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Climate change affects biodiversity in a variety of ways, necessitating the exploration of multiple climate dimensions using appropriate metrics. Despite the existence of several climate change metrics tools for comparing alternative climate change metrics on the same footing are lacking. To address this gap, we developed ‘climetrics' which is an extensible and reproducible R package to spatially quantify and explore multiple dimensions of climate change through a unified procedure. Six widely used climate change metrics are implemented, including 1) standardized local anomalies; 2) changes in probabilities of local climate extremes; 3) changes in areas of analogous climates; 4) novel climates; 5) changes in distances to analogous climates; and 6) climate change velocity. For climate change velocity, three different algorithms are implemented in the package including; 1) distanced-based velocity (‘dVe'); 2) threshold-based velocity (‘ve'); and 3) gradient-based velocity (‘gVe'). The package also provides additional tools to calculate the monthly mean of climate variables over multiple years, to quantify and map the temporal trend (slope) of a given climate variable at the pixel level, and to classify and map Köppen-Geiger (KG) climate zones. The 'climetrics' R package is integrated with the 'rts' package for efficient handling of raster time-series data. The functions in 'climetrics' are designed to be user-friendly, making them suitable for less-experienced R users. Detailed descriptions in help pages and vignettes of the package facilitate further customization by advanced users. In summary, the 'climetrics' R package offers a unified framework for quantifying various climate change metrics, making it a useful tool for characterizing multiple dimensions of climate change and exploring their spatiotemporal patterns.

Due to having a sensitive and fragile ecosystem, dry areas are constantly exposed to land degradation and desertification. Therefore, it is necessary to formulate appropriate strategies for quantitative assessment of desertification that are highly accurate. In this research, desertification of the region was first evaluated using MEDALUS model, then according to the results of MEDALUS model and reviewing the results of other researchers, 8 indicators remote sensing that had the highest correlation with field data were selected for modeling. Four machine learning methods Support Vector Machine (SVM), Gradient Boosting Machine (GBM), Generalized Linear Models (GLM) and Random Forests (RF) were used to model the risk of desertification in northeastern Iran. Finally, the weighted average of the ensemble model in the SDM statistical package was used to predict the desertification of the region. Based on the results obtained from MEDALUS model, the indicators of drought resistance (score 162), conservation operations (score 158) and soil salinity (score 155), in the working units of abandoned lands, wetland lands, and Salty lands located in the north East of the region, have increased the process of desertification. The results of modeling using machine learning methods showed that in 2002, the SVM model (AUC = 0.91, TSS = 0.93, and Kappa = 0.86) and in 2021, the RF model (AUC = 0.94, TSS = 0.94, and Kappa = 0.90) have performed best. The forecast of the combined model for desertification in 2021 in the studied area showed that the northeastern and sporadically in the central parts of the studied area are affected by the progress of the desertification process. Therefore, by considering the results of the combined model (as a model with the least uncertainty), it is possible to reduce the progress of the desertification process by planning, optimal management and applying corrective methods in the areas affected by desertification.

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.

A longstanding debate exists on whether ecological phenomena should be modelled ‘top-down’ (modelling patterns that arise from mechanisms) or ‘bottom-up’ (modelling mechanisms to generate pattern). Recently, the discussion re-emerged in the context of modelling the spread of SARS-CoV-21 . Simply put, the point made by Carlson et al. was that top-down correlative models are inappropriate, whereas bottom-up epidemiological models are fine. Rather than opposing families of models, we argue that all have strengths and limitations and that judicious use of all available tools is the way forward.

As new cases of COVID-19 are being confirmed worldwide pressure is mounting to increase understanding of the factors underlying the spread the disease. Using data on local transmissions until the 23rd of March 2020, we develop an ensemble of 200 ecological niche models to project monthly variation in climate suitability for spread of SARS-CoV-2 throughout a typical climatological year. Although cases of COVID-19 are reported all over the world, most outbreaks display a pattern of clustering in relatively cool and dry areas. The predecessor SARS-CoV-1 was linked to similar climate conditions. Should the spread of SARS CoV-2 continue to follow current trends, asynchronous seasonal global outbreaks could be expected. According to the models, temperate warm and cold climates are more favorable to spread of the virus, whereas arid and tropical climates are less favorable. However, model uncertainties are still high across much of sub-Saharan Africa, Latin America and South East Asia. While models of epidemic spread utilize human demography and mobility as predictors, climate can also help constrain the virus. This is because the environment can mediate human-to-human transmission of SARS-CoV-2, and unsuitable climates can cause the virus to destabilize quickly, hence reducing its capacity to become epidemic.

