Projected impacts of climate change on protected birds and nature reserves in China

Sci. Bull. DOI 10.1007/s11434-015-0892-y

www.scibull.com www.springer.com/scp

Article

Life & Medical Sciences

Projected impacts of climate change on protected birds and nature reserves in China Xueyan Li • Nicholas Clinton • Yali Si Jishan Liao • Lu Liang • Peng Gong

•

Received: 23 July 2015 / Accepted: 26 August 2015  Science China Press and Springer-Verlag Berlin Heidelberg 2015

Abstract Knowledge about climate change impacts on species distribution at national scale is critical to biodiversity conservation and design of management programs. Although China is a biodiversity hot spot in the world, potential influence of climate change on Chinese protected birds is rarely studied. Here, we assess the impact of climate change on 108 protected bird species and nature reserves using species distribution modeling at a relatively fine spatial resolution (1 km) for the first time. We found that a large proportion of protected species would have potential suitable habitat shrink and northward range shift by 77–90 km in response to projected future climate change in 2080. Southeastern China would suffer from losing climate suitability, whereas the climate conditions in Qinghai–Tibet Plateau and northeastern China were projected to become suitable for more protected species. On Electronic supplementary material The online version of this article (doi:10.1007/s11434-015-0892-y) contains supplementary material, which is available to authorized users. X. Li  N. Clinton  Y. Si  L. Liang  P. Gong Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China X. Li College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China Y. Si  P. Gong Joint Center for Global Change Studies, Beijing 100875, China J. Liao  P. Gong (&) State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences, and Beijing Normal University, Beijing 100101, China e-mail: [email protected]

average, each protected area in China would experience a decline of suitable climate for 3–4 species by 2080. Climate change will modify which species each protected area will be suitable for. Our results showed that the risk of extinction for Chinese protected birds would be high, even in the moderate climate change scenario. These findings indicate that the management and design of nature reserves in China must take climate change into consideration. Keywords Climate change impact  Protected bird species  Habitat suitability  Range shift  Species distribution model

1 Introduction The last decades have witnessed great changes in global climate, with the averaged warming rate of the last 50 years is nearly twice for the last 100 years [1, 2]. Climate change has significant impact on community composition [3, 4], phenological patterns [5], and ecosystem structure [6] of terrestrial and marine ecosystem [7, 8]. Recent studies demonstrated not only latitudinal [9, 10] but also elevational [11, 12] species range shifts in different regions caused by climate change. These projected range shifts would also threaten the effectiveness of nature reserves in the future [13–16]. The fate of protected species with relative small range and narrow niches under climate change has caused special concern [17–19]. It is generally recognized that protected species are more vulnerable to climate change [20, 21] because their specific requirements of habitat reduce their adaptive capacity to climate change [22]. As the ‘‘climate refugia’’ of protected species, nature reserves (e.g., protected areas, national parks) would lose their effectiveness

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under climate change, as species may move out of current distributions or face local extinction [15]. It had been predicted that the amount of potential habitat within protected areas would decrease under climate change [23]. Successful management of nature reserves would be implemented by protecting species from their threaten factors; it is increasingly clear that climate change should be taken into consideration when developing protection programs or designing nature reserves for rare or endangered species [14, 23, 24]. However, biodiversity conservation is biased by knowledge gaps of species distributions, and further studies are needed in data-poor regions [25]. It has been argued that China would be one of the most heavily impacted regions in the world under climate change [26, 27]. Localized studies of individual species in China have found evidence for potentially negative effects of climate change such as range shrink [28–30]. However, how climate change would affect China’s bird species, protected bird in particular, has rarely been studied [28, 29, 31], partly due to incomplete species occurrence data. In addition, most nature reserves in China were established without systematic planning [32] and climate change consideration, which may lead to a limited level of effectiveness against climate change. The reliability of biogeography study is highly dependent on the data quality that fits for the purpose [33, 34]. However, a bird distribution database that is convenient for spatial analysis in China has rarely been studied [35, 36]. In recent years, citizen science [34, 37] displayed a great potential in providing extensive data on species distributions [38–41] but its inherent identification errors and geographical biases have been criticized [42, 43]. We used species distribution modeling (SDM) to examine distribution range shift, species turnover rate and conservation effectiveness of Chinese protected birds. SDM [44–46] is a useful tool in assessing the impact of climate change on protected species [47] and conservation areas [14, 23]. These models help us to understand the relationship between species occurrence and environment, and project it into future distributions under climate change. Widespread use of SDM has been criticized by its uncertainty; it has been reported that variable-selection [48, 49], thresholding techniques [50, 51] and the choice of future climate scenarios [52] would alter model projections. However, models under careful treatment remain to be effective approaches widely used in assessing the impact of climate change [14, 53, 54]. We addressed these problems by using a carefully selected set of environmental variables including climatic and non-climatic factors, a pre-comparison among different thresholds and parameters, and a voting approach to dealing with discrepancy among model outputs.

