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AIM: During the past decade ecologists have attempted to estimate the parameters of species distribution models by combining locations of species presence observed in opportunistic surveys with spatially referenced covariates of occurrence. Several statistical models have been proposed for the analysis of presence‐only data, but these models have largely ignored the effects of imperfect detection and survey bias. In this paper I describe a model‐based approach for the analysis of presence‐only data that accounts for errors in the detection of individuals and for biased selection of survey locations. INNOVATION: I develop a hierarchical, statistical model that allows presence‐only data to be analysed in conjunction with data acquired independently in planned surveys. One component of the model specifies the spatial distribution of individuals within a bounded, geographic region as a realization of a spatial point process. A second component of the model specifies two kinds of observations, the detection of individuals encountered during opportunistic surveys and the detection of individuals encountered during planned surveys. MAIN CONCLUSIONS: Using mathematical proof and simulation‐based comparisons, I demonstrate that biases induced by errors in detection or biased selection of survey locations can be reduced or eliminated by using the hierarchical model to analyse presence‐only data in conjunction with counts observed in planned surveys. I show that a relatively small number of high‐quality data (from planned surveys) can be used to leverage the information in presence‐only observations, which usually have broad spatial coverage but may not be informative of both occurrence and detectability of individuals. Because a variety of sampling protocols can be used in planned surveys, this approach to the analysis of presence‐only data is widely applicable. In addition, since the point‐process model is formulated at the level of an individual, it can be extended to account for biological interactions between individuals and temporal changes in their spatial distributions.

Genetic variation among populations is reflected in biogeographic patterns for many species, but general rules of spatial genetic variation have not been established. In this paper, we establish a theoretical framework based on projecting environmental Grinellian niches back through time to relate the present geographic distribution of population genetic structure to a given species' historical evolutionary context. Thanks to advances in next‐generation sequencing technologies, as well as more accurate climate models and the amassing of information stored in biological collections, it is possible to implement this theoretical framework directly. We develop a case study of the tassel‐eared squirrel Sciurus aberti to jointly analyze spatial, environmental, and genetic data to predict the historical endemic area of this species. Our results reveal that in cases of genetic isolation by geographic distance, the prevalence of environmental suitability over time corresponds to the genetic fixation index (Fst) of populations with respect to a source population. Populations closer to the historical endemic area show higher genetic diversity and a lower Fst value. This empirical example relates back to the theoretical framework, allowing two further advances: 1) a layer of biogeographic explanation for the results obtained from population genomic methods; and 2) predictive maps of this genetic structure to support biodiversity conservation efforts. Overall, this work advances a perspective that integrates population genetics with historical patterns of species distribution. The limitations posed in the theoretical framework should be considered before implementing the suitability prevalence area (SPA) in a general way over different taxa. Otherwise, the predictability of the genetic diversity of populations as a product of environmental stability over time may not be adequate.

The Caatinga, a seasonally dry tropical forest in northeastern Brazil, is notable for its biodiversity and high proportion of endemic plants adapted to its semi-arid environment. Among its prominent tree species, Myracrodruon urundeuva (Aroeira) stands out due to its extensive distribution and economic value. Despite its significance, little is known about the environmental factors influencing its distribution. This study uses species distribution modeling (SDM) to assess the current and potential distribution of M. urundeuva and its habitat suitability under various climate change scenarios. Utilizing models like GLM, GAM, and BRT, and MaxEnt, the research analyzes georeferenced occurrence data and bioclimatic variables (selected by the variance inflation factor) from precipitation and temperature metrics. Our findings indicate that M. urundeuva is projected to experience relative stability or slight expansion in suitable habitats under future climate scenarios, including the pessimistic SSP585 scenario. However, localized habitat losses may occur, particularly in certain regions and timeframes, highlighting the complex and regionally variable impacts of climate change. This study emphasizes the need for localized and regional action plans to mitigate climate change impacts on M. urundeuva ’s habitats. Conservation efforts should target areas identified as stable, ensuring the species’ resilience against escalating climate threats, thereby preserving one of its critical habitats within the Caatinga.

. Scaevola plumieri is an important pioneer on many tropical and subtropical sand dunes, forming a large perennial subterranean plant with only the tips of the branches emerging above accreting sand. In South Africa it is the dominant pioneer on sandy beaches along the east coast, less abundant on the south coast and absent from the southwest and west coasts. Transpiration rates (E) of S. plumieri are predictably related to atmospheric vapour pressure deficit under a wide range of conditions and can therefore be predicted from measurement of ambient temperature and relative humidity. Scaling measurements of E at the leaf level to the canopy level has been demonstrated previously. Using a geographic information system, digital maps of regional climatic variables were used to calculate digital maps of potential transpiration from mean monthly temperature and relative humidity values, effectively scaling canopy level transpiration rates to a regional level. Monthly potential transpiration was subtracted from the monthly median rainfall to produce a map of mean monthly water balance. Seasonal growth was correlated with seasonal water balance. Localities along the coast with water deficits in summer corresponded with the recorded absence of S. plumieri, which grows and reproduces most actively in the summer months. This suggests that reduced water availability during the summer growth period limits the distribution of S. plumieri along the southwest coast, where water deficits develop in summer. Temperature is also important in limiting the distribution of S. plumieri on the southwest coast of South Africa through its effects on the growth and phenology of the plant.

Scaevola plumieri is an important pioneer on many tropical and subtropical sand dunes, forming a large perennial subterranean plant with only the tips of the branches emerging above accreting sand. In South Africa it is the dominant pioneer on sandy beaches along the east coast, less abundant on the south coast and absent from the southwest and west coasts. Transpiration rates (E) of S. plumieri are predictably related to atmospheric vapour pressure deficit under a wide range of conditions and can therefore be predicted from measurement of ambient temperature and relative humidity. Scaling measurements of E at the leaf level to the canopy level has been demonstrated previously. Using a geographic information system, digital maps of regional climatic variables were used to calculate digital maps of potential transpiration from mean monthly temperature and relative humidity values, effectively scaling canopy level transpiration rates to a regional level. Monthly potential transpiration was subtracted from the monthly median rainfall to produce a map of mean monthly water balance. Seasonal growth was correlated with seasonal water balance. Localities along the coast with water deficits in summer corresponded with the recorded absence of S. plumieri, which grows and reproduces most actively in the summer months. This suggests that reduced water availability during the summer growth period limits the distribution of S. plumieri along the southwest coast, where water deficits develop in summer. Temperature is also important in limiting the distribution of S. plumieri on the southwest coast of South Africa through its effects on the growth and phenology of the plant. Nomenclature: Dyer (1967). Abbreviations: E = Transpiration; ISSR = Inter Simple Sequence Repeat; LAI = Leaf area index; RH = Relative humidity; SVP = Saturation vapour pressure; VPD = Vapour pressure deficit.

Random forest and similar Machine Learning techniques are already used to generate spatial predictions, but spatial location of points (geography) is often ignored in the modeling process. Spatial auto-correlation, especially if still existent in the cross-validation residuals, indicates that the predictions are maybe biased, and this is suboptimal. This paper presents a random forest for spatial predictions framework (RFsp) where buffer distances from observation points are used as explanatory variables, thus incorporating geographical proximity effects into the prediction process. The RFsp framework is illustrated with examples that use textbook datasets and apply spatial and spatio-temporal prediction to numeric, binary, categorical, multivariate and spatiotemporal variables. Performance of the RFsp framework is compared with the state-of-the-art kriging techniques using fivefold cross-validation with refitting. The results show that RFsp can obtain equally accurate and unbiased predictions as different versions of kriging. Advantages of using RFsp over kriging are that it needs no rigid statistical assumptions about the distribution and stationarity of the target variable, it is more flexible towards incorporating, combining and extending covariates of different types, and it possibly yields more informative maps characterizing the prediction error. RFsp appears to be especially attractive for building multivariate spatial prediction models that can be used as “knowledge engines” in various geoscience fields. Some disadvantages of RFsp are the exponentially growing computational intensity with increase of calibration data and covariates and the high sensitivity of predictions to input data quality. The key to the success of the RFsp framework might be the training data quality—especially quality of spatial sampling (to minimize extrapolation problems and any type of bias in data), and quality of model validation (to ensure that accuracy is not effected by overfitting). For many data sets, especially those with lower number of points and covariates and close-to-linear relationships, model-based geostatistics can still lead to more accurate predictions than RFsp.

Increasing extreme climatic events threaten the functioning of terrestrial ecosystems 1 , 2 . Because soil microbes govern key biogeochemical processes, understanding their response to climate extremes is crucial in predicting the consequences for ecosystem functioning 3 , 4 . Here we subjected soils from 30 grasslands across Europe to four contrasting extreme climatic events under common controlled conditions (drought, flood, freezing and heat), and compared the response of soil microbial communities and their functioning with those of undisturbed soils. Soil microbiomes exhibited a small, but highly consistent and phylogenetically conserved, response under the imposed extreme events. Heat treatment most strongly impacted soil microbiomes, enhancing dormancy and sporulation genes and decreasing metabolic versatility. Microbiome response to heat in particular could be predicted by local climatic conditions and soil properties, with soils that do not normally experience the extreme conditions being imposed being most vulnerable. Our results suggest that soil microbiomes from different climates share unified responses to extreme climatic events, but that predicting the extent of community change may require knowledge of the local microbiome. These findings advance our understanding of soil microbial responses to extreme events, and provide a first step for making general predictions about the impact of extreme climatic events on soil functioning. Soils from 30 grasslands across Europe were subjected to 4 contrasting extreme climatic events under drought, flood, freezing and heat conditions, with the results suggesting that soil microbiomes from different climates share unified responses to extreme climatic events.

Aim: The incompleteness of information on biodiversity distribution is a major issue for ecology and conservation. Researchers have made many attempts to quantify the amount of biodiversity that still remains unknown. We evaluated whether models that integrate ecogeographical variables with measures of the effectiveness of sampling can be used to estimate biodiversity patterns (species richness) of reptiles in remote areas that have received limited surveys. Location: The Western Palaearctic (Europe, Northern Africa, the Middle East and Central Asia). Methods: We gathered data on the distribution of turtles, amphisbaenians and lizards. We used regression models integrating spatial autocorrelation (spatial eigenvector mapping and Bayesian autoregressive models) to analyse species richness, and identified relationships between species richness, ecogeographical features and large-scale measures of accessibility. Results: The two regression techniques were in agreement. Known species richness was dependent on ecogeographical factors, peaking in areas with high temperature and annual actual evapotraspiration, and intermediate cover of natural vegetation. However, richness declined sharply in the least accessible areas. Our models revealed regions where reptile richness is likely to be higher than currently known, particularly in the biodiversity hotspots in the south of the Arabian Peninsula, the Irano-Anatolian region, and the Central Asian mountains. An independent validation data set, with distribution data collected recently throughout the study region, confirmed that combining accessibility measures with ecogeographical variables allows a good estimate of reptile richness, even in remote areas that have received limited monitoring so far. Some remote regions that support very rich communities are covered very little by protected areas. Main conclusions: Integrating accessibility measures into species distribution models allows biologists to identify areas where current knowledge underestimates the actual richness of reptiles. Our study identifies regions requiring future biodiversity research, proposes a novel approach to biodiversity prediction in poorly studied areas, and identifies potential regions for conservation.

Models based on species distributions are widely used and serve important purposes in ecology, biogeography and conservation. Their continuous predictions of environmental suitability are commonly converted into a binary classification of predicted (or potential) presences and absences, whose accuracy is then evaluated through a number of measures that have been the subject of recent reviews. We propose four additional measures that analyse observation-prediction mismatch from a different angle – namely, from the perspective of the predicted rather than the observed area – and add to the existing toolset of model evaluation methods. We explain how these measures can complete the view provided by the existing measures, allowing further insights into distribution model predictions. We also describe how they can be particularly useful when using models to forecast the spread of diseases or of invasive species and to predict modifications in species' distributions under climate and land-use change.

There is little appreciation of the level of extinction risk faced by one‐sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data‐deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species’ life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity.