Study of Vulture Habitat Suitability and Impact of Climate Change in Central India Using MaxEnt

Journal of Resources and Ecology, Journal Year: 2021, Volume and Issue: 12(1)

Published: Jan. 22, 2021

Vultures provide invaluable ecosystem services and play an important role in balancing. The number of native vultures India has declined the past. Acquiring present knowledge their habitat spread is essential to manage prevent such a decline. It envisaged that ongoing climate crisis may further cause change suitability impact existing population. Therefore, this study Central India—a vulture stronghold, aimed at predicting changes short long term data statistically graphically by using Species Distribution Model. MaxEnt software was chosen for its advantages over other models, like presence-only performing well with incomplete data, small sample sizes gaps, etc. Global Climate Model ensemble (CCSM4, HadGEM2AO MIROC5), used get better prediction. Fourteen robust models (AUC 0.864–0.892) were developed from 1000 locations seven species two seasons together. Selected climatic environmental variables predict current habitat. Future prediction based on only. most influencing distribution precipitation (bio 15, bio 18, 19) temperature 3, 5). Forest water bodies major influencers within land use-landcover At finer scale, while extremely suitable area decreased highly increased time, total marginally 2050 but 2070. For broader consideration, net loss 5% 7.17% 2070 (RCP4.5). Similarly, RCP8.5 6% 7.3% generated can be conservation planning management thus protecting any future threat.

Language: Английский

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Multi-scale habitat selection modeling using combinatorial optimization of environmental covariates: A case study on nature reserve of red-crowned cranes

Ecological Indicators, Journal Year: 2023, Volume and Issue: 154, P. 110488 - 110488

Published: June 15, 2023

Multi-scale habitat selection modeling (HSM) has garnered attention due to its ability incorporate scale dependence of species. The key multi-scale HSM is select the appropriate combination scales for different resources or environmental conditions, and then construct a set covariates as features HSM. However, existing methods do not determine under unified model. In this study, combinatorial optimization approach proposed. We regard search space, use heuristic algorithm best-fitting model optimal each resource condition. case study conducted in Yancheng National Nature Reserve, proposed applied endangered red-crowned crane. compare method with single-scale, random-scale other approaches. results show that selected based on obtained best accuracy spatial prediction suitability test AUC 0.865 daytime scenario 0.932 nighttime scenario. Moreover, are utilized generate response curves, providing suggestions restoration management crane population nature reserve.

Language: Английский

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The role of remote sensing in species distribution models: a review

International Journal of Remote Sensing, Journal Year: 2024, Volume and Issue: 46(2), P. 661 - 685

Published: Nov. 4, 2024

Species distribution models (SDMs) are invaluable for delineating ecological niches and assessing habitat suitability, facilitating the projection of species distributions across spatial temporal dimensions. This capability is crucial conservation planning, management understanding impacts climate change. Remote sensing has emerged as a superior alternative to traditional field surveys in developing SDMs, offering cost-effective, repetitive data collection over comprehensive scales. Despite rapid advancements remote technologies analytical methods, specific contributions SDMs historically, potential pathways its integration with remain ambiguous. Therefore, our study set forth two objectives: firstly, conduct thorough review sensing's role focusing on environmental predictors, response variables, scalability validation; secondly, outline prospective research trajectories within SDMs. Our findings reveal that offers plethora predictors encompassing climate, topography, land cover use, spectral metrics biogeochemical cycles. A variety techniques, including random forest, deep learning linear unmixing, facilitate derivation SDM variables development diverse Furthermore, enables validation through mapping outputs.

Language: Английский

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Potential Range Shift of Snow Leopard in Future Climate Change Scenarios

Sustainability, Journal Year: 2022, Volume and Issue: 14(3), P. 1115 - 1115

Published: Jan. 19, 2022

The snow leopard (Panthera uncia) lives in alpine ecosystems Central Asia, where it could face intensive climate change and is thus a major conservation concern. We compiled dataset of 406 GPS-located occurrences based on field surveys, literature, the GBIF database. used Random Forest to build different species distribution models with maximum 27 explanatory variables, including climatic, topographical, human impact predict potential for make projections. estimated range shifts under two global representative concentration pathways 2050 2070. found center may move northwest by about 200 km upward elevation 100 m Unlike previous studies leopard, we highlighted that rather than northward are main changing climate, since landform their habitat allows an shift, whereas mountains valleys would block movement. Conservation should therefore prioritize protecting its current over making movement corridors.