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To investigate the potential impact of climate change on Chinese protected birds, we used citizen-based distribution data of 108 protected bird species and SDM, to: (1) explore the potential impact of climate change; (2) quantify and map the spatial pattern of species range shift; (3) evaluate the potential effectiveness of nature reserves in China under climate change.

2 Data 2.1 Study area and species distribution data The main study area was the mainland of China including Hong Kong (China for short); all islands were removed except for Hainan Island. The climate of China is extremely diverse, with a subarctic climate in northern parts, whereas a tropical climate in southern fringes. Monsoon winds dominate most eastern regions, but Qinghai–Tibet Plateau has a different climate due to high elevations. Using a citizen-based database developed from birdwatching reports [55], we investigated 108 breeding species of Chinese protected birds, including 56 species of threatened birds [56] (i.e., EN, VU, CR conservation status species based on IUCN Red List http://www.iucnredlist. org/) and 11 endemic birds [57]. Species distribution data were obtained from China Bird Report (CBR, http:// birdtalker.net/) between 2003 and 2007. CBR is a platform for professional or amateur bird lovers to report their observations, which provides the latest distribution information about Chinese birds. All reports were examined by experts, and reports with wrong or vague geographical descriptions were removed. Because of statistical problems caused by limited number of occurrence data, we excluded species with fewer than 10 records [58]. Breeding distribution of migrate species was extracted according to time information. The list of species modeled is provided in Table S1. 2.2 Environment data For suitability analysis, we considered a total of 27 environmental variables including climate, topography, vegetation and hydrology data (Table S2). All data were resampled into 1 km 9 1 km grid cells. Current climate variables were extracted from WorldClim v.1.4 database (www.worldclim.org/), representative of 1950–2000. Since correlated predictors may cause over-parameterization and a reduced predictive power, we used Pearson’s correlation analysis to reduce the number of correlated climatic variables [54]. For variable pairs with P [ 0.9, we only

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retained one of them followed previous studies [59, 60]. We selected input variables that reflect properties of energy and water, known to play important roles in determining species distributions [61, 62], and had the lowest correlation with other factors. Future climate projections for 2020, 2050 and 2080 were also derived from WorldClim; we chose four GCM outputs (CCCMA3, CSIRO2, HadCM3 and NIES99, see Table S2) from two Intergovernmental Panel on Climate Change regional climate change scenarios: A2 and B2 [63, 64]. In general, China will be warmer and wetter in the twenty-first century (Table S3), but GCM outputs showed high discrepancy in Qinghai–Tibet Plateau. We used SRTM v4.1 to generate topographic variables. Vegetation was described by NDVI (http://westdc.westgis. ac.cn) and vegetation type [65]. Hydrological variables were extracted from national river and wetland maps [66]. To evaluate effectiveness of nature reserve network in China, we used protected area distribution data derived from the World Database of Protected Areas (WDPA, http://www.wdpa.org/). WDPA contains points and polygons data, after clipped by China boundary (http://www. geodata.cn/), those points overlay on polygons and without information about cover areas were removed. Data processing and statistical analyses were performed using ArcGIS 9.3.

3 Methods 3.1 Species distribution modeling Here, we adopted MAXENT [67], a machine-learning algorithm based on the principle of maximum entropy, to predict potential distribution. MAXENT is one of the best presence-only SDMs which is robust to biased samples [68–70]. For each species, presence data were randomly partitioned into two sets, using 75 % for training and 25 % for validation. We implemented MAXENT using version 3.3 (http://www.cs.princeton.edu/*schapire/maxent/) and default settings recommended [71]. Area under the curve (AUC) of a receiver operating characteristic (ROC) plot [72] was used to measure model performance. AUC is a threshold-independent accuracy measure that is widely used in species distribution modeling [27, 73]. We used threshold that maximum training sensitivity plus specificity logistic (determining model parameter and threshold see supporting information) to divide the probability maps into presence and absence areas [50, 74]. The number of species that could survive (presence) in each grid cell (1 km2) is identified as species richness SR following Eq. (1).

