Research Article |
Corresponding author: Hong Qu ( yw229628927@163.com ) Academic editor: Yu-Pin Lin
© 2018 Hong Qu, Chun-Jing Wang, Zhi-Xiang Zhang.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Hong Qu H, Wang C-J, Zhang Z-X (2018) Planning priority conservation areas under climate change for six plant species with extremely small populations in China. Nature Conservation 25: 89-106. https://doi.org/10.3897/natureconservation.25.20063
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The concept of Plant Species with Extremely Small Populations (PSESP) has been employed to guide conservation of threatened plant species in China. Climate change has a high potential to threaten PSESP. As a result, it is necessary to integrate climate change effects on PSESP into conservation planning in China. Here, ecological niche modelling is used to project current and future habitat distributions of six PSESP in China under climate change scenarios and conservation planning software is applied to identify priority conservation areas (PCAs) for these PSESP based on habitat distributions. These results were used to provide proposals for in-situ and ex-situ conservation measures directed at PSESP. It was found that annual precipitation was important for habitat distributions for all six PSESP (with the percentage contribution to habitat distributions ranging from 18.1 % to 74.9 %) and non-climatic variables including soil and altitude have a large effect on habitat suitability of PSESP. Large quantities of PCAs occurred within some provincial regions for these six PSESP (e.g. Sichuan and Jilin for the PSESPCathaya argyrophylla, Taxus cuspidata, Annamocarya sinensis and Madhuca pasquieri), indicating that these are likely to be appropriate areas for in-situ and ex-situ conservation measures directed at these PSESP. Those nature reserves with large quantities of PCAs were identified as promising sites for in-situ conservation measures of PSESP; such reserves include Yangzie and Dongdongtinghu for C. argyrophylla, Songhuajiangsanhu and Changbaishan for T. cuspidata and Shiwandashanshuiyuanlian for Tsoongiodendron odorum. These results suggest that existing seed banks and botanical gardens occurring within identified PCAs should allocate more resources and space to ex-situ conservation of PSESP. In addition, there should be additional botanical gardens established for ex-situ conservation of PSESP in PCAs outside existing nature reserves. To address the risk of negative effects of climate change on PSESP, it is necessary to integrate in-situ and ex-situ conservation as well as climate change monitoring in PSESP conservation planning.
PSESP, climatic change, systematic conservation planning, China, in-situ and ex-situ conservation measures
Climate change has a large potential to threaten plant diversity from species to biomes, as well as hinder endangered species protection (
As many of its species are currently threatened or on the brink of extinction, China is one of the highest priorities for biodiversity conservation globally (
Identifying priority conservation areas (PCAs) is a useful step in making climate change adaptation strategies for the conservation of PSESP. Recently, many conservation biologists and ecologists have used ecological niche modelling (ENM) in combination with conservation planning software to identify PCAs for endemic, threatened and endangered plant species under climate change conditions (
PSESP as a designation is not only important for conservation prioritisation in China, but also may be a useful framework in conservation efforts for threatened plants around the world (
The primary objective of this study is to identify PCAs for PSESP in China under climate change conditions. To achieve this objective, six PSESP were selected as study species and ENM used to model the habitat distributions of these PSESP under current and future climate scenarios and the environmental variables that contribute significantly to the habitat distributions of the focal PSESP were explored. Then, conservation planning software was used to identify PCAs for PSESP in China under projected climate change conditions based on the species’ habitat requirements. Finally, the regions were identified with high potential to serve as effective conservation sites for the focal PSESP based on identified PCAs and suggestions were developed for in-situ and ex-situ conservation measures of PSESP.
The State Forestry Administration of China has been concentrating on management of PSESP through its “Conservation Programme for Wild Plants with Extremely Small Populations in China (from 2011 to 2015)” (http://www.forestry.gov.cn/portal/main/s/72/content-540092.html). This plan identifies PSESP as species comprising fewer than 5,000 individuals and restricted to known localities (
Characteristics of the six focal PSESP and Maxent performance test results.
