Species Distribution and Climate Change

This study predicts the future invasion risk of Amegilla pulchra under climate and anthropogenic changes, finding higher risks in non-native regions and contrasting effects between native and non-native areas.

The paper introduces an R package called SAMPLE to estimate sampling effort needed for accurate species occurrence rate estimation, using simulation and real coral data.

This study models past and future distribution shifts of Cannabis sativa under different climate scenarios, identifying key environmental variables and predicting significant habitat loss by 2050.

This study identifies factors driving biodiversity recording activity by citizen scientists, finding that weekends, mild weather, and spring months correlate with higher recording frequency.

This study uses ecological niche models with global occurrence data to predict suitable habitats for marine species in data-scarce regions, focusing on sharks in the Southwest Atlantic.

The paper develops a habitat suitability model for marbled murrelets using LiDAR-derived tree metrics to identify suitable nesting patches and individual trees in redwood timberlands.

This study identifies climate refugia for the Eurasian Stone-curlew under climate change and finds most are outside protected areas, suggesting a need for targeted conservation actions.

The paper compares correlative and process-based species distribution models to assess their performance under climate change, finding process-based models more reliable.

The paper identifies grassland habitat suitability hotspots under current and future climates using species distribution modeling and overlays them with connectivity and protection data to guide restoration efforts.

This study reclassifies Koppen-Geiger climate zones using microclimate data, finding that microclimate reclassifies 38% of global areas and shows greater spatial variability.

The paper uses a seed addition experiment to test lagged climate responses and microclimatic buffering in plant range shifts, finding support for lagged responses over microclimatic buffering.

The paper assesses distribution shifts of eight rare woody species in South China under climate change and identifies wood density as a trait predicting vulnerability to global change.

This study shows that ignoring invisible landscapes, like individual experience, can bias habitat selection estimates and future population distribution projections.

This paper improves species distribution model range edge predictions by integrating climatic extremes using the GEV distribution, showing better performance in capturing extreme events compared to existing methods.

This study confirms the impact of climate change on biodiversity through a global survey of experts, finding significant correlations between climate change and biodiversity disruption.

This study uses Joint Species Distribution Models to predict how global change affects bee biodiversity in the Mojave and Sonoran Deserts, showing varying impacts across protected and non-protected areas.

The paper introduces metaRange, an R package for building mechanistic range models, and demonstrates its ability to simulate species range dynamics under various ecological scenarios.

This study shows that finer spatial grain in species distribution models improves predictions of bird distributions and biodiversity at continental scales.

This paper predicts phenological stages of invasive species using environmental data and opportunistic observations to improve early detection.

This paper integrates expert range maps with species distribution models using stacked generalization to improve range delineation and account for uncertainty.

This paper introduces a GPU-accelerated version of the Hmsc-R JSDM framework, achieving over 1000x speed-up for large datasets and improving scalability for biodiversity modeling.

This study maps the spatial distribution of insular cliff vegetation in the Balearic islands and predicts its future changes under climate scenarios.

The paper introduces the R package enmpa for ecological niche modeling using presence-absence data and generalized linear models, demonstrating its application with a simulated pathogen example.

The paper models the potential distribution of sweet cherry trees in China using the MaxEnt model, identifying key climate factors and suitable regions for cultivation.

The paper evaluates urban tolerance of native species in Los Angeles using crowd-sourced data to identify patterns and inform biodiversity management.

The paper reassesses range limits of Alaska's land mammals by comparing occurrence records with existing range maps, identifying extralimital records and updating baseline range maps.

The paper uses ecological niche modeling to assess competition between native and naturalized ferns in Hawaiian dryland habitats, finding significant overlap and conservation risks.

This study assesses how including migrant species in conservation policies can reduce extinction risk and alter global conservation priorities.

The paper reassesses climate change risks for terrestrial ecosystems by transforming climate data into phytoclimates, finding significant changes and novel climates by 2070.

This study uses MERRA-2 data and field observations to analyze historical changes in climatic suitability for Cassin's Sparrow, revealing complex patterns that vary by time series and environmental drivers.