SRi ¼

M X

Sij ;

ð1Þ

j¼1

where Sij is the predicted suitability score for species j in grid cell i which is converted from continuous values to 1 denote presence and 0 denote absence. SRi is the species richness in grid cell i, ranged from 0 when this grid cell is unsuitable for all species to M (M = 108) for cell is suitable for all species. The estimated current distribution was used as the baseline for comparison with predicted suitability in projected climate scenarios. By swapping current climate conditions for projected climate variables in the input data, we obtained the predicted distribution in the future. Under two climate scenarios and four GCMs, the model produces 24 future distribution maps for each bird species (4 GCM* 2 Scenario* 3 timelines). To ensemble multiple results, we took a voting approach based on the binary presence/absence maps. One grid cell will be suitable for a given bird species only if more than 50 % of all models (three or more) reach same results. 3.2 Assessing climate change influence In order to assess the impact of climate change on protected species, we measured the change in predicted distribution range and center for each species. Here, we adopted a widely accepted assumption that the real distribution of species would be somewhere in between two extremes of range shifts due to different dispersal abilities of birds: full dispersal and no dispersal [18]. With full dispersal assumption, bird species are assumed to expand their ranges into future suitable habitat. With no dispersal assumption, birds do not expand beyond their existing range, though parts of the existing range may become unsuitable in the future. To evaluate species composition change, we calculated a species turnover rate Ti for a given grid cell as: Ti ¼

Gi þ Li ; SRi þ Gi

ð2Þ

Gi is the number of species gained in the cell i. Li is the number of species lost, and SRi is the current species richness found in the cell [75]. Species turnover rate can measure the dissimilarity between the current and future species composition [76], and often regarded as a good proxy of community structure in the assessment of potential impacts of climate change [77]. The evaluation of future effectiveness of nature reserves starts with measuring richness in each grid cell following [15]. For each grid cell i, we multiplied its richness by its

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proportion covered by a given nature reserve following Eq. (3), SRres i ¼ CAi  SRi ;

ð3Þ

CAi is the proportion of grid cell i covered by nature reserves, and SRres is the species richness in grid cell i i covered by nature reserves. CAi ranged from 0 to 1. SRres i ranged from 0 when the grid cell is unsuitable for all species or with no areas conserved, to M, for cell with climate suitability equal to 1 for all species and fully conserved by nature reserves. Species richness within the given nature reserve n is quantified by averaging all grid cells, SRres n ¼

N 1 X  SRres ; N i¼1 i

ð4Þ

where SRres is species richness within a given nature n reserve n (n = 1, 2, …, 2139), and N is the number of grid cells in the given nature reserve. This projected richness change for each nature reserve is identified as the protective capacity.

4 Results Our results indicate that most species would experience habitat shrink and northward shift in a future, wetter and warmer China (Table S3 in Supporting Information), and this range shift would become more prominent through timelines (Table 1). Under the full dispersal assumption, 19 species would lose more than half of their habitats by 2080 for A2 scenario (13 for B2 scenario), and 11 (6 for B2) of them (see Table S1 in Supporting Information) would suffer from more serious habitat loss ([80 %). However, several species will still be climate change winners (expand into newly suitable habitat) under the full dispersal assumption. With the no dispersal assumption, 24

species would lose more than 50 % of their habitat and 11 species would lose more than 80 % of their habitat by 2080 in A2 scenario (14 species would lose more than 50 % of their habitat and 6 of them would lose more than 80 % under B2 scenario). 4.1 Species richness change Climate change has significant impact on future richness pattern of protected birds in China. Take 2080 for instance, the most negatively affected regions (with highest proportion of climate losers) under scenario A2 would be distributed between 20–30N, where up to 30 % of protected birds would disappear (Fig. 1). Projected richness maps would be similar under A2 or B2 scenarios, but the situation under scenario B2 would be better (nearly 40 % of study areas have higher richness than A2 scenario). However, in high-latitude regions such as the three northeastern provinces between 45–55N, richness of protected bird species would increase more than 40 % in 2080. The average north shift of the distribution center (the average latitude of all suitable cells) of all species would be 0.81 (approximately 90 km) by year 2080 for the A2 scenario (77 km for B2 scenario). A total of 34 species would move more than 50 kilometers northward under A2 scenario (28 species under B2 scenario). In addition, most habitat loss would happen in eastern China. Western China would be less affected. Species richness in the 5000- to 6000-m elevation zone would increase considerably, mainly because of the increase in suitability in the Qinghai–Tibet Plateau region (where average elevation is 4500 m), given the relatively low species richness there at present. In general, protected birds will migrate to higher latitudes and higher elevation in response to climate change. Grid cells with the top 5 % highest species richness will move northward by nearly 1 and upward by about 200 m in the mainland of China.

