Maxent modeling for predicting the spatial distribution of three raptors in the Sanjiangyuan National Park, China

Zhang, Jingjie; Jiang, Feng; Li, Guangying; Qin, Wen; Li, Shengqing; Gao, Hongmei; Cai, Zhenyuan; Lin, Gonghua; Zhang, Tongzuo

doi:10.1002/ece3.5243

Cited by 113 publications

(96 citation statements)

References 74 publications

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“…We chose Maxent due to its popularity in the literature 30 , its ease of use, and its ability to model complex relationships among covariates lacking in other methods 83,84 . Additionally, Maxent performs consistently well across ecosystems, species, and scales 32,80,[85][86][87][88][89][90] . Maxent models attempt to approximate the probability of a species presence, conditioned on environmental variables, using presence-only data supplemented with model-generated background locations 83 .…”

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confidence: 83%

Multiscale consensus habitat modeling for landscape level conservation prioritization

Poor

Scheick

Mullinax

2020

Sci Rep

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Globally, wide-ranging carnivore populations are imperiled due to human-caused habitat fragmentation. Where populations are fragmented, habitat quantification is often the first step in conservation. Presence-only species distribution models can provide robust results when proper scales and data are considered. We aimed to identify habitat for a fragmented carnivore population at two scales and aid conservation prioritization by identifying potential future habitat fragmentation. We used location data and environmental variables to develop a consensus model using Maxent and Mahalanobis distance to identify black bear (Ursus americanus floridanus) habitat across Florida, USA. We compared areas of habitat to areas of predicted sea level rise, development, and protected areas. Local-scale models performed better than state-scale models. We identified 23,798 km2 of habitat at the local-scale and 45,703 km2 at the state-scale. Approximately 10% of state- and 14% of local-scale habitat may be inundated by 2100, 16% of state- and 7% of local-scale habitat may be developed, and 54% of state- and 15% of local-scale habitat is unprotected. Results suggest habitat is at risk of fragmentation. Lack of focused conservation and connectivity among bear subpopulations could further fragmentation, and ultimately threaten population stability as seen in other fragmented carnivore populations globally.

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confidence: 83%

Multiscale consensus habitat modeling for landscape level conservation prioritization

Poor

Scheick

Mullinax

2020

Sci Rep

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“…Sanjiangyuan, or Source of Three Rivers, refers to the area's role as the headwaters of China's three largest rivers (Yangtze river, Yellow river and Lancang river). The region has global influence and dictates China's ecosystem (Zhang, Jiang, et al, 2019). A variety F I G U R E 1 Tibetan brown bear (Ursus arctos pruinosus) captured by camera trapping in the Yangtze River Zone of Sanjiangyuan National Park, China of endemic alpine flora and fauna constitutes Sanjiangyuan's excessive biological diversity.…”

Section: Study Areamentioning

confidence: 99%

“…A variety F I G U R E 1 Tibetan brown bear (Ursus arctos pruinosus) captured by camera trapping in the Yangtze River Zone of Sanjiangyuan National Park, China of endemic alpine flora and fauna constitutes Sanjiangyuan's excessive biological diversity. As China's first national park, it has become an exhibition area of nature protection and ecological culture heritage on the Tibetan Plateau (Zhang, Jiang, et al, 2019). Unfortunately, it is also one of the most sensitive regions to climate change Wang, Song, & Hu, 2010).…”

Section: Study Areamentioning

confidence: 99%

“…SDMs have become essential for approaching research challenges in fields such as biogeography, evolution, ecology, and conservation biology (Guisan & Thuiller, ). At present, researchers have used the maximum entropy (MaxEnt) model to assess the habitat suitability for a variety of rare or endangered wildlife around the world (Bai et al, ; Li, Liu, Xue, Zhang, & Li, ; Li et al, ; Zhang, Jiang, et al, ; Zhang, Clauzel, et al, ). Furthermore, the Circuit model is commonly used by natural resource managers to predict wildlife dispersal paths and design ecological corridors, which are often used in wildlife management and conservation practice (Li et al, ; McRae & Beier, ; McRae, Shah, & Mohapatra, ; Walpole, Bowman, Murray, & Wilson, ; Zhang, Clauzel, et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…model to assess the habitat suitability for a variety of rare or endangered wildlife around the world Li, Liu, Xue, Zhang, & Li, 2017;Li et al, 2019;Zhang, Jiang, et al, 2019;Zhang, Clauzel, et al, 2019). Furthermore, the Circuit model is commonly used by natural resource managers to predict wildlife dispersal paths and design ecological corridors, which are often used in wildlife management and conservation practice McRae & Beier, 2007;McRae, Shah, & Mohapatra, 2013;Walpole, Bowman, Murray, & Wilson, 2012;Zhang, Clauzel, et al, 2019).…”