Language: Английский

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Using machine learning to predict habitat suitability of sloth bears at multiple spatial scales

Ecological Processes, Journal Year: 2021, Volume and Issue: 10(1)

Published: June 29, 2021

Abstract Background Habitat resources occur across the range of spatial scales in environment. The environmental are characterized by upper and lower limits, which define organisms’ distribution their communities. Animals respond to these at optimal scale. Therefore, multi-scale assessments critical identifying correct scale habitat most influential determining species-habitat relationships. This study used a machine learning algorithm random forest (RF), evaluate scale-dependent selection sloth bears ( Melursus ursinus ) around Bandhavgarh Tiger Reserve, Madhya Pradesh, India. Results We 155 spatially rarified occurrences out 248 occurrence records obtained from camera trap captures n = 36) scats located 212) field. calculated focal statistics for 13 variables ten surrounding each presence-absence record bears. Large (> 5000 m) small (1000–2000 were dominant perceived features. Among covariates, farmlands degraded forests essential patches associated with bear occurrences, followed sal dry deciduous forests. final suitability model was highly accurate had very low out-of-bag (OOB) error rate. high accuracy rate also using alternate validation matrices. Conclusions Human-dominated landscapes expanding human populations, changing land-use patterns, increasing fragmentation. Farmland habitats constitute ~ 40% landform buffer zone reserve. One management implications may be suitable human-modified integrating them existing conservation landscapes.

Language: Английский

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Anthropogenic pressures increase extinction risk of an isolated Asian elephant (Elephas maximus) population in southwestern China, as revealed by a combination of molecular‐ and landscape‐scale approaches

Integrative Zoology, Journal Year: 2021, Volume and Issue: 17(6), P. 1078 - 1094

Published: March 17, 2021

Identification of the effect anthropogenic threats on ecosystem is crucial. We used molecular tools and remote sensing to evaluate population status an isolated Asian elephant in southwestern China response changes habitat suitability between 1989 2019. A total 22 unique genotypes were identified from 117 dung samples collected March June 2018 using microsatellite DNA analysis, including 13 males 9 females. Based size fecal boli, 1 animal was a juvenile, subadults, 12 adults, indicating that recruitment limited. The effective small (15.3) but there no signature recent bottleneck. observed low genetic diversity (He = 0.46 ± 0.05) high level inbreeding (Fis 0.43 0.11), suggesting viability risk extinction. In total, these elephants lost nearly two thirds (62%) their 3 decades. expansion agriculture rubber plantations followed by increase human settlements after increased isolation this population. recommend resettlement 800 inhabitants 2 villages abandonment associated farmland would make additional 20 km2 suitable available. This could allow 14 elephants, possibly translocating individuals elsewhere China. Our findings can be applied management conservation other fragmented populations or range countries elephants.

Language: Английский

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Genetic diversity and spatial structures of snow leopards (Panthera uncia) reveal proxies of connectivity across Mongolia and northwestern China

Landscape Ecology, Journal Year: 2022, Volume and Issue: 38(4), P. 1013 - 1031

Published: Dec. 24, 2022

Language: Английский

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Multiscale habitat suitability modeling for a threatened raptor offers insight into ecological model transferability

Ecological Modelling, Journal Year: 2024, Volume and Issue: 496, P. 110845 - 110845

Published: Aug. 27, 2024

Language: Английский

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Simulating multi-scale optimization and variable selection in species distribution modeling

Ecological Informatics, Journal Year: 2024, Volume and Issue: unknown, P. 102832 - 102832

Published: Sept. 1, 2024

Language: Английский

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Seeing the Big‐ to Fine‐Grained Picture: Exploring the Baseline Status of Mammal Occupancy Across Myanmar Using Scale‐Optimised Modelling

Diversity and Distributions, Journal Year: 2024, Volume and Issue: 30(11)

Published: Oct. 15, 2024

ABSTRACT Aim Myanmar, an Indo‐Burmese biodiversity hotspot, lacks baseline data on species occurrence and distribution. This hinders monitoring optimisation of conservation development plans. We aim to document mammal occupancy, interactions with environmental factors scale‐dependent responses. Location Hkakaborazi National Park, Htamanthi Wildlife Sanctuary, Alaungdaw Kathapa Rakhine Yoma Elephant Range, Say Taung Myinmoletkhat Key Biodiversity Areas distributed across Myanmar. Methods Camera trap throughout Myanmar were used analyse occupancy. conducted a multiscale hierarchical spatial modelling process, using local pooled also optimised scale five scales six predictors, univariate occupancy models. then selected scale‐optimised variables for multivariate modelling, repeating this process each local, regional national datasets. Results The study identified 47 terrestrial observed strong nonstationarity in estimates. Relationships differed among highly dependent. Importantly, estimates produced by pooling sites greatly different from any the individual sites, suggesting that high heterogeneity abundance requires or nested account variation. Main Conclusions Future efforts should focus Northern if range‐restricted rare are be protected, while still given common which serve as potential indicators overall community structure. results datasets underscores misleading interpretations aggregated nonstationary ecological systems. Metareplicated analyses geographically ecologically proximal provide important view variation patterns guiding design improving understanding drivers change large regions, such Southeast Asia.

Language: Английский

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