Identifying priority core habitats and corridors for effective conservation of brown bears in Iran

Scientific Reports, Journal Year: 2021, Volume and Issue: 11(1)

Published: Jan. 13, 2021

Abstract Iran lies at the southernmost range limit of brown bears globally. Therefore, understanding habitat associations and patterns population connectivity for in is relevant species’ conservation. We applied species distribution modeling to predict suitability identify core areas corridors. Our results showed that forest density, topographical roughness, NDVI human footprint were most influential variables predicting bear distribution. The crucial corridor networks are concentrated Alborz Zagros Mountains. These two predicted be fragmented into a total fifteen isolated patches if dispersal across landscape limited 50,000 cost units, aggregates capable dispersing 400,000 units. found low overlap between corridors, habitats with protected areas, suggesting existing area network may not adequate conservation Iran. suggest effective requires protection both corridors them, especially outside Iran’s areas.

Language: Английский

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Potential Impacts of Climate Change on the Habitat Suitability of the Dominant Tree Species in Greece

Plants, Journal Year: 2022, Volume and Issue: 11(12), P. 1616 - 1616

Published: June 20, 2022

Climate change is affecting species distribution and ecosystem form function. Forests provide a range of services, understanding their vulnerability to climate important for designing effective adaptation strategies. Species Distribution Modelling (SDM) has been extensively used derive habitat suitability maps under current conditions project shifts change. In this study, we model the future dominant tree in Greece (Abies cephalonica, Abies borisii-regis, Pinus brutia, halepensis, nigra, Quercus ilex, pubescens, frainetto Fagus sylvatica), based on species-specific presence data from EU-Forest database, enhanced with that currently under-represented terms occurrence points. By including these additional data, areas relatively drier some study were included SDM development, yielding potentially lower conditions. SDMs developed each taxon using soil at resolution ~1 km2. Model performance was assessed found adequately simulate potential distributions. Subsequently, models SSP1-2.6 SSP5-8.5 scenarios 2041-2070 2071-2100 time periods. Under scenarios, reduction habitat-suitable predicted most species, higher elevation taxa experiencing more pronounced shrinkages. An exception endemic A. cephalonica its sister which, although mid high elevations, seem able maintain scenarios. Our findings suggest could significantly affect dynamics forest ecosystems Greece, ecological, economic social implications, thus adequate mitigation measures should be implemented.

Language: Английский

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Comparing the performance of global, geographically weighted and ecologically weighted species distribution models for Scottish wildcats using GLM and Random Forest predictive modeling

Ecological Modelling, Journal Year: 2024, Volume and Issue: 492, P. 110691 - 110691

Published: April 8, 2024

Species distribution modeling has emerged as a foundational method to predict occurrence and suitability of species in relation environmental variables advance ecological understanding guide conservation planning. Recent research, however, shown that species-environmental relationships habitat model predictions are often nonstationary space, time context. This calls into question approaches assume global, stationary realized niche use predictive describe it. paper explores this issue by comparing the performance models for wildcat hybrid based on (1) global pooled data across individuals, (2) geographically weighted aggregation individual models, (3) ecologically (4) combinations geographical weighting. Our study system included GPS telemetry from 14 hybrids Scotland. We developed both using Generalized Linear Models (GLM) Random Forest machine learning compare these differing algorithms how they analyses. validated predicted four different ways. First, we used independent hold-out collared hybrids. Second, 8 additional previous were not training sample. Third, sightings sent public researchers expert opinion. Fourth, collected camera trap surveys between 2012 – 2021 various sources produce combined dataset showing where wildcats had been detected. results show validation individuals train provides highly biased assessment true other locations, with particular appearing perform exceptionally (and inaccurately) well when same models. Very obtained three sources. Each sets gave result terms best overall model. The average datasets suggested produced potential was an ensemble Model GLM suggests debate over whether which vs is superior or aggregated may be false choice. presented here prediction applies combination all framework.

Language: Английский

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Background sampling for multi-scale ensemble habitat selection modeling: Does the number of points matter?

Ecological Informatics, Journal Year: 2022, Volume and Issue: 72, P. 101914 - 101914

Published: Nov. 13, 2022

Ensemble habitat selection modeling is becoming a popular approach among ecologists to answer different questions. Since we are still in the early stages of development and application ensemble modeling, there remain many questions regarding performance parameterization. One important gap, which this paper addresses, how number background points used train models influences model. We an empirical presence-only dataset three selections scale-optimized using six algorithms (GLM, GAM, MARS, ANN, Random Forest, MaxEnt). tested four combinations component models: (a) equal numbers presences, (b) equaled ten times (c) 10,000 points, (d) optimized for each Among regression-based approaches, MARS performed best when built with points. machine learning models, RF presences AUC indicated that performing model was including while TSS increased as increased. found trained optimal outperformed ensembles same although differences were slight. When single method, can perform better than model, but fluctuates not properly selected. On other hand, provides consistently high accuracy regardless point sampling approach. Further, optimizing within provide improvement. suggest evaluating more across multiple species investigate might affect scenarios.

Language: Английский

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Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids

Ecological Informatics, Journal Year: 2023, Volume and Issue: 75, P. 102026 - 102026

Published: Feb. 18, 2023

Species Distribution Models (SDMs) are a powerful tool to derive habitat suitability predictions relating species occurrence data with features. Two of the most frequently applied algorithms model species-habitat relationships Generalised Linear (GLM) and Random Forest (RF). The former is parametric regression providing functional models direct interpretability. latter machine learning non-parametric algorithm, more tolerant than other approaches in its assumptions, which has often been shown outperform algorithms. Other have developed produce robust SDMs, like training bootstrapping spatial scale optimisation. Using felid presence-absence from three study regions Southeast Asia (mainland, Borneo Sumatra), we tested performances SDMs by implementing four modelling frameworks: GLM RF bootstrapped non-bootstrapped data. With Mantel ANOVA tests explored how combinations influenced their predictive performances. Additionally, scale-optimisation responded species' size, taxonomic associations (species genus), area algorithm. We found that choice algorithm had strong effect determining differences between SDMs' predictions, while no effect. followed species, were main factors driving scales identified. trained showed higher performance, however, revealed significant only explaining variance observed sensitivity specificity and, when interacting bootstrapping, Percent Correctly Classified (PCC). Bootstrapping significantly explained specificity, PCC True Skills Statistics (TSS). Our results suggest there systematic identified produced vs. RF, but neither approach was consistently better other. divergent inconsistent abilities analysts should not assume inherently superior test multiple methods. implications for SDM development, revealing inconsistencies introduced on optimisation, selecting broader RF.

Language: Английский

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Big cats persisting in human-dominated landscape: Habitat suitability and connectivity of leopards in central North China

Landscape Ecology, Journal Year: 2024, Volume and Issue: 39(5)

Published: April 23, 2024

Abstract Context The leopard ( Panthera pardus ), the only large carnivore species occurring in central North China, has undergone substantial range contraction and population decline due to anthropogenic pressure across region. Objectives In this study, we aimed map its current suitable habitats assess degree of connectivity between core inform future conservation planning big cat at landscape scale. Methods We conducted study China (34°11´ ~ 43°49´N, 103°11´ 123°54´E, about 936,000 km 2 ). collected occurrence locations (N = 196) leopards from 2014–2020, modeled habitat suitability using an “ensemble” distribution model by incorporating environmental variables. then identified potential dispersal corridors patches (≥ 100 ) through analysis. Results preferred humid forests higher elevations with less human disturbance. Their were highly fragmented, main located Shanxi, Shaanxi, border Gansu Ningxia provinces. Among all 8,679 habitats, 14 (139–1,084 , mean 495.21 a total area 6,933 among which 25.26% (1,751 are covered nature reserves 11 confirmed occurrence. also 8 least-cost pathways these average length 57.22 km. Conclusions Our results revealed that, persisting fragmented fragile habitats. remaining should be considered managed as regional meta-population for their long-term persistence human-dominated landscape.

Language: Английский

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Meta‐replication, sampling bias, and multi‐scale model selection: A case study on snow leopard (Panthera uncia) in western China

Ecology and Evolution, Journal Year: 2020, Volume and Issue: 10(14), P. 7686 - 7712

Published: July 1, 2020

Abstract Replicated multiple scale species distribution models (SDMs) have become increasingly important to identify the correct variables determining and their influences on ecological responses. This study explores multi‐scale habitat relationships of snow leopard ( Panthera uncia ) in two areas Qinghai–Tibetan Plateau western China. Our primary objectives were evaluate degree which relationships, expressed by predictors, scales response, magnitude effects, consistent across or locally landcape‐specific. We coupled univariate optimization maximum entropy algorithm produce multivariate SDMs, inferring relative suitability for ensembling top performing models. optimized SDMs based average omission rate ensembles’ overlap with a simulated reference model. Comparison highlighted landscape‐specific responses limiting factors. These dependent effects hydrological network, anthropogenic features, topographic complexity, heterogeneity landcover patch mosaic. Overall, even accounting specific local differences, we found general landscape attributes associated requirements, consisting positive association uplands ridges, aggregated low‐contrast landscapes, large extents grassy herbaceous vegetation. As means performance bias correction methods, explored three datasets showing range intensities. The corrections depends intensity; however, density kernels offered reliable strategy under all circumstances. reveals response leopards environmental confirms role meta‐replicated designs identification spatially varying Furthermore, this makes contributions ongoing discussion about best approaches sampling correction.

