Spatial assessment of biodiversity and conservation priorities in Hamedan Province, Iran, using a landscape ecology approach DOI
Sedighe Abdollahi, Parinaz Khalilzadeh,

Elahe Zeilabi

и другие.

Journal of Environmental Studies and Sciences, Год журнала: 2024, Номер 14(2), С. 358 - 371

Опубликована: Янв. 23, 2024

Язык: Английский

Using ecological security pattern to identify priority protected areas: A case study in the Wuhan Metropolitan Area, China DOI Creative Commons
Wen Zeng, Huan Tang, Xun Liang

и другие.

Ecological Indicators, Год журнала: 2023, Номер 148, С. 110121 - 110121

Опубликована: Март 14, 2023

Protected areas (PAs) play a key role in mitigating ecological crises. Currently, priority protected (PPAs) focus on biological conservation, and few studies have considered the connectivity between patches. Few formulated future conservation measures from two dimensions of security pattern (ESP) reserve effectiveness. To fill this gap, study use ESP to identify that meet objectives. We take Wuhan metropolitan area as research area. constructed framework for formulating development plans based areas. The complete method system, we focused construction evaluation index system landscape connectivity. Then, effectiveness PAs could be evaluated, PPAs identified. results showed there were five isolated among existing PAs. Moreover, total was 9328.91 km2, they had high value. Due low protection rate PPAs, are not main target PAs; thus, new According our plan, with different classes will achieve functions work. Our focuses achieving sustainable formulates environmental land planning balance urban development. It can provide information support realization 2030 vision

Язык: Английский

Процитировано

40

Defining priority areas for conservation based on multispecies functional connectivity DOI

Wanderson Lopes Lamounier,

Juliana Silveira dos Santos,

Evandro Luís Linhari Rodrigues

и другие.

Biological Conservation, Год журнала: 2024, Номер 290, С. 110438 - 110438

Опубликована: Янв. 5, 2024

Язык: Английский

Процитировано

9

Melanesia holds the world’s most diverse and intact insular amphibian fauna DOI Creative Commons
Paul M. Oliver, Deborah S. Bower, Peter J. McDonald

и другие.

Communications Biology, Год журнала: 2022, Номер 5(1)

Опубликована: Ноя. 4, 2022

Abstract Identifying hotspots of biological diversity is a key step in conservation prioritisation. Melanesia—centred on the vast island New Guinea—is increasingly recognised for its exceptionally species-rich and endemic biota. Here we show that Melanesia has world’s most diverse insular amphibian fauna, with over 7% global frog species less than 0.7% land area, 97% endemic. We further estimate nearly 200 additional candidate have been discovered but remain unnamed, pointing to total fauna excess 700 species. Nearly 60% Melanesian lineage direct-developing microhylids characterised by smaller distributions co-occurring families, suggesting lineage-specific high beta driver anuran megadiversity. A comprehensive status assessment highlights geographic concentrations recently described range-restricted threatened taxa warrant urgent actions. Nonetheless, world standards, relatively intact, 6% assessed listed as no documented extinctions; thus it provides an unparalleled opportunity understand conserve megadiverse intact

Язык: Английский

Процитировано

35

Not seeing the forest for the trees: Generalised linear model out-performs random forest in species distribution modelling for Southeast Asian felids DOI Creative Commons
Luca Chiaverini, David W. Macdonald, Andrew J. Hearn

и другие.

Ecological Informatics, Год журнала: 2023, Номер 75, С. 102026 - 102026

Опубликована: Фев. 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.

Язык: Английский

Процитировано

23

Multi‐scale, multivariate community models improve designation of biodiversity hotspots in the Sunda Islands DOI
Luca Chiaverini, David W. Macdonald, Helen M. Bothwell

и другие.

