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Aim: Climate change poses a challenge to the Azores' biodiversity, with consequences that remain unexplored. To shed light on the potential impacts of climate change, we have developed a large ensemble of species distribution models (SDMs) for species found in the coastal marine environments and examined their spatiotemporal turnover and stability. Location: The Azorean archipelago. Taxon: Coastal marine species (mammals, fish, turtles, seabirds, kelp forest and corals). Methods: SDMs were fitted a large ensemble comprising 10 machine learning algorithms and a fivefold cross‐validation resampling procedure, thus yielding a maximum number of 50 models fitted per species. These models were then utilised for projecting species distribution under different future scenarios. The projected distributions of the species were employed to assess changes in the stability of their ranges throughout the entire modelled period (2030–2100) and in their community compositions by examining changes in alpha diversity and beta diversity over 10‐year periods. Results: We show that under our model assumptions over 12% of the modelled units could lose suitable climate by the end of the century, with this number increasing up to 25% under a high carbon emissions scenario. Climate change refugia, which are areas of long‐term species range stability, are expected to be mainly located in the coastal areas in the northernmost part of the archipelago. A substantial loss of suitable climate is anticipated for mammals and birds, which is likely to trigger a major loss of species on the islands of Santa Maria, São Miguel, Pico and Faial. For fish, the loss of suitable climates is less pronounced. However, climate change is expected to cause a major reshuffling of the pelagic fish assemblage, with important consequences for local fisheries on each island. Main Conclusions: Our models provide insights into how climate change may alter the distribution of Azorean marine coastal species, offering important guidance for conservation and management efforts in these important North Atlantic ecosystems. [ABSTRACT FROM AUTHOR]

Quantifying how climatic change affects wheat production, and accurately predicting its potential distributions in the face of future climate, are highly important for ensuring food security in Ethiopia. This study leverages advanced machine learning algorithms including Random Forest, Maxent, Boosted Regression Tree, and Generalised Linear Model alongside an ensemble approach to accurately predict shifts in wheat habitat suitability in the Central Ethiopia Region over the upcoming decades. An extensive dataset consisting of 19 bioclimatic variables (Bio1–Bio19), elevation, solar radiation, and topographic positioning index was refined by excluding collinear predictors to increase model accuracy. The analysis revealed that the precipitation of the wettest month, minimum temperature of the coldest month, temperature seasonality, and precipitation of the coldest quarter are the most influential factors, which collectively account for a significant proportion of habitat suitability changes. The future projections revealed that up to 100% of the regions currently classified as moderately or highly suitable for wheat could become unsuitable by 2050, 2070, and 2090, illustrating a dramatic potential decline in wheat production. Generally, the future of wheat cultivation will depend heavily on developing varieties that can thrive under altered conditions; thus, immediate and informed action is needed to safeguard the food security of the region. [ABSTRACT FROM AUTHOR]

Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models (BEMs). The approach can be used to investigate patterns and processes of species richness. If data limitations on individual species distributions are inevitable, but how do they affect inferences of patterns and processes of species richness? We investigate the influence of different data sources on estimated species richness gradients in China. We fitted BEMs using species distributions data for 334 bird species obtained from (1) global range maps, (2) regional checklists, (3) museum records and surveys, and (4) citizen science data using presence‐only (Mahalanobis distance), presence‐background (MAXENT), and presence–absence (GAM and BRT) BEMs. Individual species predictions were stacked to generate species richness gradients. Here, we show that different data sources and BEMs can generate spatially varying gradients of species richness. The environmental predictors that best explained species distributions also differed between data sources. Models using citizen‐based data had the highest accuracy, whereas those using range data had the lowest accuracy. Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty. When multiple data sets exist for the same region and taxa, we advise that explicit treatments of uncertainty, such as sensitivity analyses of the input data, should be conducted during the process of modeling. [ABSTRACT FROM AUTHOR]

The increasing online availability of biodiversity data and advances in ecological modeling have led to a proliferation of open‐source modeling tools. In particular, R packages for species distribution modeling continue to multiply without guidance on how they can be employed together, resulting in high fidelity of researchers to one or several packages. Here, we assess the wide variety of software for species distribution models (SDMs) and highlight how packages can work together to diversify and expand analyses in each step of a modeling workflow. We also introduce the new R package ‘sdmverse' to catalog metadata for packages, cluster them based on their methodological functions, and visualize their relationships. To demonstrate how pluralism of software use helps improve SDM workflows, we provide three extensive and fully documented analyses that utilize tools for modeling and visualization from multiple packages, then score these tutorials according to recent methodological standards. We end by identifying gaps in the capabilities of current tools and highlighting outstanding challenges in the development of software for SDMs. [ABSTRACT FROM AUTHOR]