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades-including a maturation of relevant theory and key concepts-but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an "Overview" talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades-including a maturation of relevant theory and key concepts-but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an "Overview" talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.

While the scientific community documents environmental degradation and develops scenarios to identify the operational margins of system Earth, less attention is given to how decisions are made that steer the system in one direction or the other. We propose to use strategy games for this purpose, increasing the representation of human agency in scenario development and creating spaces for deliberation between different worldviews. Played by the right people, strategy games could help break free from established norms and support more transparent democratic dialogues, responding to the human and social limitations of current decision-making. The question is, who gets to play?

Humans have moved species away from their native ranges since the Neolithic, but globalization accelerated the rate at which species are being moved. We fitted more than half million distribution models for 610 traded bird species on the CITES list to examine the separate and joint effects of global climate and land-cover change on their potential end-of-century distributions. We found that climate-induced suitability for modelled invasive species increases with latitude, because traded birds are mainly of tropical origin and much of the temperate region is ‘tropicalizing.’ Conversely, the tropics are becoming more arid, thus limiting the potential from cross-continental invasion by tropical species. This trend is compounded by forest loss around the tropics since most traded birds are forest dwellers. In contrast, net gains in forest area across the temperate region could compound climate change effects and increase the potential for colonization of low-latitude birds. Climate change has always led to regional redistributions of species, but the combination of human transportation, climate, and land-cover changes will likely accelerate the redistribution of species globally, increasing chances of alien species successfully invading non-native lands. Such process of biodiversity homogenization can lead to emergence of non-analogue communities with unknown environmental and socioeconomic consequences.

Scientists still poorly understand how biotic interactions and dispersal limitation jointly interact and affect the ability of species to track suitable habitats under climate change. Here, we examine how animal-plant interactions and dispersal limitations might affect the responses of Brazil nut-dependent frogs facing projected climate change. Using ecological niche modelling and dispersal simulations, we forecast the future distributions of the Brazil nut tree and three commensalist frog species over time (2030, 2050, 2070, and 2090) in the regional rivalry (SSP370) scenario that includes great challenges to mitigation and adaptation. With the exception of one species, projections point to a decrease in suitable habitats of up to 40.6%. For frog species with potential reductions of co-occurrence areas, this is expected to reduce up to 23.8% of suitable areas for binomial animal-plant relationships. Even so, biotic interactions should not be lost over time. Species will depend on their own dispersal abilities to reach analogous climates in the future for maintaining ecological and evolutionary processes associated with commensal taxa. However, ecological and evolutionary processes associated with commensal taxa should be maintained in accordance with their own dispersal ability. When dispersal limitation is included in the models, the suitable range of all three frog species is reduced considerably by the end of the century. This highlights the importance of dispersal limitation inclusion for forecasting future distribution ranges when biotic interactions matter.

While the scientific community documents environmental degradation and develops scenarios to identify the operational margins of system Earth, less attention is given to how decisions are made that steer the system in one direction or the other. We propose to use strategy games for this purpose, increasing the representation of human agency in scenario development and creating spaces for deliberation between different worldviews. Played by the right people, strategy games could help break free from established norms and support more transparent democratic dialogues, responding to the human and social limitations of current decision-making. The question is, who gets to play?

Humans have moved species away from their native ranges since the Neolithic, but globalization accelerated the rate at which species are being moved. We fitted more than half million distribution models for 610 traded bird species on the CITES list to examine the separate and joint effects of global climate and land-cover change on their potential end-of-century distributions. We found that climate-induced suitability for modelled invasive species increases with latitude, because traded birds are mainly of tropical origin and much of the temperate region is ‘tropicalizing.’ Conversely, the tropics are becoming more arid, thus limiting the potential from cross-continental in vasion by tropical species. This trend is compounded by forest loss around the tropics since most traded birds are forest dwellers. In contrast, net gains in forest area across the temperate region could compound climate change effects and increase the potential for colonization of low-latitude birds. Climate change has always led to regional redistributions of species, but the combination of human transportation, climate, and land-cover changes will likely accelerate the redistribution of species globally, increas ing chances of alien species successfully invading non-native lands. Such process of biodiversity homogenization can lead to emergence of non-analogue communities with unknown environmental and socioeconomic consequences.

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow. In this paper, we present a new R package—rasterdiv—to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms.