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Implications of flood disturbance for conservation and management of giant panda habitat in human-modified landscapes
Biological Conservation 232 (2019) 35–42
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Implications of flood disturbance for conservation and management of giant panda habitat in human-modified landscapes Eric I. Amecaa,b, Qiang Daic, Yonggang Niea, Xiaodong Gud, Fuwen Weia,e,
T
⁎
a Key Laboratory of Animal Ecology & Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China b Faculty of Biology, University of Veracruz, Circuito Gonzalo Aguirre Beltrán s/n, Zona Universitaria, Xalapa, Veracruz 91090, Mexico c Chengdu Institute of Biology, Chinese Academy of Sciences, Meilingshiguan Food Unit, Wuhou, Chengdu, Sichuan 610000, China d Sichuan Station of Wildlife Survey and Management, Chengdu 610082, China e University of Chinese Academy of Science, Beijing 100049, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Ailuropoda melanoleuca Extreme weather events Flood disturbance Habitat use Human density Nature reserves
As certain extreme weather events are becoming frequent and intense, conservationists must identify areas across species' ranges recurrently affected, especially with regard to threatened species. Focusing on the giant panda (Ailuropoda melanoleuca) and historical flood frequency distribution, we determined overlaps between panda distribution affected by floods and nature reserves. We also examined the correspondence between areas subject to high flood exposure densities, areas with high panda habitat use, and areas that exhibit high human density. Of the 67 reserves established for giant panda conservation 7 included areas with the highest flood exposure densities while having a mean exposure ranging between 20 and 75%. In Sichuan province up to 32% of areas of high habitat use were subject to low flood density, and 10% overlapped with areas subject to high flood density. We also found that 40% of the total area with high human density was subject to a high flood density. Our findings indicate that high frequency of flooding is affecting areas of nature reserves where people are rather than areas which pandas are using more intensively. In areas occupied by pandas, strategies should remain focus on mitigating habitat degradation and fragmentation caused by human activities that can also reduce habitat resilience to floods. Management aimed at reducing vulnerability and enhancing resilience in flood-prone areas is warranted if we are to prevent negative indirect impacts on panda habitat driven by human responses to increasingly frequent and intense extreme weather events in the coming decades.
1. Introduction The habitat requirements and the influence of human pressures on extinction risk of the giant panda (Ailuropoda melanoleuca) have been studied comprehensively (e.g., Hull et al., 2014; Liu et al., 2001; Zhang et al., 2011). As a result of decisive policies and interventions (Wei et al., 2015), the giant panda was downlisted from “Endangered” to “Vulnerable” in the IUCN Red List of Threatened Species (Swaisgood et al., 2017). This does not mean wild giant panda populations are no longer threatened but that their extinction risk has been progressively reduced. Priorities are now shifting towards targeting emergent threats that erode the spatial cohesion of remnant habitat and alter the species' range dynamics (Viña et al., 2010; Xu et al., 2017; Zhao et al., 2017). With the availability of climate datasets and an increasing understanding of panda ecological requirements, there have been several
studies aimed to predict changes in current and potential distribution of suitable habitat at different scales. In a recent study, integrating temperature, elevation, vegetation, bamboo distribution data, and habitat suitability it has been estimated that panda habitat in north Minshan Mountains will increase by 2.7% over the next 50 years (Liu et al., 2016) and, therefore, implying this region might be capable of supporting the expansion of wild giant panda populations. By contrast, bioclimatic envelope modelling applied for the entire panda geographic distribution suggests that over half of the present habitats could be lost through degradation caused by increasingly warmer and drier climate by 2070 (Li et al., 2015). Wild giant panda populations, however, have historically been exposed to warmer climate and occupied habitats at lower elevations prior the intensification of human pressures (Hu and Wei, 2004; Hunt Jr, 2004; Swaisgood et al., 2016). Thus, should these predictions prove accurate, the species has the potential to adapt with
⁎ Corresponding author at: Key Laboratory of Animal Ecology & Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China. E-mail address: [email protected] (F. Wei).
https://doi.org/10.1016/j.biocon.2019.01.019 Received 21 August 2018; Received in revised form 12 January 2019; Accepted 24 January 2019 0006-3207/ © 2019 Published by Elsevier Ltd.
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upon which they depend for survival.
expected shifts in climate conditions, yet sound management interventions will need to be in place to assist the recovery of habitat gaps and connectivity in formerly occupied areas (Swaisgood et al., 2017). Climate-related impacts on species, however, do not derive from exposure to slowly developing changes in climate alone. Exposure to extreme weather events (e.g., cold waves, floods, heat waves) can also have adverse consequences on species (Lescroël et al., 2014; Pavelka and Chapman, 2006; Wernberg et al., 2013). In this regard, intrinsic biological attributes may help species' populations to withstand a climate-related change (e.g., sea level rise, change in seasonality, extreme events) or adapt to it for a given level of exposure (Zhang et al., 2016). Nevertheless, the interacting effects between extreme weather events and human activities (including responses forced by extreme events) may surpass species capacity to withstand and/or adapt, and hence increasing their vulnerability to population declines and potentially local extinctions (Brook et al., 2008). Floods are a type of extreme weather events predicted to increase in frequency and intensity under global climate change (Hirabayashi et al., 2013; Seneviratne et al., 2012). In southwest China, analyses of long-term trends in flood incidences reveal both increased frequency and increased intensity over the past half century (Ji et al., 2015). In particular, areas encompassing the distribution of the giant panda are prone to floods generated by the interaction between episodes of intense precipitation, the topography of mountainous terrain, and the ongoing effects of previous deforestation (e.g., soil erosion, reduction in water retention, increase in superficial runoff) (He et al., 2012; Taylor et al., 1996). Floods can interrupt the normal activities of animal populations and trigger behavioural responses to the harsh environmental conditions being experienced (Alho and Silva, 2012). In the short term, species poorly adapted to flood disturbance and/or those with limited opportunities for dispersal can experience injuries from mechanical friction of debris flow or drowning (Wuczyński and Jakubiec, 2013). Declines in animal populations have also been associated with the interplay between flood-related habitat changes (e.g., loss of vegetation cover, soil erosion, slope steepness, debris accumulation) and exposure to anthropogenic stressors (Choudhury, 1998; Swanson et al., 1998). The natural history of giant panda indicates that avoidance of habitat disturbance may be facilitated through spatial behaviours that are associated with optimal foraging (Wei et al., 2015) and/or localization of den sites and water bodies (Zhang et al., 2011). This may allow individuals to avoid areas previously known to be unsuitable and relocate to non-disturbed areas (Hull et al., 2016; Meng et al., 2016; Tarou et al., 2004). Greater dispersal across continuous habitat and higher availability of food and shelters may have played a significant role in reducing the stress imposed on panda populations by large environmental disturbances such as bamboo die-offs, floods, earthquakes and wildfires over millennia (Hu, 2001; Johnson et al., 1988; Meng et al., 2016; Sun et al., 2016). However, the continuing effects of human-induced habitat degradation and fragmentation over the past century (Zhu et al., 2013) may mean that disturbance generated by extreme weather events alters panda habitat with greater magnitude. In particular, human activities in response to these events may consequently result in negative indirect impacts for wildlife and habitat. In order to help develop strategies to build resilience and support recovery from extreme weather events, information on patterns of exposure is warranted. Here, we use data on historical flood frequency, giant panda distribution and habitat use, nature reserve boundaries, and human density inside nature reserves to (i) examine spatial patterns of flood exposure; (ii) quantify the proportion of panda distribution subject to floods across nature reserves; and (iii) determine the correspondence between areas subject to high flood exposure densities, areas of high giant panda habitat use, and the areas exhibiting high human density. With this study, we advocate the need to better understand species responses to extreme weather events as well as to develop context-specific strategies to reduce deterioration of risk-prone areas
2. Materials and methods 2.1. Study area The study area consisted of the six mountainous regions (Qinling, Min, Qionglai, Greater Xiangling, Lesser Xiangling, and Liang) at the eastern edge of the Tibetan Plateau where the giant panda is currently distributed. The largest populations are currently found in Qinling, Min, and Qionglai whereas the ones with smaller size are distributed in Liang, Greater Xiangling, and Lesser Xiangling and face a higher risk of local extinction (Swaisgood et al., 2016). The characteristics of the region (e.g., elevation, precipitation regimes, slope steepness, soil structure, and vegetation cover) favour the occurrence of floods. Large areas comprising the giant panda habitat are also prone to earthquakes (Wen et al., 2008) which can generate flooding as a result of the collapse of loose materials caused by localized heavy precipitation (Cui et al., 2012). 2.2. Giant panda distribution data We obtained the giant panda geographic distribution from the IUCN Red List Assessment of Threatened Species version 2017.1. Following the coded domain values for species presence in the guidelines, we selected range polygons labelled as “extant” as they represent areas where the species is known or very likely to occur. Hence, by focusing on these areas (hereafter refer to as giant panda distribution) we aimed to avoid overestimating flood exposure while targeting areas most likely to be occupied by panda populations today and the recent past. 2.3. Flood data We extracted the frequency and geographic distribution of flood extents as GIS vector data from the Dartmouth Flood Observatory (Brakenridge, 2012). The observatory uses imagery gathered by satellite remote sensing sources, and weather serving reporting to measure and draw bounding polygons of areas affected by flood events from 1985 to the present (Brakenridge and Anderson, 2006; Reager and Famiglietti, 2009). Flood data after 2013 was not available for the study area. Therefore we extracted flood data for the period 1985–2013 with focus on the panda distribution. Moreover, to compare individual flood events, we estimated a flood magnitude index defined by the following formula: log10 (D x T x A) where D is the flood duration in days, T is the recurrence interval of flood severity in years, and A is the aereal extent affected by a given flood in km2. Because of the logarithmic basis of the scaling (resembling the Richter Scale for earthquakes), each whole number step in the index represents a tenfold increase in magnitude, so that floods reaching 7, 8 and 9 are associated with truly large magnitude events (Kundzewicz et al., 2014). The flood dataset included records that took place within the same geographic areas and hence may be influenced by similar environmental conditions (e.g., climate, topography) (Dormann et al., 2007). In this regard, to evaluate the existence of spatial autocorrelation in our flood dataset, we performed a mantel test using two distance matrices: one containing spatial distances and one containing distances between estimated flood magnitudes. We ran correlations with 9999 permutations using the mantel.rtest function in the R package ‘ade4’, version 1.7–8 (Dray and Dufour, 2007) in R (Team, 2017). No significant spatial autocorrelation was found (Mantel test, R = − 0.007; Permutation P value = 0.42); it was therefore unnecessary to correct for this type of spatial dependence. 2.4. Quantifying flood exposure Flood exposure was defined as any overlap between areas affected 36
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by individual flood events and the giant panda distribution. Thus using the intersect geoprocessing tool in ArcGIS (version 10.3) (ESRI, 2015) we superimposed the spatial extent of individual flood events one at the time with the giant panda distribution and examined flood frequency and levels of coverage. In a second stage we overlapped the giant panda distribution exposed to floods and the 67 nature reserves established for giant panda conservation. In doing so, we obtained the spatial boundary of nature reserves from the China's State Forestry Administration (SFA) (SFA, 2015a). We also generated a density map of flood exposure (number of individual flood incidences per grid of 1 × 1 km resolution) targeting areas that constitute the giant panda distribution, including the network of nature reserves. With this approach we aimed to identify areas more recurrently exposed to floods while having management support and access to government funding for development projects in place. Thus, flood risk management in these areas is most likely to be funded, implemented and monitored.
Heishuihe, Wawushan (all n = 17), and Longxi-hongkou, Maanshan and Wolong (all n = 16) (Fig. 3). 3.2. Giant panda habitat use and flood exposure Based in our analysis of giant panda presence sign data from the third national survey, 10% of areas with high habitat use overlapped areas in the upper quartile of the distribution of flood densities whereas 32% were found in areas in the lower quartile (Fig. 4a). High habitat use derived from the fourth national survey revealed a similar pattern: 15% of total area with high habitat use overlapped areas in the upper quartile of the distribution of flood densities, and 30% with areas in the lower quartile (Fig. 4b). 3.3. Human population density and flood exposure Examination of human density in the year 2010 in the panda distribution within nature reserves revealed that 40% and 19% of the areas with high human density were affected by flood densities in the upper and lower quartile of the distribution, respectively (Fig. 5). On the other hand, 23% and 44% of the areas with low human density overlapped with flood densities in the upper and lower quartile, respectively. A list with the nature reserves by mountain range that capture areas with high human density subject to high flood densities is available in Appendix S1.
2.5. Flood exposure and giant panda habitat use A high density of giant panda signs at a given location represents a high habitat use in that area (Hull et al., 2014; Zhao et al., 2017). In this regard, we used presence sign data from the Third and Fourth Giant Panda National Surveys in Sichuan Province (SFA, 2006; SFA, 2015b) to determine such areas. The protocols for the two surveys (conducted between May to July in 2000 and 2012, respectively) were the same: grids with size of 1.4 × 1.4 km were generated over the survey regions and giant panda signs (e.g., panda droppings, footmark, hair, forage traces) were surveyed along transects in each grid. We identified areas of high habitat use by conducting a kernel density analysis in ArcGIS (bandwidth h = 5000 m and 1000 m resolution) on all panda signs for each census. We defined areas of high habitat use as the densest 50% of the grids with a density > 0.001 panda signs/km2 and use them to perform an intersect analysis with the flood density dataset. In particular, we targeted overlaps with areas in the upper quartile of the distribution of flood exposure densities (hereafter refer to as flood densities) prior the third and fourth giant panda surveys in Sichuan Province (1985–1999 and 2001–2011, respectively).
4. Discussion Because different types of extreme events have been in operation throughout giant panda's existence (e.g., bamboo die-offs, floods, earthquakes, wildfires) (Johnson et al., 1988; Meng et al., 2016; Sun et al., 2016), it is expected that adaptation over thousands of years of evolution (e.g., microhabitat selection, spatial memory abilities, use of environmental cues to time phenological events) and greater quality, extent, and connectedness of habitats may have enabled giant pandas to adapt to their changing environment over millennia (Wei et al., 2015). However, unlike in past periods, the habitat of giant pandas is being confronted with rapidly evolving threats resulting from human impacts. In addition, flood frequency has increased in the region over the past half century (Ji et al., 2015) which may lead human communities to exploit with more intensity natural resources that are crucial for the giant panda. Our results show that high frequency of flooding in Qionglai, Liang, and Greater Xiangling mountains is affecting areas inside reserves with high human density rather than areas which pandas are using more intensively. It is important to note that reserves include non suitable habitat for the species that is currently occupied by people (e.g., mountain valleys and areas below the floodplain). In these rural areas people are highly dependent on local natural resources for their livelihoods (An et al., 2006). Thus, it is expected that resource use will intensify in order to mitigate or adapt to sudden episodes of extreme environmental disturbance (Turner et al., 2010). This demand in local natural resources may cause negative indirect impacts on wildlife and habitat. In a longitudinal study on the impact of different extreme events on rural livelihoods in Sichuan, Liu (2014) reported an annual increment in frequency and intensity of flooding events (period 1990–2013) after a magnitude 7.9 earthquake struck the region in 2008. Insufficient resources to cope with increasingly frequent flooding, led local people in Wolong National Nature Reserve to practice illegal logging and hunting to sustain their livelihoods (Liu, 2014). Wood harvesting to rebuild damaged properties can affect the forest that pandas depend on for shelter and denning, and provides suitable conditions for bamboo to grow which constitute 99% of the pandas' diet (Zhang et al., 2011). On the other hand, pandas can be unintentionally trapped in snares placed for hunting other wildlife (Swaisgood et al., 2016). In our study, we
2.6. Flood exposure and human density To examine how areas having a high human density overlap with flood densities across the panda distribution inside nature reserves, available population data was extracted from the Gridded Population of the World (GPW-v4) (CIESIN, 2017). The GPW uses population input data collected at the most detailed spatial resolution and applies the areal-weighting method to disaggregate census data (Doxsey-Whitfield et al., 2015). For China, the GWP used population input data from the “2010 county population census” using the township level data for gridding. Areas with high human density were defined as those with a number of persons per grid (1 km x ~1 km resolution) in the upper quartile of the distribution across the study area. Finally, we overlaid flood densities (period 1985–2010) on the human density map and investigated levels of coverage. 3. Results 3.1. Panda distribution and nature reserves exposed to floods Between 1985 and 2013 our analyses show that 18 floods, of a total 34, overlapped individually ≥25% of the giant panda distribution. 14 of these events had a magnitude ≥7 (Fig. 1). Of the 67 nature reserves, 30 contain areas of the giant panda distribution that were subject to 9–21 floods. In particular, flood exposure in 11 reserves (6 in Qinling, 3 in Min, 1 in Qionglai, and 1 in Liang mountains) was > 50% (Fig. 2). The greatest flood density affecting the panda distribution inside nature reserves was found in Laojunshan (n = 21), followed by Fengtongzhai, 37
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Fig. 1. Percentage of flood exposure, categorized by magnitude, across the distribution of the giant panda (Ailuropoda melanoleuca) (period 1985–2013). The magnitude index combines the recurrence interval of flood severity with the extent of each flood event and its duration. Each whole number step in the index represents a tenfold increase in magnitude reflected by the size of the circles.
natural resources for emergency mitigation. We found that high habitat use by giant pandas overlapped primarily with areas subject to low and moderate flood incidence. In this regard, availability of microrefugia (Zhang et al., 2011) and spatial behaviours associated with optimal foraging and localization of water, den sites or mates may help pandas to disperse to non-disturbed
identified 15 nature reserves containing areas where people confront a high flood exposure (Appendix S1). In these areas, we recommend the establishment of flood warning systems, risk assessments and risk reduction plans to increase preparedness and minimize loss of lives and livelihoods. Furthermore timely and adequate supply food and materials for reconstruction will be critical to help reduce the demand in
Fig. 2. Nature reserves grouped by mountain range that capture areas of the distribution of the giant panda (Ailuropoda melanoleuca) exposed to flood events (period 1985–2013). Graphs show the number of floods (bars) and the mean percentage coverage of the total nature reserve capturing areas of the panda distribution exposed to floods (dots). 38
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Fig. 3. Density map of flood exposure (period 1985–2013) across areas that constitute the distribution of the giant panda (Ailuropoda melanoleuca), including the network of 67 nature reserves established for giant panda conservation. Areas affected by individual flood events (blue colour scale) are represented using grids of 1 × 1 km resolution. The letters indicate 7 nature reserves exhibiting grids in the upper quartile of the distribution: a, Laojunshan; b, Fengtongzhai; c, Heishuihe; d, Wawushan; e, Longxi-hongkou; f, Maanshan; g, Wolong. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
as floods and observed patterns giant panda habitat use. Aware of these limitations, we believe our results can help inform about areas inside nature reserves where efforts addressing existing human pressures should integrate the risk assessment to natural disturbances such as floods, to maximize the benefit of conservation and management investments. Today, giant panda wild populations face different degree of isolation and habitat fragmentation resulting from ongoing human encroachment and the effects of past habitat loss. Floods are predicted to become more frequent across areas encompassing both giant panda populations and human communities which are also prone to earthsystem geohazards (Ji et al., 2015; Jung et al., 2015; Zhang et al., 2014). Our study is a first step towards monitoring context-specific habitat conditions and anticipating where further environmental disruptions may increase vulnerability. Building more resilient communities in areas susceptible to extreme weather events can help prevent additional pressures on pandas and their habitat, and hence keep improving the species' conservation status. Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biocon.2019.01.019.
habitats (Gong et al., 2017; Zhang et al., 2014). Zheng et al. (2012) found that habitat affected by landslides after the Wenchuan earthquake in the Min mountain region had a significantly lower panda presence than habitats without landslides. Zheng et al. (2012) hypothesized that pandas reduced the use of areas affected by the earthquake because the region could provide suitable habitat at other locations. Following this rationale for flood-related habitat disturbance, conservation strategies in areas occupied by pandas should remain focus on mitigating habitat degradation and fragmentation caused by human activities (logging and wood harvesting, urbanization, livestock farming and ranching) that may also increase physical exposure to floods and hamper pandas' ability to reach alternative habitat patches (Zhang et al., 2011). Current conditions of panda habitat fragmentation and overutilization may be difficult to reverse quickly in non-protected landscapes. For the giant panda and other vulnerable species, the management and maintenance of nature reserves should be ensured to avoid an increment in human interferences that could amplify the effects of extreme weather events. There are some limitations in our study. We considered that areas recurrently exposed to floods across the panda distribution will experience environmental disturbance associated with changes at the local scale (e.g., removal of understory vegetation, fallen trees) that are known to affect panda population dynamics (Swaisgood et al., 2017). However, neither habitat changes nor continuous monitoring of population size variation over time were investigated. Moreover, certain human activities that are known to alter giant panda habitat use (e.g., fuelwood collection and wood harvesting, free-ranging livestock, urbanization) may influence both incidence of natural disturbances such
Acknowledgements We greatly appreciate the State Forestry Administration for the provision of giant panda presence sign data and nature reserve data. Our research was funded by the Chinese Academy of Sciences, China (Grant No. QYZDY-SSW-SMC019) and National Key Program of Research and Development, Ministry of Science and Technology of 39
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Fig. 4. Areas of high habitat use by giant panda (highlighted in red) and flood exposure density (blue colour scale) within its distribution in Sichuan province, China. Areas affected by individual flood events for the period 1985–1999 (panel a) and the period 2000–2011 (panel b) are represented using grids of 1 × 1 km resolution. High habitat use was estimated using presence sign data of the third and fourth national giant panda surveys. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 40
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Fig. 5. Percentage of human density, by quartile range, subject to different flood extents across the giant panda distribution inside nature reserves. Human density is an estimate of the number of persons per grid of 1 by 1 km resolution for the year 2010. Flood density indicates the number of individual flood extents per grid of 1 × 1 km resolution for the period 1985–2010. Percentages per each quartile do not add up to 100% due to rounding.
China (Grant No. 2016YFC0503200). EIA acknowledges funding from the Chinese Academy of Sciences President's International Fellowship Initiative (Grant No. 2018VBB0017).
He, H., Zhou, J., Peart, M.R., Chen, J., Zhang, Q., 2012. Sensitivity of hydrogeomorphological hazards in the Qinling Mountains, China. Quat. Int. 282, 37–47. Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., ... Kanae, S., 2013. Global flood risk under climate change. Nat. Clim. Chang. 3 (9), 816–821. Hu, J., 2001. Research on the Giant Panda. Shanghai Publishing House of Science and Technology, Shanghai. Hu, J., Wei, F., 2004. Comparative ecology of giant pandas in the five mountain ranges of their distribution in China. In: Giant Pandas: Biology and Conservation. University of California Press, Berkeley, CA, pp. 137–148. Hull, V., Roloff, G., Zhang, J., Liu, W., Zhou, S., Huang, J., ... Liu, J., 2014. A synthesis of giant panda habitat selection. Ursus 25 (2), 148–162. Hull, V., Zhang, J., Huang, J., Zhou, S., Vina, A., Shortridge, A., ... Ouyang, Z., 2016. Habitat use and selection by giant pandas. PLoS One 11 (9), e0162266. Hunt Jr., R.M., 2004. A paleontologist's perspective on the origin and relationships of the giant panda. In: Giant Pandas: Biology and Conservation. 308. Ji, Y., Zhou, G., Wang, S., Wang, L., 2015. Increase in flood and drought disasters during 1500–2000 in Southwest China. Nat. Hazards 77 (3), 1853–1861. Johnson, K., Schaller, G., Jinchu, H., 1988. Responses of Giant Pandas to a Bamboo Dieoff. National Geographic Research (USA). Jung, C.-Y., Shin, H.-J., Jang, C.J., Kim, H.-J., 2015. Projected change in east Asian summer monsoon by dynamic downscaling: moisture budget analysis. Asia-Pac. J. Atmos. Sci. 51 (1), 77–89. Kundzewicz, Z.W., Kanae, S., Seneviratne, S.I., Handmer, J., Nicholls, N., Peduzzi, P., ... Mach, K., 2014. Flood risk and climate change: global and regional perspectives. Hydrol. Sci. J. 59 (1), 1–28. Lescroël, A., Ballard, G., Grémillet, D., Authier, M., Ainley, D.G., 2014. Antarctic climate change: extreme events disrupt plastic phenotypic response in Adélie penguins. PLoS One 9 (1), e85291. Li, R., Xu, M., Wong, M.H.G., Qiu, S., Li, X., Ehrenfeld, D., Li, D., 2015. Climate change threatens giant panda protection in the 21st century. Biol. Conserv. 182, 93–101. Liu, W., 2014. Forensic analyses on a compound disaster and its impacts following the 2008 Wenchuan earthquake in China. In: EGU General Assembly Conference Abstracts. 16. Liu, J., Linderman, M., Ouyang, Z., An, L., Yang, J., Zhang, H., 2001. Ecological degradation in protected areas: the case of Wolong nature reserve for giant pandas. Science 292 (5514), 98–101. Liu, G., Guan, T., Dai, Q., Li, H., Gong, M., 2016. Impacts of temperature on giant panda habitat in the north Minshan Mountains. Ecol. Evol. 6 (4), 987–996. Meng, Q.-K., Miao, F., Zhen, J., Huang, Y., Wang, X.-Y., Peng, Y., 2016. Impact of earthquake-induced landslide on the habitat suitability of giant panda in Wolong, China. J. Mt. Sci. 13 (10), 1789–1805. Pavelka, M.S., Chapman, C.A., 2006. Population structure of black howlers (Alouatta pigra) in southern Belize and responses to hurricane iris. In: New Perspectives in the Study of Mesoamerican Primates, pp. 143–163. Reager, J., Famiglietti, J., 2009. Global terrestrial water storage capacity and flood potential using GRACE. Geophys. Res. Lett. 36 (23). Seneviratne, S.I., Nicholls, N., Easterling, D., Goodess, C.M., Kanae, S., Kossin, J., ... Rahimi, M., 2012. Changes in Climate Extremes and their Impacts on the Natural Physical Environment. State Forestry Administration, P.R.C, 2006. The 3rd National Survey Report on Giant Panda in China. China Science Press, Beijing, China (in Chinese). State Forestry Administration, P.R.C, 2015a. Department of Wildlife Conservation and Nature Reserve Management. Beijing, China. State Forestry Administration, P.R.C, 2015b. The 4th National Survey Report on Giant Panda in China. China Science Press, Beijing, China (in Chinese). Sun, W., Zhang, E., Shen, J., Chen, R., Liu, E., 2016. Black carbon record of the wildfire history of western Sichuan Province in China over the last 12.8 ka. Front. Earth Sci. 10 (4), 634–643. Swaisgood, R., Wang, D., Wei, F., 2016. Ailuropoda Melanoleuca: the IUCN red List of Threatened Species. International Union for Conservation of Nature e.
Author's contributions E.I.A., and F.W. conceived and designed the study; Y.N., Q.D., and X.G. made substantial data contributions; E.I.A.J. and F.W. led the analysis; all authors contributed greatly to the discussion of results; E.I.A.J. wrote the first draft of this manuscript and all authors contributed to the final version. Data accessibility statement All input information used in this study are either available through the references provided in the text or upon request from the corresponding author. References Alho, C.J., Silva, J.S., 2012. Effects of severe floods and droughts on wildlife of the Pantanal wetland (Brazil)—a review. Animals 2 (4), 591–610. An, L., He, G., Liang, Z., Liu, J., 2006. Impacts of demographic and socioeconomic factors on spatio-temporal dynamics of panda habitat. Biodivers. Conserv. 15 (8), 2343–2363. Brakenridge, G., 2012. Global Active Archive of Large Flood Events. University of Colorado, Dartmouth Flood Observatory. http://floodobservatory.colorado.edu/, Accessed date: August 2017. Brakenridge, R., Anderson, E., 2006. MODIS-based flood detection, mapping and measurement: the potential for operational hydrological applications. In: Transboundary Floods: Reducing Risks through Flood Management, pp. 1–12. Brook, B.W., Sodhi, N.S., Bradshaw, C.J., 2008. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23 (8), 453–460. Center for International Earth Science Information Network - CIESIN - Columbia University, 2017. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 10. NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY. https://doi.org/10.7927/H4DZ068D. (Accessed October 2018). Choudhury, A., 1998. Flood havoc in Kaziranga. Pachydenn 26, 83–87. Cui, P., Lin, Y.-M., Chen, C., 2012. Destruction of vegetation due to geo-hazards and its environmental impacts in the Wenchuan earthquake areas. Ecol. Eng. 44, 61–69. Dormann, C.F., McPherson, J.M., Araújo, M.B., Bivand, R., Bolliger, J., Carl, G., Davies, R.G., Hirzel, A., Jetz, W., Kissling, W.D., Kühn, I., 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30 (5), 609–628. Doxsey-Whitfield, E., MacManus, K., Adamo, S.B., Pistolesi, L., Squires, J., Borkovska, O., Baptista, S.R., 2015. Taking advantage of the improved availability of census data: a first look at the gridded population of the world, version 4. Pap. Appl. Geog. 1 (3), 226–234. Dray, S., Dufour, A.-B., 2007. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22 (4), 1–20. ESRI, R., 2015. ArcGIS Desktop: Release 10.3 Environmental Systems Research Institute, CA. Gong, M., Fan, Z., Zhang, X., Liu, G., Wen, W., Zhang, L., 2017. Measuring the effectiveness of protected area management by comparing habitat utilization and threat dynamics. Biol. Conserv. 210, 253–260.
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Rousseaux, C.S., 2013. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Chang. 3 (1), 78–82. Wuczyński, A., Jakubiec, Z., 2013. Mortality of game mammals caused by an extreme flooding event in south-western Poland. Nat. Hazards 69 (1), 85–97. Xu, W., Viña, A., Kong, L., Pimm, S.L., Zhang, J., Yang, W., ... Liu, J., 2017. Reassessing the conservation status of the giant panda using remote sensing. Nat. Ecol. Evol. 1 (11), 1635. Zhang, Z., Swaisgood, R.R., Zhang, S., Nordstrom, L.A., Wang, H., Gu, X., ... Wei, F., 2011. Old-growth forest is what giant pandas really need. Biol. Lett. 7 (3) rsbl20101081. Zhang, Z., Yuan, S., Qi, D., Zhang, M., 2014. The Lushan earthquake and the giant panda: impacts and conservation. Integrative Zool. 9 (3), 376–378. Zhang, Q., Hong, Y., Zou, F., Zhang, M., Lee, T.M., Song, X., Rao, J., 2016. Avian responses to an extreme ice storm are determined by a combination of functional traits, behavioural adaptations and habitat modifications. Sci. Rep. 6, 22344. Zhao, C., Yue, B., Ran, J., Moermond, T., Hou, N., Yang, X., Gu, X., 2017. Relationship between human disturbance and endangered giant panda Ailuropoda melanoleuca habitat use in the Daxiangling Mountains. Oryx 51 (1), 146–152. Zheng, W., Xu, Y., Liao, L., Yang, X., Gu, X., Shang, T., Ran, J., 2012. Effect of the Wenchuan earthquake on habitat use patterns of the giant panda in the Minshan Mountains, southwestern China. Biol. Conserv. 145 (1), 241–245. Zhu, L., Hu, Y., Qi, D., Wu, H., Zhan, X., Zhang, Z., ... Gu, X., 2013. Genetic consequences of historical anthropogenic and ecological events on giant pandas. Ecology 94 (10), 2346–2357.
T712A45033386. http://www.iucnredlist.org/details/712/0. Swaisgood, R.R., Wang, D., Wei, F., 2017. Panda downlisted but not out of the woods. Conserv. Lett. 11 (1), e12355. Swanson, F.J., Johnson, S.L., Gregory, S.V., Acker, S.A., 1998. Flood disturbance in a forested mountain landscape. Bioscience 48 (9), 681–689. Tarou, L., Snyder, R., Maple, T., 2004. Spatial memory in the giant panda. In: Lindburg, D., Baragona, K. (Eds.), Giant Pandas: Biology and Conservation, pp. 101–105. Taylor, A.H., Zisheng, Q., Jie, L., 1996. Structure and dynamics of subalpine forests in the Wang Lang Natural Reserve, Sichuan, China. Vegetatio 124 (1), 25–38. Team, R.C., 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2016). Turner, W.R., Bradley, B.A., Estes, L.D., Hole, D.G., Oppenheimer, M., Wilcove, D.S., 2010. Climate change: helping nature survive the human response. Conserv. Lett. 3 (5), 304–312. Viña, A., Tuanmu, M.-N., Xu, W., Li, Y., Ouyang, Z., DeFries, R., Liu, J., 2010. Range-wide analysis of wildlife habitat: implications for conservation. Biol. Conserv. 143 (9), 1960–1969. Wei, F., Swaisgood, R., Hu, Y., Nie, Y., Yan, L., Zhang, Z., ... Zhu, L., 2015. Progress in the ecology and conservation of giant pandas. Conserv. Biol. 29 (6), 1497–1507. Wen, X.-Z., Ma, S.-L., Xu, X.-W., He, Y.-N., 2008. Historical pattern and behavior of earthquake ruptures along the eastern boundary of the Sichuan-Yunnan faultedblock, southwestern China. Phys. Earth Planet. Inter. 168 (1), 16–36. Wernberg, T., Smale, D.A., Tuya, F., Thomsen, M.S., Langlois, T.J., De Bettignies, T., ...
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Implications of flood disturbance for conservation and management of giant panda habitat in human-modified landscapes Eric I. Amecaa,b, Qiang Daic, Yonggang Niea, Xiaodong Gud, Fuwen Weia,e,
T
⁎
a Key Laboratory of Animal Ecology & Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China b Faculty of Biology, University of Veracruz, Circuito Gonzalo Aguirre Beltrán s/n, Zona Universitaria, Xalapa, Veracruz 91090, Mexico c Chengdu Institute of Biology, Chinese Academy of Sciences, Meilingshiguan Food Unit, Wuhou, Chengdu, Sichuan 610000, China d Sichuan Station of Wildlife Survey and Management, Chengdu 610082, China e University of Chinese Academy of Science, Beijing 100049, China
A R T I C LE I N FO
A B S T R A C T
Keywords: Ailuropoda melanoleuca Extreme weather events Flood disturbance Habitat use Human density Nature reserves
As certain extreme weather events are becoming frequent and intense, conservationists must identify areas across species' ranges recurrently affected, especially with regard to threatened species. Focusing on the giant panda (Ailuropoda melanoleuca) and historical flood frequency distribution, we determined overlaps between panda distribution affected by floods and nature reserves. We also examined the correspondence between areas subject to high flood exposure densities, areas with high panda habitat use, and areas that exhibit high human density. Of the 67 reserves established for giant panda conservation 7 included areas with the highest flood exposure densities while having a mean exposure ranging between 20 and 75%. In Sichuan province up to 32% of areas of high habitat use were subject to low flood density, and 10% overlapped with areas subject to high flood density. We also found that 40% of the total area with high human density was subject to a high flood density. Our findings indicate that high frequency of flooding is affecting areas of nature reserves where people are rather than areas which pandas are using more intensively. In areas occupied by pandas, strategies should remain focus on mitigating habitat degradation and fragmentation caused by human activities that can also reduce habitat resilience to floods. Management aimed at reducing vulnerability and enhancing resilience in flood-prone areas is warranted if we are to prevent negative indirect impacts on panda habitat driven by human responses to increasingly frequent and intense extreme weather events in the coming decades.
1. Introduction The habitat requirements and the influence of human pressures on extinction risk of the giant panda (Ailuropoda melanoleuca) have been studied comprehensively (e.g., Hull et al., 2014; Liu et al., 2001; Zhang et al., 2011). As a result of decisive policies and interventions (Wei et al., 2015), the giant panda was downlisted from “Endangered” to “Vulnerable” in the IUCN Red List of Threatened Species (Swaisgood et al., 2017). This does not mean wild giant panda populations are no longer threatened but that their extinction risk has been progressively reduced. Priorities are now shifting towards targeting emergent threats that erode the spatial cohesion of remnant habitat and alter the species' range dynamics (Viña et al., 2010; Xu et al., 2017; Zhao et al., 2017). With the availability of climate datasets and an increasing understanding of panda ecological requirements, there have been several
studies aimed to predict changes in current and potential distribution of suitable habitat at different scales. In a recent study, integrating temperature, elevation, vegetation, bamboo distribution data, and habitat suitability it has been estimated that panda habitat in north Minshan Mountains will increase by 2.7% over the next 50 years (Liu et al., 2016) and, therefore, implying this region might be capable of supporting the expansion of wild giant panda populations. By contrast, bioclimatic envelope modelling applied for the entire panda geographic distribution suggests that over half of the present habitats could be lost through degradation caused by increasingly warmer and drier climate by 2070 (Li et al., 2015). Wild giant panda populations, however, have historically been exposed to warmer climate and occupied habitats at lower elevations prior the intensification of human pressures (Hu and Wei, 2004; Hunt Jr, 2004; Swaisgood et al., 2016). Thus, should these predictions prove accurate, the species has the potential to adapt with
⁎ Corresponding author at: Key Laboratory of Animal Ecology & Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, 1 Beichen West Road, Chaoyang District, Beijing 100101, China. E-mail address: [email protected] (F. Wei).
https://doi.org/10.1016/j.biocon.2019.01.019 Received 21 August 2018; Received in revised form 12 January 2019; Accepted 24 January 2019 0006-3207/ © 2019 Published by Elsevier Ltd.
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upon which they depend for survival.
expected shifts in climate conditions, yet sound management interventions will need to be in place to assist the recovery of habitat gaps and connectivity in formerly occupied areas (Swaisgood et al., 2017). Climate-related impacts on species, however, do not derive from exposure to slowly developing changes in climate alone. Exposure to extreme weather events (e.g., cold waves, floods, heat waves) can also have adverse consequences on species (Lescroël et al., 2014; Pavelka and Chapman, 2006; Wernberg et al., 2013). In this regard, intrinsic biological attributes may help species' populations to withstand a climate-related change (e.g., sea level rise, change in seasonality, extreme events) or adapt to it for a given level of exposure (Zhang et al., 2016). Nevertheless, the interacting effects between extreme weather events and human activities (including responses forced by extreme events) may surpass species capacity to withstand and/or adapt, and hence increasing their vulnerability to population declines and potentially local extinctions (Brook et al., 2008). Floods are a type of extreme weather events predicted to increase in frequency and intensity under global climate change (Hirabayashi et al., 2013; Seneviratne et al., 2012). In southwest China, analyses of long-term trends in flood incidences reveal both increased frequency and increased intensity over the past half century (Ji et al., 2015). In particular, areas encompassing the distribution of the giant panda are prone to floods generated by the interaction between episodes of intense precipitation, the topography of mountainous terrain, and the ongoing effects of previous deforestation (e.g., soil erosion, reduction in water retention, increase in superficial runoff) (He et al., 2012; Taylor et al., 1996). Floods can interrupt the normal activities of animal populations and trigger behavioural responses to the harsh environmental conditions being experienced (Alho and Silva, 2012). In the short term, species poorly adapted to flood disturbance and/or those with limited opportunities for dispersal can experience injuries from mechanical friction of debris flow or drowning (Wuczyński and Jakubiec, 2013). Declines in animal populations have also been associated with the interplay between flood-related habitat changes (e.g., loss of vegetation cover, soil erosion, slope steepness, debris accumulation) and exposure to anthropogenic stressors (Choudhury, 1998; Swanson et al., 1998). The natural history of giant panda indicates that avoidance of habitat disturbance may be facilitated through spatial behaviours that are associated with optimal foraging (Wei et al., 2015) and/or localization of den sites and water bodies (Zhang et al., 2011). This may allow individuals to avoid areas previously known to be unsuitable and relocate to non-disturbed areas (Hull et al., 2016; Meng et al., 2016; Tarou et al., 2004). Greater dispersal across continuous habitat and higher availability of food and shelters may have played a significant role in reducing the stress imposed on panda populations by large environmental disturbances such as bamboo die-offs, floods, earthquakes and wildfires over millennia (Hu, 2001; Johnson et al., 1988; Meng et al., 2016; Sun et al., 2016). However, the continuing effects of human-induced habitat degradation and fragmentation over the past century (Zhu et al., 2013) may mean that disturbance generated by extreme weather events alters panda habitat with greater magnitude. In particular, human activities in response to these events may consequently result in negative indirect impacts for wildlife and habitat. In order to help develop strategies to build resilience and support recovery from extreme weather events, information on patterns of exposure is warranted. Here, we use data on historical flood frequency, giant panda distribution and habitat use, nature reserve boundaries, and human density inside nature reserves to (i) examine spatial patterns of flood exposure; (ii) quantify the proportion of panda distribution subject to floods across nature reserves; and (iii) determine the correspondence between areas subject to high flood exposure densities, areas of high giant panda habitat use, and the areas exhibiting high human density. With this study, we advocate the need to better understand species responses to extreme weather events as well as to develop context-specific strategies to reduce deterioration of risk-prone areas