Name | Form | Altitude (m) | Individual | Record | Training AUC | Test AUC | Training Omission | Ecoregion |
---|---|---|---|---|---|---|---|---|
Cathaya argyrophylla | Tree | 900–1900 | 4484 | 10 | 0.983 | 0.980 | 0.00±0.00 | TBMF |
Taxus cuspidata | Tree | 500–1000 | 42700 | 24 | 0.997 | 0.996 | 0.03±0.04 | TBMF |
Annamocarya sinensis | Tree | 500–2500 | 472 | 19 | 0.993 | 0.987 | 0.04±0.02 | TBMF |
Ulmus elongata | Tree | 500–900 | 1430 | 11 | 0.995 | 0.993 | 0.03±0.04 | TSMBF |
Tsoongiodendron odorum | Tree | 500–1000 | 6548 | 49 | 0.989 | 0.985 | 0.04±0.04 | TSMBF |
Madhuca pasquieri | Tree | 0–1100 | 6429 | 23 | 0.992 | 0.990 | 0.05±0.04 | TBMF |
Spatial data were obtained for 14 environmental variables at a 10-arc-min resolution including eight soil, one topographic, one natural state and four climate variables (Suppl. material 1: Table S1;
To model the future habitat distributions of PSESP in the 2080s (i.e. 2070–2099), the average projection maps generated under four global climate models were used (i.e. bcc_csm1_1, csiro_mk3_6_0, gfdl_cm3 and mohc_hadgem2_es) and two greenhouse gas concentration scenarios as representative concentration pathways (RCPs) of 4.5 (mean, 780 ppm; range, 595 to 1005 by 2100) and 8.5 (mean, 1685 ppm; range, 1415 to 1910 by 2100), representing low and high gas concentration scenarios, respectively (http://www.ccafs-climate.org/).
Using Maxent (a commonly-used ENM software) and the 14 environmental variables, the current and future species distributions for the six focal PSESP were modelled with maximum entropy (
For modelling the distributions of PSESP, the Maxent sets were as follows: 1) the regularisation multiplier (beta) was set to two to produce a smooth and general response that could be modelled in a biologically realistic manner (
The analysis produced a receiver operating characteristic (ROC) curve, which established each value of the prediction results as a possible judging threshold; the corresponding sensitivity and specificity of the predicted results were obtained (
The Zonation conservation planning software (http://cbig.it.helsinki.fi/software/) was used to prioritise conservation areas for PSESP under conditions of climate change (
The distributions of each species under current, low and high gas concentration scenarios, as assessed by the Maxent value of each grid cell, were used as input feature maps for the Zonation software (
As limited resources rarely allow all potential habitats to be conserved, the top 10% of grid cells of distributions were extracted (referred to as the grid cells ranking in the top 10% in the following), based on PCAs for each PSESP according to realised ecoregional ranges of species as presented in
First, grid cells of PCAs were downscaled from 10 arc-minutes to 2.5 arc-minutes and the number of grid cells was used to quantify the size of PCAs in order to improve the precision of the assessment (
All ENMs had AUC values greater than 0.7 for both the training and test data sets and the training omission rates were less than 17 %, indicating a high level of accuracy for each model (Table
Percentage contribution of environmental variables to predicted distributions of PSESP.
Name | Alt | Bio1 | Bio4 | Bio12 | Bio15 | BLD | CEC | CLYPPT | CRFVOL | OCSTHA | PHIHOX | SLTPPT | SNDPPT | HF |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Cathaya argyrophylla | 2.36 | 1.5 | 1.06 | 20.88 | 16.39 | 52.72 | 0.26 | 0.23 | 0.13 | 0 | 4.47 | 0 | 0 | 0 |
Taxus cuspidata | 6.41 | 1.84 | 33.6 | 18.12 | 0.13 | 19.91 | 0.63 | 0 | 0.91 | 0.44 | 1.3 | 0.28 | 16.28 | 0.14 |
Annamocarya sinensis | 18.65 | 0.16 | 3.4 | 63 | 0 | 4.31% | 0.3 | 0.57% | 5.13% | 2.16% | 0.6 | 0.72 | 0.92 | 0.09 |
Ulmus elongata | 2.54 | 0 | 1.72 | 22.11 | 21.81 | 23.06 | 22.53 | 1.18 | 0.52 | 0.02 | 4.21 | 0.31 | 0 | 0 |
Tsoongiodendron odorum | 7.58 | 5.13 | 1.39 | 74.91 | 0.28 | 1.08 | 1.17 | 0.15 | 5.91 | 0.01 | 0.33 | 1.67 | 0.16 | 0.23 |