Language: Английский

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From Part to Whole: Scale-Dependence Habitat Selection by Snow Leopards (Panthera Uncia)

Published: Jan. 1, 2025

Snow leopards (Panthera uncia) are regarded as the most charismatic apex predator in alpine Asia, yet their populations under serious threat from human activities and habitat fragmentation. Ensuring effectiveness of current protected areas is critical for conservation, which necessitates a comprehensive understanding selection patterns at different spatial scales. Here, we conducted five-year camera trap survey snow Qilian Mountains used multi-scale modelling to investigate connectivity. Our results revealed scale-dependence leopard selection. We found that smaller scales, prey resource topographic variables were main factors determining leopards. Particularly, distribution probability primarily determined overall small scale. At larger however, there was stronger correlation between climate well impacts. The scale-optimized multivariate models indicated significant gaps protecting core habitats ensuring landscape More than 50% projected patches not included areas. Areas with highest number (Subei County) corridors (Tianjun also had least half area outside study provides insights conservation planning suggests prioritizing previously overlooked essential corridors.

Language: Английский

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Understanding Multi‐Scale and Multi‐Species Habitat Selection by Mammals in the Eastern Himalayan Biodiversity Hotspot

Ecology and Evolution, Journal Year: 2025, Volume and Issue: 15(4)

Published: April 1, 2025

ABSTRACT Human‐induced habitat loss and fragmentation threaten biodiversity in the Eastern Himalayas, a crucial part of Indo‐Myanmar hotspot. This study examines distribution 10 mammal species Arunachal Pradesh using multi‐scale ensemble modeling approach, integrating Generalized Linear Models (GLM), Additive (GAM), MaxEnt to assess suitability. By analyzing 57 environmental predictor variables across multiple spatial scales, we found that elevation is key determinant for carnivores such as dhole Asiatic golden cat, while herbivores like northern red muntjac mainland serow prefer broadleaf forests. Species distributions showed distinct patterns, with most concentrated south, except widely distributed yellow‐throated marten. Dhole leopard cat preferred elevated forests, favored mixed Herbivores were at higher elevations, whereas Indian wild pig grasslands degraded habitats near human settlements. While protected areas (PAs) exhibited richness, significant suitable also exist outside these regions, underscoring need landscape‐level conservation strategies. Precipitation seasonality population density emerged predictors, highlighting influence climatic anthropogenic factors on Our findings emphasize necessity conserving large, connected landscapes mitigate human‐induced pressures climate change impacts species. combining ecological insights, this provides framework prioritizing efforts. Future research should expand data collection broader temporal geographic scales incorporate projections anticipate shifts. These are critical guiding effective planning management ecologically rich yet vulnerable region.

Language: Английский

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Snow Leopard habitat vulnerability assessment under climate change and connectivity corridor in Xinjiang Uygur autonomous region, China

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 25, 2025

Climate change is recognized as one of the greatest challenges to global biodiversity. The endangered snow leopard (Panthera uncia), an apex predator in high-altitude mountain ecosystems, serves important indicator ecological health. Understanding impacts climate on distribution patterns essential for developing effective conservation strategies. Based BIOMOD2 model, this study assesses current suitable habitats and project future changes under various scenarios, well evaluates protection gap corridor construction Xinjiang Uygur Autonomous Region, China. results indicated total area habitat approximately 686,200 km2 conditions. remains relatively stable or slightly increases low emissions while predictions show a gradual decline moderate high scenarios. Currently, are fragmented, with connectivity among patches, posing threats population. Vulnerable primarily located Altai, northwestern Junggar Basin, central Tianshan Mountains. Potential areas projected emerge Kunlun It suggested that greater focus be placed unprotected refugia, enhancing corridors, fostering cross-border cooperation, implementing long-term monitoring efforts. This provides valuable insights strategies aimed at mitigating populations Xinjiang,

Language: Английский

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