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Identifying climate refugia and its potential impact on Tibetan brown bear (Ursus arctos pruinosus) in Sanjiangyuan National Park, China

Dai

Hacker

Zhang

et al. 2019

Ecology and Evolution

Self Cite

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Climate change has direct impacts on wildlife and future biodiversity protection efforts. Vulnerability assessment and habitat connectivity analyses are necessary for drafting effective conservation strategies for threatened species such as the Tibetan brown bear (Ursus arctos pruinosus). We used the maximum entropy (MaxEnt) model to assess the current (1950–2000) and future (2041–2060) habitat suitability by combining bioclimatic and environmental variables, and identified potential climate refugia for Tibetan brown bears in Sanjiangyuan National Park, China. Next, we selected Circuit model to simulate potential migration paths based on current and future climatically suitable habitat. Results indicate a total area of potential suitable habitat under the current climate scenario of approximately 31,649.46 km2, of which 28,778.29 km2 would be unsuitable by the 2050s. Potentially suitable habitat under the future climate scenario was projected to cover an area of 23,738.6 km2. Climate refugia occupied 2,871.17 km2, primarily in the midwestern and northeastern regions of Yangtze River Zone, as well as the northern region of Yellow River Zone. The altitude of climate refugia ranged from 4,307 to 5,524 m, with 52.93% lying at altitudes between 4,300 and 4,600 m. Refugia were mainly distributed on bare rock, alpine steppe, and alpine meadow. Corridors linking areas of potentially suitable brown bear habitat and a substantial portion of paths with low‐resistance value were distributed in climate refugia. We recommend various actions to ameliorate the impact of climate change on brown bears, such as protecting climatically suitable habitat, establishing habitat corridors, restructuring conservation areas, and strengthening monitoring efforts.

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Seasonal niche and spatial distribution modelling of the loggerhead (Caretta caretta) in the Adriatic and Ionian seas

Zampollo

Arcangeli

Costantino

et al. 2022

Aquatic Conservation

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Although Caretta caretta is the most common turtle in the eastern Mediterranean Sea, its distribution is relatively difficult to assess due to the lack of data in offshore waters. The ecological niches in the central‐southern Adriatic Sea and the north‐eastern Ionian Sea were investigated for loggerhead turtles with body sizes ≥20 cm during two periods: winter (W), which includes months from October to March, and breeding–nesting (BN), from April to September. Differences in the spatial distribution of loggerheads were explored between periods, and the environmental predictors driving habitat selection were examined. Loggerheads mainly select habitats within the continental shelf (<200 m) differently, and exhibit: (i) a generalist behaviour in the Adriatic subregion during BN and a specific selection of environmental conditions during W; and (ii) a specialist behaviour in the Ionian Sea. The Adriatic niche appeared as an important foraging ground year‐round, whereas the distribution of suitable areas in the Ionian Sea is patchy and limited to specific habitats close to shore. Extreme values of sea surface temperatures (SSTs) from October to March (W) influenced loggerhead distribution differently in the subregions: areas experiencing cold and warm SST peaks were selected, respectively, in the Adriatic and Ionian subregions. Moreover, SST in April largely contributed to defining the habitat suitability in BN for both subregions This study further confirms the importance of the Adriatic and Ionian seas for the conservation of loggerhead sea turtles and the need for seasonal investigations to establish effective conservation measures. Here, the use of a cost‐effective method (fixed line transects from ferries) to inform the distribution of sea turtles in offshore waters is a valuable methodology for long‐term seasonal investigations of the habitat use of sea turtles.

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Maxent modeling for predicting the spatial distribution of three raptors in the Sanjiangyuan National Park, China

Cited by 113 publications

References 74 publications

Multiscale consensus habitat modeling for landscape level conservation prioritization

Multiscale consensus habitat modeling for landscape level conservation prioritization

Identifying climate refugia and its potential impact on Tibetan brown bear (Ursus arctos pruinosus) in Sanjiangyuan National Park, China

Seasonal niche and spatial distribution modelling of the loggerhead (Caretta caretta) in the Adriatic and Ionian seas

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