Table 1 Projected habitat change of protected bird species (n = 108) in 2020, 2050 and 2080 under A2/B2 scenarios, no dispersal*full dispersal assumption Scenario

Statistics

2020

2050

2080

No. Species

97–108

92–108

92–108

Aver_habitat change

-11.5 % to -14.76 %

-19.21 % to -24.12 %

-27.33 % to -33.17 %

No. species

98–108

93–108

94–108

Aver_habitat change

-12.93 % to -15.97 %

-16.58 % to -21.16 %

-21.67 % to -27.18 %

0–11

0–16

0–16

Habitat contraction (full dispersal*no dispersal) A2 B2

Habitat expansion (no dispersal*full dispersal) A2

No. species Aver_habitat change

0 %–2.48 %

0 %–4.9 %

0 %–8.99 %

B2

No. species

0–10

0–15

0–14

Aver_habitat change

0 %–2.6 %

0 %–4.23 %

0 %–6.35 %

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Fig. 1 Species richness change for Chinese protected birds from present to 2080, under the A2 scenario and their latitudinal/longitudinal/ altitudinal gradients. The change in species richness is colored from blue to red. The arrows represent the distance (magnitude) and direction of change of species habitat distribution centers over the 80 year period. For latitudinal/longitudinal gradient of species richness, the black line stands for the full dispersal assumption and the gray line for no dispersal assumption

4.2 Species turnover rate Species turnover map (Fig. 2) also shows a gradient along latitude. Given the low species richness at present, northern China would gain many more species from the south, leading to a relatively high turnover rate. Turnover rate would also be high in Qinghai–Tibet Plateau as a result of gains from lower elevations. The high rate of turnover in North China Plain is caused by its relatively high species loss in the future, although the absolute loss of the low and middle reaches of Yangtze River is much greater. Our results suggest that turnover rate will increase with time, implying large changes in suitable habitat (Figs. S1a, b) in response to climate change. 4.3 Influence on nature reserves Range shifts may reduce protection effectiveness by pushing birds out of their protected areas. Our results

shows great negative impact on Chinese protected areas of future climate change, and this impact would be more severe through time (Figs. S1-c, d). Loser species are predominant in most nature reserves of China. For the A2 scenario, nearly 90 % of conservation areas would suffer from decline in species richness in 2080 (86.5 % for the B2 scenario). The average species loss would be 3–4 species under both scenarios. One hundred and twenty-three protected areas among them, mostly in southern China, would each lose more than 10 protected bird species in 2080 under A2 scenario (33 protected areas for the B2 scenario). If the proportion of suitable habitat area in nature reserves declines to zero, affected species could lose protection completely. Fourteen species would lose all their suitable habitat area in WDPA in 2080 for the A2 scenario (10 for the B2 scenario). Geographically, the most negatively affected protected areas are all distributed in southern China, along the lower and middle reaches of Yangtze River basin. Top 10 worst affected protected areas

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Fig. 2 Turnover rate (G ? L)/(SR ? G) for Chinese protected birds from present to 2080, under the A2 scenario and their latitudinal/longitudinal/altitudinal gradients. Turnover rate is colored from blue to red. The condition of (SR ? G) = 0 indicates a cell unsuitable for any species both at present and in the future and is colored in gray. For latitudinal/longitudinal gradient of species richness, the black line stands for the full dispersal assumption and the gray line for no dispersal assumption

distributed in Jiangxi (6), Guangdong (3) and Hubei provinces (1), where intensified human activities could worsen this situation. However, species richness will rise in northeast and Tibet’s protected areas, such as Yalvjiang protected area (Fig. 3).