Madhuca pasquieri | 6.91 | 3.83 | 9.72 | 61.74 | 0.01 | 2.32 | 12.62 | 1.01 | 1.46 | 0.07 | 0.02 | 0.25 | 0 | 0.05 |
Province | Cathaya argyrophylla | Taxus cuspidata | Annamocarya sinensis | Ulmus elongata | Tsoongiodendron odorum | Madhuca pasquieri | Total |
---|---|---|---|---|---|---|---|
Anhui | 98999 | 0 | 7122 | 0 | 0 | 4946 | 111067 |
Fujian | 0 | 0 | 0 | 44356 | 9013 | 0 | 53369 |
Gansu | 0 | 0 | 14403 | 0 | 0 | 0 | 14403 |
Guangdong | 0 | 0 | 0 | 7970 | 77130 | 0 | 85100 |
Guangxi | 0 | 0 | 0 | 4303 | 74487 | 0 | 78790 |
Hainan | 0 | 0 | 0 | 0 | 19843 | 0 | 19843 |
Hebei | 0 | 800 | 0 | 0 | 0 | 0 | 800 |
Heilongjiang | 0 | 112413 | 0 | 0 | 0 | 0 | 112413 |
Henan | 72 | 0 | 4828 | 0 | 0 | 0 | 4900 |
Hubei | 60968 | 0 | 37233 | 159 | 0 | 16691 | 115051 |
Hunan | 50584 | 0 | 3484 | 38545 | 0 | 14745 | 107358 |
Inner Mongolia | 0 | 2400 | 0 | 0 | 0 | 0 | 2400 |
Jiangsu | 22032 | 0 | 0 | 0 | 0 | 6798 | 28830 |
Jiangxi | 55232 | 0 | 9043 | 54250 | 839 | 70620 | 189984 |
Jilin | 0 | 168579 | 0 | 0 | 0 | 0 | 168579 |
Liaoning | 0 | 62161 | 0 | 0 | 0 | 0 | 62161 |
Shaanxi | 0 | 0 | 38544 | 0 | 0 | 0 | 38544 |
Shandong | 0 | 0 | 0 | 0 | 0 | 205 | 205 |
Shanxi | 0 | 0 | 215 | 0 | 0 | 0 | 215 |
Sichuan | 7032 | 0 | 189237 | 0 | 0 | 177506 | 373775 |
Taiwan | 0 | 0 | 0 | 7350 | 12078 | 0 | 19428 |
Tibet | 0 | 0 | 36198 | 0 | 919 | 31169 | 68286 |
Yunnan | 0 | 0 | 494 | 0 | 7465 | 494 | 8453 |
Zhejiang | 51113 | 0 | 6197 | 45862 | 0 | 22938 | 126110 |
Out of all provinces, the greatest total area of PCAs for the studied PSESP occurred in Sichuan and Jilin; PCAs in these provinces included those of C. argyrophylla, A. sinensis and M. pasquieri and T. cuspidata (Fig.
The nature reserves with largest capacity to conserve the focal PSESP included Yangzie, Songhuajiangsanhu, Changbaishan, Dongdongtinghu and Shiwandashanshuiyuanlian (Figs
These results indicate that some provincial regions (e.g. Sichuan and Jilin) contain large areas of habitat as identified by the PCAs for the six PSESP. As such, the outputs serve as tools to identify potential areas for the conservation of PSESP. Figure
In an attempt to take into account these future scenarios, current and future suitable distributions were integrated into PCA predictions in order to consider where and how ex-situ conservation could be used in PCAs for PSESP (
It was found that climatic variables and, particularly annual precipitation, were important for distributions of the six focal PSESP in China (Table
To address the negative effects of climate change on PSESP, there is a need to integrate in-situ and ex-situ conservation measures and climate change monitoring into conservation planning for the six focal PSESP. The delineation of PCAs may be used for providing in-situ and ex-situ conservation measures for PSESP populations and habitats. Monitoring of environmental variation is essential for successful in-situ and ex-situ conservation management of PSESP (
We thank for the valuable comments of editor and reviewers for the improvement of the early manuscript. This work has been supported by the project entrusted by China’s State Forestry Administration “Effectiveness assessment of small nature reserves: a case of Lin’an city, Zhejiang Province”.
Table S1, S2; Figure S1, S2
Data type: (measurement/occurence/multimedia/etc.)
Explanation note:
Table S1. Environmental variables used in this study.
Table S2. Potential nature reserves overlapped with priority conservation areas for PSESP.
Figure S1. Distribution probabilities of PSESP under current, low and high greenhouse gas concentration scenarios. The colour from yellow to blue represents increasing distribution probability for PSESP.
Figure S2. Priority conservation rank of PSESP. The colour from yellow to blue represents increasing priority conservation rank for PSESP.