2. Materials and methods 2.1. Study area The study area consisted of the six mountainous regions (Qinling, Min, Qionglai, Greater Xiangling, Lesser Xiangling, and Liang) at the eastern edge of the Tibetan Plateau where the giant panda is currently distributed. The largest populations are currently found in Qinling, Min, and Qionglai whereas the ones with smaller size are distributed in Liang, Greater Xiangling, and Lesser Xiangling and face a higher risk of local extinction (Swaisgood et al., 2016). The characteristics of the region (e.g., elevation, precipitation regimes, slope steepness, soil structure, and vegetation cover) favour the occurrence of floods. Large areas comprising the giant panda habitat are also prone to earthquakes (Wen et al., 2008) which can generate flooding as a result of the collapse of loose materials caused by localized heavy precipitation (Cui et al., 2012). 2.2. Giant panda distribution data We obtained the giant panda geographic distribution from the IUCN Red List Assessment of Threatened Species version 2017.1. Following the coded domain values for species presence in the guidelines, we selected range polygons labelled as “extant” as they represent areas where the species is known or very likely to occur. Hence, by focusing on these areas (hereafter refer to as giant panda distribution) we aimed to avoid overestimating flood exposure while targeting areas most likely to be occupied by panda populations today and the recent past. 2.3. Flood data We extracted the frequency and geographic distribution of flood extents as GIS vector data from the Dartmouth Flood Observatory (Brakenridge, 2012). The observatory uses imagery gathered by satellite remote sensing sources, and weather serving reporting to measure and draw bounding polygons of areas affected by flood events from 1985 to the present (Brakenridge and Anderson, 2006; Reager and Famiglietti, 2009). Flood data after 2013 was not available for the study area. Therefore we extracted flood data for the period 1985–2013 with focus on the panda distribution. Moreover, to compare individual flood events, we estimated a flood magnitude index defined by the following formula: log10 (D x T x A) where D is the flood duration in days, T is the recurrence interval of flood severity in years, and A is the aereal extent affected by a given flood in km2. Because of the logarithmic basis of the scaling (resembling the Richter Scale for earthquakes), each whole number step in the index represents a tenfold increase in magnitude, so that floods reaching 7, 8 and 9 are associated with truly large magnitude events (Kundzewicz et al., 2014). The flood dataset included records that took place within the same geographic areas and hence may be influenced by similar environmental conditions (e.g., climate, topography) (Dormann et al., 2007). In this regard, to evaluate the existence of spatial autocorrelation in our flood dataset, we performed a mantel test using two distance matrices: one containing spatial distances and one containing distances between estimated flood magnitudes. We ran correlations with 9999 permutations using the mantel.rtest function in the R package ‘ade4’, version 1.7–8 (Dray and Dufour, 2007) in R (Team, 2017). No significant spatial autocorrelation was found (Mantel test, R = − 0.007; Permutation P value = 0.42); it was therefore unnecessary to correct for this type of spatial dependence. 2.4. Quantifying flood exposure Flood exposure was defined as any overlap between areas affected 36
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by individual flood events and the giant panda distribution. Thus using the intersect geoprocessing tool in ArcGIS (version 10.3) (ESRI, 2015) we superimposed the spatial extent of individual flood events one at the time with the giant panda distribution and examined flood frequency and levels of coverage. In a second stage we overlapped the giant panda distribution exposed to floods and the 67 nature reserves established for giant panda conservation. In doing so, we obtained the spatial boundary of nature reserves from the China's State Forestry Administration (SFA) (SFA, 2015a). We also generated a density map of flood exposure (number of individual flood incidences per grid of 1 × 1 km resolution) targeting areas that constitute the giant panda distribution, including the network of nature reserves. With this approach we aimed to identify areas more recurrently exposed to floods while having management support and access to government funding for development projects in place. Thus, flood risk management in these areas is most likely to be funded, implemented and monitored.
Heishuihe, Wawushan (all n = 17), and Longxi-hongkou, Maanshan and Wolong (all n = 16) (Fig. 3). 3.2. Giant panda habitat use and flood exposure Based in our analysis of giant panda presence sign data from the third national survey, 10% of areas with high habitat use overlapped areas in the upper quartile of the distribution of flood densities whereas 32% were found in areas in the lower quartile (Fig. 4a). High habitat use derived from the fourth national survey revealed a similar pattern: 15% of total area with high habitat use overlapped areas in the upper quartile of the distribution of flood densities, and 30% with areas in the lower quartile (Fig. 4b). 3.3. Human population density and flood exposure Examination of human density in the year 2010 in the panda distribution within nature reserves revealed that 40% and 19% of the areas with high human density were affected by flood densities in the upper and lower quartile of the distribution, respectively (Fig. 5). On the other hand, 23% and 44% of the areas with low human density overlapped with flood densities in the upper and lower quartile, respectively. A list with the nature reserves by mountain range that capture areas with high human density subject to high flood densities is available in Appendix S1.
2.5. Flood exposure and giant panda habitat use A high density of giant panda signs at a given location represents a high habitat use in that area (Hull et al., 2014; Zhao et al., 2017). In this regard, we used presence sign data from the Third and Fourth Giant Panda National Surveys in Sichuan Province (SFA, 2006; SFA, 2015b) to determine such areas. The protocols for the two surveys (conducted between May to July in 2000 and 2012, respectively) were the same: grids with size of 1.4 × 1.4 km were generated over the survey regions and giant panda signs (e.g., panda droppings, footmark, hair, forage traces) were surveyed along transects in each grid. We identified areas of high habitat use by conducting a kernel density analysis in ArcGIS (bandwidth h = 5000 m and 1000 m resolution) on all panda signs for each census. We defined areas of high habitat use as the densest 50% of the grids with a density > 0.001 panda signs/km2 and use them to perform an intersect analysis with the flood density dataset. In particular, we targeted overlaps with areas in the upper quartile of the distribution of flood exposure densities (hereafter refer to as flood densities) prior the third and fourth giant panda surveys in Sichuan Province (1985–1999 and 2001–2011, respectively).
4. Discussion Because different types of extreme events have been in operation throughout giant panda's existence (e.g., bamboo die-offs, floods, earthquakes, wildfires) (Johnson et al., 1988; Meng et al., 2016; Sun et al., 2016), it is expected that adaptation over thousands of years of evolution (e.g., microhabitat selection, spatial memory abilities, use of environmental cues to time phenological events) and greater quality, extent, and connectedness of habitats may have enabled giant pandas to adapt to their changing environment over millennia (Wei et al., 2015). However, unlike in past periods, the habitat of giant pandas is being confronted with rapidly evolving threats resulting from human impacts. In addition, flood frequency has increased in the region over the past half century (Ji et al., 2015) which may lead human communities to exploit with more intensity natural resources that are crucial for the giant panda. Our results show that high frequency of flooding in Qionglai, Liang, and Greater Xiangling mountains is affecting areas inside reserves with high human density rather than areas which pandas are using more intensively. It is important to note that reserves include non suitable habitat for the species that is currently occupied by people (e.g., mountain valleys and areas below the floodplain). In these rural areas people are highly dependent on local natural resources for their livelihoods (An et al., 2006). Thus, it is expected that resource use will intensify in order to mitigate or adapt to sudden episodes of extreme environmental disturbance (Turner et al., 2010). This demand in local natural resources may cause negative indirect impacts on wildlife and habitat. In a longitudinal study on the impact of different extreme events on rural livelihoods in Sichuan, Liu (2014) reported an annual increment in frequency and intensity of flooding events (period 1990–2013) after a magnitude 7.9 earthquake struck the region in 2008. Insufficient resources to cope with increasingly frequent flooding, led local people in Wolong National Nature Reserve to practice illegal logging and hunting to sustain their livelihoods (Liu, 2014). Wood harvesting to rebuild damaged properties can affect the forest that pandas depend on for shelter and denning, and provides suitable conditions for bamboo to grow which constitute 99% of the pandas' diet (Zhang et al., 2011). On the other hand, pandas can be unintentionally trapped in snares placed for hunting other wildlife (Swaisgood et al., 2016). In our study, we
2.6. Flood exposure and human density To examine how areas having a high human density overlap with flood densities across the panda distribution inside nature reserves, available population data was extracted from the Gridded Population of the World (GPW-v4) (CIESIN, 2017). The GPW uses population input data collected at the most detailed spatial resolution and applies the areal-weighting method to disaggregate census data (Doxsey-Whitfield et al., 2015). For China, the GWP used population input data from the “2010 county population census” using the township level data for gridding. Areas with high human density were defined as those with a number of persons per grid (1 km x ~1 km resolution) in the upper quartile of the distribution across the study area. Finally, we overlaid flood densities (period 1985–2010) on the human density map and investigated levels of coverage. 3. Results 3.1. Panda distribution and nature reserves exposed to floods Between 1985 and 2013 our analyses show that 18 floods, of a total 34, overlapped individually ≥25% of the giant panda distribution. 14 of these events had a magnitude ≥7 (Fig. 1). Of the 67 nature reserves, 30 contain areas of the giant panda distribution that were subject to 9–21 floods. In particular, flood exposure in 11 reserves (6 in Qinling, 3 in Min, 1 in Qionglai, and 1 in Liang mountains) was > 50% (Fig. 2). The greatest flood density affecting the panda distribution inside nature reserves was found in Laojunshan (n = 21), followed by Fengtongzhai, 37
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Fig. 1. Percentage of flood exposure, categorized by magnitude, across the distribution of the giant panda (Ailuropoda melanoleuca) (period 1985–2013). The magnitude index combines the recurrence interval of flood severity with the extent of each flood event and its duration. Each whole number step in the index represents a tenfold increase in magnitude reflected by the size of the circles.
natural resources for emergency mitigation. We found that high habitat use by giant pandas overlapped primarily with areas subject to low and moderate flood incidence. In this regard, availability of microrefugia (Zhang et al., 2011) and spatial behaviours associated with optimal foraging and localization of water, den sites or mates may help pandas to disperse to non-disturbed
identified 15 nature reserves containing areas where people confront a high flood exposure (Appendix S1). In these areas, we recommend the establishment of flood warning systems, risk assessments and risk reduction plans to increase preparedness and minimize loss of lives and livelihoods. Furthermore timely and adequate supply food and materials for reconstruction will be critical to help reduce the demand in
Fig. 2. Nature reserves grouped by mountain range that capture areas of the distribution of the giant panda (Ailuropoda melanoleuca) exposed to flood events (period 1985–2013). Graphs show the number of floods (bars) and the mean percentage coverage of the total nature reserve capturing areas of the panda distribution exposed to floods (dots). 38
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Fig. 3. Density map of flood exposure (period 1985–2013) across areas that constitute the distribution of the giant panda (Ailuropoda melanoleuca), including the network of 67 nature reserves established for giant panda conservation. Areas affected by individual flood events (blue colour scale) are represented using grids of 1 × 1 km resolution. The letters indicate 7 nature reserves exhibiting grids in the upper quartile of the distribution: a, Laojunshan; b, Fengtongzhai; c, Heishuihe; d, Wawushan; e, Longxi-hongkou; f, Maanshan; g, Wolong. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
as floods and observed patterns giant panda habitat use. Aware of these limitations, we believe our results can help inform about areas inside nature reserves where efforts addressing existing human pressures should integrate the risk assessment to natural disturbances such as floods, to maximize the benefit of conservation and management investments. Today, giant panda wild populations face different degree of isolation and habitat fragmentation resulting from ongoing human encroachment and the effects of past habitat loss. Floods are predicted to become more frequent across areas encompassing both giant panda populations and human communities which are also prone to earthsystem geohazards (Ji et al., 2015; Jung et al., 2015; Zhang et al., 2014). Our study is a first step towards monitoring context-specific habitat conditions and anticipating where further environmental disruptions may increase vulnerability. Building more resilient communities in areas susceptible to extreme weather events can help prevent additional pressures on pandas and their habitat, and hence keep improving the species' conservation status. Supplementary data to this article can be found online at https:// doi.org/10.1016/j.biocon.2019.01.019.
habitats (Gong et al., 2017; Zhang et al., 2014). Zheng et al. (2012) found that habitat affected by landslides after the Wenchuan earthquake in the Min mountain region had a significantly lower panda presence than habitats without landslides. Zheng et al. (2012) hypothesized that pandas reduced the use of areas affected by the earthquake because the region could provide suitable habitat at other locations. Following this rationale for flood-related habitat disturbance, conservation strategies in areas occupied by pandas should remain focus on mitigating habitat degradation and fragmentation caused by human activities (logging and wood harvesting, urbanization, livestock farming and ranching) that may also increase physical exposure to floods and hamper pandas' ability to reach alternative habitat patches (Zhang et al., 2011). Current conditions of panda habitat fragmentation and overutilization may be difficult to reverse quickly in non-protected landscapes. For the giant panda and other vulnerable species, the management and maintenance of nature reserves should be ensured to avoid an increment in human interferences that could amplify the effects of extreme weather events. There are some limitations in our study. We considered that areas recurrently exposed to floods across the panda distribution will experience environmental disturbance associated with changes at the local scale (e.g., removal of understory vegetation, fallen trees) that are known to affect panda population dynamics (Swaisgood et al., 2017). However, neither habitat changes nor continuous monitoring of population size variation over time were investigated. Moreover, certain human activities that are known to alter giant panda habitat use (e.g., fuelwood collection and wood harvesting, free-ranging livestock, urbanization) may influence both incidence of natural disturbances such
Acknowledgements We greatly appreciate the State Forestry Administration for the provision of giant panda presence sign data and nature reserve data. Our research was funded by the Chinese Academy of Sciences, China (Grant No. QYZDY-SSW-SMC019) and National Key Program of Research and Development, Ministry of Science and Technology of 39
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Fig. 4. Areas of high habitat use by giant panda (highlighted in red) and flood exposure density (blue colour scale) within its distribution in Sichuan province, China. Areas affected by individual flood events for the period 1985–1999 (panel a) and the period 2000–2011 (panel b) are represented using grids of 1 × 1 km resolution. High habitat use was estimated using presence sign data of the third and fourth national giant panda surveys. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.) 40
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Fig. 5. Percentage of human density, by quartile range, subject to different flood extents across the giant panda distribution inside nature reserves. Human density is an estimate of the number of persons per grid of 1 by 1 km resolution for the year 2010. Flood density indicates the number of individual flood extents per grid of 1 × 1 km resolution for the period 1985–2010. Percentages per each quartile do not add up to 100% due to rounding.
China (Grant No. 2016YFC0503200). EIA acknowledges funding from the Chinese Academy of Sciences President's International Fellowship Initiative (Grant No. 2018VBB0017).
He, H., Zhou, J., Peart, M.R., Chen, J., Zhang, Q., 2012. Sensitivity of hydrogeomorphological hazards in the Qinling Mountains, China. Quat. Int. 282, 37–47. Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., ... Kanae, S., 2013. Global flood risk under climate change. Nat. Clim. Chang. 3 (9), 816–821. Hu, J., 2001. Research on the Giant Panda. Shanghai Publishing House of Science and Technology, Shanghai. Hu, J., Wei, F., 2004. Comparative ecology of giant pandas in the five mountain ranges of their distribution in China. In: Giant Pandas: Biology and Conservation. University of California Press, Berkeley, CA, pp. 137–148. Hull, V., Roloff, G., Zhang, J., Liu, W., Zhou, S., Huang, J., ... Liu, J., 2014. A synthesis of giant panda habitat selection. Ursus 25 (2), 148–162. Hull, V., Zhang, J., Huang, J., Zhou, S., Vina, A., Shortridge, A., ... Ouyang, Z., 2016. Habitat use and selection by giant pandas. PLoS One 11 (9), e0162266. Hunt Jr., R.M., 2004. A paleontologist's perspective on the origin and relationships of the giant panda. In: Giant Pandas: Biology and Conservation. 308. Ji, Y., Zhou, G., Wang, S., Wang, L., 2015. Increase in flood and drought disasters during 1500–2000 in Southwest China. Nat. Hazards 77 (3), 1853–1861. Johnson, K., Schaller, G., Jinchu, H., 1988. Responses of Giant Pandas to a Bamboo Dieoff. National Geographic Research (USA). Jung, C.-Y., Shin, H.-J., Jang, C.J., Kim, H.-J., 2015. Projected change in east Asian summer monsoon by dynamic downscaling: moisture budget analysis. Asia-Pac. J. Atmos. Sci. 51 (1), 77–89. Kundzewicz, Z.W., Kanae, S., Seneviratne, S.I., Handmer, J., Nicholls, N., Peduzzi, P., ... Mach, K., 2014. Flood risk and climate change: global and regional perspectives. Hydrol. Sci. J. 59 (1), 1–28. Lescroël, A., Ballard, G., Grémillet, D., Authier, M., Ainley, D.G., 2014. Antarctic climate change: extreme events disrupt plastic phenotypic response in Adélie penguins. PLoS One 9 (1), e85291. Li, R., Xu, M., Wong, M.H.G., Qiu, S., Li, X., Ehrenfeld, D., Li, D., 2015. Climate change threatens giant panda protection in the 21st century. Biol. Conserv. 182, 93–101. Liu, W., 2014. Forensic analyses on a compound disaster and its impacts following the 2008 Wenchuan earthquake in China. In: EGU General Assembly Conference Abstracts. 16. Liu, J., Linderman, M., Ouyang, Z., An, L., Yang, J., Zhang, H., 2001. Ecological degradation in protected areas: the case of Wolong nature reserve for giant pandas. Science 292 (5514), 98–101. Liu, G., Guan, T., Dai, Q., Li, H., Gong, M., 2016. Impacts of temperature on giant panda habitat in the north Minshan Mountains. Ecol. Evol. 6 (4), 987–996. Meng, Q.-K., Miao, F., Zhen, J., Huang, Y., Wang, X.-Y., Peng, Y., 2016. Impact of earthquake-induced landslide on the habitat suitability of giant panda in Wolong, China. J. Mt. Sci. 13 (10), 1789–1805. Pavelka, M.S., Chapman, C.A., 2006. Population structure of black howlers (Alouatta pigra) in southern Belize and responses to hurricane iris. In: New Perspectives in the Study of Mesoamerican Primates, pp. 143–163. Reager, J., Famiglietti, J., 2009. Global terrestrial water storage capacity and flood potential using GRACE. Geophys. Res. Lett. 36 (23). Seneviratne, S.I., Nicholls, N., Easterling, D., Goodess, C.M., Kanae, S., Kossin, J., ... Rahimi, M., 2012. Changes in Climate Extremes and their Impacts on the Natural Physical Environment. State Forestry Administration, P.R.C, 2006. The 3rd National Survey Report on Giant Panda in China. China Science Press, Beijing, China (in Chinese). State Forestry Administration, P.R.C, 2015a. Department of Wildlife Conservation and Nature Reserve Management. Beijing, China. State Forestry Administration, P.R.C, 2015b. The 4th National Survey Report on Giant Panda in China. China Science Press, Beijing, China (in Chinese). Sun, W., Zhang, E., Shen, J., Chen, R., Liu, E., 2016. Black carbon record of the wildfire history of western Sichuan Province in China over the last 12.8 ka. Front. Earth Sci. 10 (4), 634–643. Swaisgood, R., Wang, D., Wei, F., 2016. Ailuropoda Melanoleuca: the IUCN red List of Threatened Species. International Union for Conservation of Nature e.