5 Discussion We successfully modeled (AUC 0.94 ± 0.06) climate change impacts on 108 protected bird species and protected areas. Our results reinforce the widely recognized point of view that under future climate change, specie ranges would shift northward or upward [12, 78, 79]. More than 85 % of protected bird species would become climate losers under A2/B2 scenarios. Future suitable habitats for protected birds will beyond their current ranges, leading to drops in

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protection effectiveness for nature reserves in China. In line with former projections about range change [28, 29, 80, 81], our results demonstrated national protected species may severely be threatened by climate change and showed vulnerable species that need specific concern (Table S1). Even under mild climate change scenario (i.e., B2 scenario) and full dispersal assumption, several key species under high-level protection would lose most of their potential climatic suitable habitat. Previous study of Chinese avian species often focuses on one or more specific species; the assessment of protected species and nature reserves was limited [82]. Using analysis of species range shift within nature reserves under twenty-first century climate change, we present that climate change is a severe threat to the Chinese nature reserve network, since most nature reserves would lose their capacity to provide suitable habitat for bird species of

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Fig. 3 Species richness change in nature reserves in 2080 A2 scenario under full dispersal assumption. Protected areas are colored from red to green representing species richness change from decrease to increase. The 10 worst affected protected areas were colored in purple

current conservation concern under A2 and B2 scenarios. In line with previous studies [80], our results revealed nature reserves in Yangtze River basin need improved management strategies. Six of top 10 most affected nature reserves located in Jiangxi Province (Fig. 3), indicated it is urgent to enhance their conservation effectiveness against climate change. In addition, the most negatively affected region along the lower and middle reaches of Yangtze River basin is under dramatic land-use change in the last decade [83, 84], resulting in great anthropogenic habitat fragmentations and population declines [85]. It can be expected that some insufficiently mobile species would be unable to keep pace with climate change. Changes in climate associated with the human disturbances would threat protected species and nature reserves in that region. Although SDM has been widely used in a variety of scientific issues, its theoretical limitation and assumptions have been criticized including ignoring the effects of dispersal limitations and adaptations [86], acclimatization and persistence ability [87], equilibrium with climate [45], as well as environmental predictor selection that need future testing [48, 49, 88]. In our study, biotic and abiotic factors that shape species distribution patterns can cast doubt on our conclusion. The decline of species richness in the southern

part of our study area could be underestimated since species interdependency was not considered in our study, and distribution alteration of some key species may cause secondary extinctions [89, 90]. On the other hand, the increase in species richness in southern Qinghai–Tibet Plateau and northeast China could be overestimated due to the full dispersal assumption. High elevation of Qinghai–Tibet Plateau may be a barrier to some species. Although accurate predictions require full knowledge of species dispersal ability which is not always available, we believe some species may not be able to track climate change fast enough to potential suitable habitats [91–93]. The selection of predictor variable can affect the prediction power and add uncertainty [48, 49]; it remains a challenge depending on both the data availability and our understandings about the environmental requirements of concerned species [86, 87]. In our study, we included a more comprehensive set of predictors as suggested by previous studies [93, 94], but also increase the risk of over fitting [95] and collinearity [96]. This study includes several species that have habitat requirements outside our study area. Given the limited study area, our results may be insufficient to evaluate the extinction risk of these protected birds as climate change also occurs outside China. Moreover, the problem of sampling bias has

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received much attention in predicting species potential distribution using presence-only data. Occurrence data are often biased toward easier to access such as cities and roads [96], especially for observation reports from volunteer birders [42]. However, in a comparison among four different sources of distribution dataset including the bird-watching database, GBIF (GBIF, http://www.gbif.org/), Birdlife International and NatureServe database (http://www.birdlife.org/) and a checklist of birds of China [97], citizen science data performed well and stable measured by four accuracy metrics including AUC, TSS, overall accuracy and Kappa (unpublished materials). Although the data bias might hamper the effectiveness of our result for decision making, we demonstrated that species of concern will be greatly influenced by future climate change, and local extinction is likely to occur.

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6 Main conclusion 15.

Our results provide the first detailed assessment of protected birds and nature reserve network in China under future climate change. Some key species and nature reserves that need specific concerns were displayed in our study. In consistence with previous studies, species will migrate northward and upward due to climate change, resulting in effectiveness decline of nature reserves. Southern China will lose suitable climate habitat for protected bird species, while northeast areas and Qinghai– Tibet Plateau will gain species in the future. Acknowledgments This work was supported by the National High Technology Research and Development Program of China (‘‘863’’ Program) (2009AA12200101) and the National Natural Science Foundation of China (41471347). We thank Wen Hanqiuzi, Fumin Lei and Sergey Venevesky for their comments on the paper.

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