0.293 | 0.095 | 0.324 | 0.334 | 0.095 | 0.341 | 0.303 | 0.099 | 0.317 |
The strength of correlation was measured by regression coefficient (Coef).
Abbreviations: Adj R 2, adjusted R 2; MAP, mean annual precipitation; MAT, mean annual temperature; MATR, mean annual temperature range; MDE, mid‐domain effect; PC, change in mean annual precipitation between the present and the Last Glacial Maximum; POP, human population; SE, standard error; TC, change in mean annual temperature between the present and the Last Glacial Maximum; TS, temperature seasonality.
p < .05
p < .01
p < .001.
The simultaneous autoregressive (SAR) models for each environmental variable and species range size of all groups of vascular plants
| Overall species | Woody species | Herbaceous species | Temperate species | Tropical species | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Coef |
| AIC | Coef |
| AIC | Coef |
| AIC | Coef |
| AIC | Coef |
| AIC | |
| MAT | −0.627 | 0.091 | 202.20 | −0.613 | 0.102 | 204.79 | −0.597 | 0.103 | 222.51 | −0.552 | 0.121 | 252.91 | −0.558 | 0.134 | 230.42 |
| MAP | −0.604 | 0.088 | 202.18 | −0.609 | 0.101 | 205.70 | −0.584 | 0.101 | 221.92 | −0.569 | 0.115 | 254.45 | −0.562 | 0.128 | 227.17 |
| MATR | 0.662 | 0.093 | 203.46 | 0.583 | 0.104 | 204.24 | 0.629 | 0.105 | 223.13 | 0.598 | 0.123 | 252.88 | 0.578 | 0.102 | 222.42 |
| TS | 0.686 | 0.094 | 202.06 | 0.596 | 0.105 | 202.89 | 0.659 | 0.107 | 222.36 | 0.578 | 0.124 | 251.83 | 0.587 | 0.103 | 221.68 |
| TC | −0.476 | 0.117 | 223.19 | −0.446 | 0.115 | 222.16 | −0.457 | 0.127 | 243.45 | −0.420 | 0.152 | 260.33 | −0.440 | 0.114 | 206.11 |
| PC | −0.439 | 0.144 | 235.65 | −0.509 | 0.154 | 216.9 | −0.512 | 0.150 | 234.00 | −0.455 | 0.115 | 263.05 | −0.408 | 0.083 | 217.76 |
| MDE | 0.339 | 0.185 | 251.72 | 0.334 | 0.195 | 240.79 | 0.322 | 0.093 | 249.54 | 0.230 | 0.162 | 282.64 | 0.213 | 0.116 | 287.36 |
| POP | 0.293 | 0.089 | 252.56 | 0.286 | 0.112 | 243.25 | 0.279 | 0.098 | 257.70 | 0.324 | 0.113 | 251.39 | 0.296 | 0.134 | 250.13 |
The strength of correlation was measured by regression coefficient (Coef).
Abbreviations: AIC, Akaike information criterion; MAP, mean annual precipitation; MAT, mean annual temperature; MATR, mean annual temperature range; MDE, mid‐domain effect; PC, change in mean annual precipitation between the present and the Last Glacial Maximum; POP, human population; SE, standard error; TC, change in mean annual temperature between the present and the Last Glacial Maximum; TS, temperature seasonality.
p < .05
p < .01
p < .001.
FIGURE 4The average percentage increase in mean squared error of each environmental variable in 1,000 Random Forest models for (a) overall species, (b) woody species, (c) herbaceous species, (d) temperate species, and (e) tropical species