Animal Conservation, Год журнала: 2022, Номер 25(5), С. 660 - 679

Опубликована: Фев. 27, 2022

Abstract Species occur in sympatric assemblages, bound together by ecological relationships and interspecific interactions. Borneo Sumatra host some of the richest assemblages biota worldwide. The region, however, faces highest global deforestation rates, which seriously threaten its unique biodiversity. We used a large camera trap dataset that recorded data for 70 terrestrial species mammals birds, to explore drivers regional richness patterns. Using multi‐scale, multivariate modelling framework quantified main environmental factors associated with patterns biodiversity, while simultaneously assessing individual each species, we determined sampled their contributions community assemblages. then mapped predicted richness, evaluated effectiveness protected areas securing biodiversity hotspots, performed gap analysis highlight biodiverse lacking protection compared our predictions maps produced using IUCN range layers. Finally, investigated performance as an indicator demonstrate is primarily affected gradients anthropogenic factors, only marginally topographic spatial factors. In both islands, are elevational vegetation climate, leading altitudinal zonation niche separation major factor characterizing islands' was north‐eastern western Sumatra. found most hotspots not formally either island; 9.2 18.2% modelled occurred within Sumatra, respectively. highlighted prediction better than, differed drastically from, layer, layer one were similar, showed low predictive power. Our suggests common generalist carnivores effective indicators have high potential focal, umbrella or assist multi‐species vertebrate conservation planning. Understanding existing critical support development strategies this rapidly changing region.

Язык: Английский

Процитировано

19

Evaluating the Impact of Climate Change on the Asia Habitat Suitability of Troides helena Using the MaxEnt Model DOI Creative Commons

Fengrong Yang,

Quanwei Liu, Jun‐Yi Yang

и другие.

Insects, Год журнала: 2025, Номер 16(1), С. 79 - 79

Опубликована: Янв. 14, 2025

Butterflies are highly sensitive to climate change, and Troides helena, as an endangered butterfly species, is also affected by these changes. To enhance the conservation of T. helena effectively plan its protected areas, it crucial understand potential impacts change on distribution. This study utilized a MaxEnt model in combination with ArcGIS technology predict global suitable habitats under current future conditions, using species’ distribution data relevant environmental variables. The results indicated that provided good prediction accuracy for helena. Under scenario, species primarily distributed tropical regions, high suitability areas concentrated rainforest climates. In scenarios, habitat medium categories generally show expansion trend, which increases over time. Especially SSP5-8.5 2090s, area projected increase 42.85%. analysis key factors revealed precipitation wettest quarter (Bio16) was most significant factor affecting has demands temperature can adapt warming. valuable identifying optimal provides reference efforts.

Язык: Английский

Процитировано

0

Anthropogenic and environmental correlates of spatial patterns of co-occurrence of small felids in a montane landscape DOI
Karma Choki, Egil Dröge, Claudio Sillero‐Zubiri

и другие.

Global Ecology and Conservation, Год журнала: 2025, Номер 58, С. e03422 - e03422

Опубликована: Янв. 19, 2025

Язык: Английский

Процитировано

0

Selecting surrogate species for species-based conservation in the Plateau-Mountains-Basin transition region of southwestern China DOI Creative Commons
Yuchen Liao, Xuewei Shi, Yan Wu

и другие.

Ecological Indicators, Год журнала: 2025, Номер 173, С. 113433 - 113433

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Ecosystem service trade-offs weaken ecological network interactions DOI
Jing Xie, Binggeng Xie, Junhan Li

и другие.

Journal of Cleaner Production, Год журнала: 2025, Номер unknown, С. 145572 - 145572

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0

Understanding Multi‐Scale and Multi‐Species Habitat Selection by Mammals in the Eastern Himalayan Biodiversity Hotspot DOI Creative Commons
Arif Ahmad,

Govindan Veeraswami Gopi

Ecology and Evolution, Год журнала: 2025, Номер 15(4)

Опубликована: Апрель 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.

Язык: Английский

Процитировано

0