Author's contributions E.I.A., and F.W. conceived and designed the study; Y.N., Q.D., and X.G. made substantial data contributions; E.I.A.J. and F.W. led the analysis; all authors contributed greatly to the discussion of results; E.I.A.J. wrote the first draft of this manuscript and all authors contributed to the final version. Data accessibility statement All input information used in this study are either available through the references provided in the text or upon request from the corresponding author. References Alho, C.J., Silva, J.S., 2012. Effects of severe floods and droughts on wildlife of the Pantanal wetland (Brazil)—a review. Animals 2 (4), 591–610. An, L., He, G., Liang, Z., Liu, J., 2006. Impacts of demographic and socioeconomic factors on spatio-temporal dynamics of panda habitat. Biodivers. Conserv. 15 (8), 2343–2363. Brakenridge, G., 2012. Global Active Archive of Large Flood Events. University of Colorado, Dartmouth Flood Observatory. http://floodobservatory.colorado.edu/, Accessed date: August 2017. Brakenridge, R., Anderson, E., 2006. MODIS-based flood detection, mapping and measurement: the potential for operational hydrological applications. In: Transboundary Floods: Reducing Risks through Flood Management, pp. 1–12. Brook, B.W., Sodhi, N.S., Bradshaw, C.J., 2008. Synergies among extinction drivers under global change. Trends Ecol. Evol. 23 (8), 453–460. Center for International Earth Science Information Network - CIESIN - Columbia University, 2017. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 10. NASA Socioeconomic Data and Applications Center (SEDAC), Palisades, NY. https://doi.org/10.7927/H4DZ068D. (Accessed October 2018). Choudhury, A., 1998. Flood havoc in Kaziranga. Pachydenn 26, 83–87. Cui, P., Lin, Y.-M., Chen, C., 2012. Destruction of vegetation due to geo-hazards and its environmental impacts in the Wenchuan earthquake areas. Ecol. Eng. 44, 61–69. Dormann, C.F., McPherson, J.M., Araújo, M.B., Bivand, R., Bolliger, J., Carl, G., Davies, R.G., Hirzel, A., Jetz, W., Kissling, W.D., Kühn, I., 2007. Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30 (5), 609–628. Doxsey-Whitfield, E., MacManus, K., Adamo, S.B., Pistolesi, L., Squires, J., Borkovska, O., Baptista, S.R., 2015. Taking advantage of the improved availability of census data: a first look at the gridded population of the world, version 4. Pap. Appl. Geog. 1 (3), 226–234. Dray, S., Dufour, A.-B., 2007. The ade4 package: implementing the duality diagram for ecologists. J. Stat. Softw. 22 (4), 1–20. ESRI, R., 2015. ArcGIS Desktop: Release 10.3 Environmental Systems Research Institute, CA. Gong, M., Fan, Z., Zhang, X., Liu, G., Wen, W., Zhang, L., 2017. Measuring the effectiveness of protected area management by comparing habitat utilization and threat dynamics. Biol. Conserv. 210, 253–260.
41
Biological Conservation 232 (2019) 35–42
E.I. Ameca et al.
Rousseaux, C.S., 2013. An extreme climatic event alters marine ecosystem structure in a global biodiversity hotspot. Nat. Clim. Chang. 3 (1), 78–82. Wuczyński, A., Jakubiec, Z., 2013. Mortality of game mammals caused by an extreme flooding event in south-western Poland. Nat. Hazards 69 (1), 85–97. Xu, W., Viña, A., Kong, L., Pimm, S.L., Zhang, J., Yang, W., ... Liu, J., 2017. Reassessing the conservation status of the giant panda using remote sensing. Nat. Ecol. Evol. 1 (11), 1635. Zhang, Z., Swaisgood, R.R., Zhang, S., Nordstrom, L.A., Wang, H., Gu, X., ... Wei, F., 2011. Old-growth forest is what giant pandas really need. Biol. Lett. 7 (3) rsbl20101081. Zhang, Z., Yuan, S., Qi, D., Zhang, M., 2014. The Lushan earthquake and the giant panda: impacts and conservation. Integrative Zool. 9 (3), 376–378. Zhang, Q., Hong, Y., Zou, F., Zhang, M., Lee, T.M., Song, X., Rao, J., 2016. Avian responses to an extreme ice storm are determined by a combination of functional traits, behavioural adaptations and habitat modifications. Sci. Rep. 6, 22344. Zhao, C., Yue, B., Ran, J., Moermond, T., Hou, N., Yang, X., Gu, X., 2017. Relationship between human disturbance and endangered giant panda Ailuropoda melanoleuca habitat use in the Daxiangling Mountains. Oryx 51 (1), 146–152. Zheng, W., Xu, Y., Liao, L., Yang, X., Gu, X., Shang, T., Ran, J., 2012. Effect of the Wenchuan earthquake on habitat use patterns of the giant panda in the Minshan Mountains, southwestern China. Biol. Conserv. 145 (1), 241–245. Zhu, L., Hu, Y., Qi, D., Wu, H., Zhan, X., Zhang, Z., ... Gu, X., 2013. Genetic consequences of historical anthropogenic and ecological events on giant pandas. Ecology 94 (10), 2346–2357.
T712A45033386. http://www.iucnredlist.org/details/712/0. Swaisgood, R.R., Wang, D., Wei, F., 2017. Panda downlisted but not out of the woods. Conserv. Lett. 11 (1), e12355. Swanson, F.J., Johnson, S.L., Gregory, S.V., Acker, S.A., 1998. Flood disturbance in a forested mountain landscape. Bioscience 48 (9), 681–689. Tarou, L., Snyder, R., Maple, T., 2004. Spatial memory in the giant panda. In: Lindburg, D., Baragona, K. (Eds.), Giant Pandas: Biology and Conservation, pp. 101–105. Taylor, A.H., Zisheng, Q., Jie, L., 1996. Structure and dynamics of subalpine forests in the Wang Lang Natural Reserve, Sichuan, China. Vegetatio 124 (1), 25–38. Team, R.C., 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2016). Turner, W.R., Bradley, B.A., Estes, L.D., Hole, D.G., Oppenheimer, M., Wilcove, D.S., 2010. Climate change: helping nature survive the human response. Conserv. Lett. 3 (5), 304–312. Viña, A., Tuanmu, M.-N., Xu, W., Li, Y., Ouyang, Z., DeFries, R., Liu, J., 2010. Range-wide analysis of wildlife habitat: implications for conservation. Biol. Conserv. 143 (9), 1960–1969. Wei, F., Swaisgood, R., Hu, Y., Nie, Y., Yan, L., Zhang, Z., ... Zhu, L., 2015. Progress in the ecology and conservation of giant pandas. Conserv. Biol. 29 (6), 1497–1507. Wen, X.-Z., Ma, S.-L., Xu, X.-W., He, Y.-N., 2008. Historical pattern and behavior of earthquake ruptures along the eastern boundary of the Sichuan-Yunnan faultedblock, southwestern China. Phys. Earth Planet. Inter. 168 (1), 16–36. Wernberg, T., Smale, D.A., Tuya, F., Thomsen, M.S., Langlois, T.J., De Bettignies, T., ...
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