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Aim: Ongoing climate change is presently influencing the distribution ranges of numerous species, with both range expansions and latitudinal shifts being observed. In southern Europe, a biogeographical border that separates African and European biota, while at the same time acting as a migration bridge for many species, these changes are of particular relevance. This study aimed to analyse the responses of nine typically African birds to climate change to provide information on the ongoing and future occupation of Europe by these species. Location: Western Palearctic and surrounding areas. Methods: To this end, the distributions of the species in their native ranges were modelled, both in the present and in future climate scenarios, using their current breeding ranges and a set of topographic and climatic variables. The climatic favourability for the nine species was then combined using fuzzy logic. Results: The results showed that southern Europe is highly favourable for our set of African birds, except for Rüppell's Vulture, and future forecasts indicated that this favourability would increase further north, again excluding the African Vulture. Main conclusions: If the climate continues to warm, further arrivals of individuals are to be expected, increasing the possibility that self‐sustaining populations may become established in southern Europe. Furthermore, new African species may start to occupy this area, with the likelihood of an Africanisation of the European fauna. Considering the role played by southern Europe as a potential focal point for the colonisation of this continent by African species, it is important to track their northward expansion and future spread. [ABSTRACT FROM AUTHOR]

Background: Experts use knowledge to infer the distribution of species based on fuzzy logical assumptions about the relationship between species and the environment. Thus, expert knowledge is amenable to fuzzy logic modelling, which give to propositions a continuous truth value between 0 and 1. In species distribution modelling, fuzzy logic may also be used to model, from a number of records, the degree to which conditions are favourable to the occurrence of a species. Therefore, fuzzy logic operations can be used to compare and combine models based on expert knowledge and species records. Here, we applied fuzzy logic modelling to the distribution of amphibians in Uruguay as inferred from expert knowledge and from observed records to infer favourable locations, with favourability being the commensurable unit for both kinds of data sources. We compared the results for threatened species, species considered by experts to be ubiquitous, and non-threatened, non-ubiquitous species. We calculated the fuzzy intersection of models based on both knowledge sources to obtain a unified prediction of favourable locations. Results: Models based on expert knowledge involved a larger number of variables and were less affected by sampling bias. Models based on experts had the same overprediction rate for the three types of species, whereas models based on species records had a lower prediction rate for ubiquitous species. Models based on expert knowledge performed equally as well or better than corresponding models based on species records for threatened species, even when they had to discriminate and classify the same set of records used to build the models based on species records. For threatened species, expert models predicted more restrictive favourable territories than those predicted based on records. Observed records generated the best-fitted models for non-threatened non-ubiquitous species, and ubiquitous species. Conclusions: Fuzzy modelling permitted the objective comparison of the potential of expert knowledge and incomplete distribution records to infer the territories favourable for different species. Distribution of threatened species was able to be better explained by subjective expert knowledge, while for generalist species models based on observed data were more accurate. These results have implications for the correct use of expert knowledge in conservation planning. [ABSTRACT FROM AUTHOR]

Background: The current modification of species distribution ranges, as a response to a warmer climate, constitutes an interesting line of work and a recent challenge for biogeography. This study aimed to determine if the climatic conditions of southern Europe are adequate to host a typical African species, the House Bunting, which is registered regularly during the last years, still in low numbers. To this end, the distribution of the species in its native range was modelled, both in the present and in future climate scenarios, using its current breeding distribution areas and a set of environmental variables. Results: The results showed that the southern half of the Iberian Peninsula exhibits high values of favourability to host this African species for the current climatic conditions. Furthermore, future forecasts indicated an increase in favourability for this area. The highly favourable areas we detected in the south of the Iberian Peninsula are already regularly receiving individuals of the species. These observations are very likely vagrant birds dispersing from recently colonised breeding areas in northern Morocco, which may indicate a continuous process of colonisation towards the north, as has occurred during the last decades in Northern Africa. Conclusions: We cannot anticipate when the House Bunting will establish on the European continent because colonisation processes are usually slow but, according to our results, we predict its establishment in the near future. We have also identified those areas hosting favourable conditions for the species in Europe. These areas are a potential focal point for the colonisation of this and other African birds if the climate continues to warm. [ABSTRACT FROM AUTHOR]

Aim: (i) To assess the dependence between the form of the decrease in biological similarity with distance (distance‐decay) and species range size and (ii) to introduce the use of a sigmoidal model, the Gompertz function, as a flexible alternative able to fit distance‐decay models under a wide variety of species range sizes. Location: Applicable worldwide. Methods: We computed distance‐decay curves from simulated communities to assess how the species range sizes shape the functional form of distance‐decay patterns (i.e. negative exponential, power‐law or sigmoidal [Gompertz] relationships). Simulations were performed using different sample sizes and species detection probabilities. We also used distribution data of South American mammals to explore the relationship between species range size and the distance‐decay form in an empirical dataset. Results: Our simulations showed that the power‐law is the best supported model when range sizes tend to be small. An increase in range sizes leads to a negative exponential relationship, taking the shape of a sigmoidal (Gompertz) relationship with the largest range size values. Similar results have been found in the distance‐decay pattern of South American mammals. Remarkably, the Gompertz function fits the data reasonably well in all scenarios. Main conclusions: The functional form of distance‐decay patterns depends on a key biogeographical attribute: species range size. This dependence makes it an interesting tool to detect biodiversity threats associated with species range expansion, such as the biotic homogenization of faunas. The Gompertz function is the mathematical model that best accommodates different frequency distributions of species range size and, thus, allows cross‐taxa comparison of this biogeographical and ecological pattern. [ABSTRACT FROM AUTHOR]

Aim: Species rarity is often used as a measure to assess the risk of extinction of species, and thus, different methods have been developed to describe the composition of rare species in biological communities. These methods usually depend on species attributes that are not always available and very often ignore imperfect species detection. In this work, we developed a new method to characterize species rarity in a community when species are detected imperfectly. Our modelling framework is based on Bayesian occupancy models to estimate species distributions under imperfect detection using presence‐nondetection data. Innovation: We propose a finite mixture occupancy model to identify rare species based on their occupancy and class‐membership probabilities. Here, we explored a two‐class finite mixture model to distinguish between rare and common species classes and presented the general modelling framework for a problem with more than two classes. By using simulations, we were able to compare our model results under different scenarios obtaining a high‐classification performance across all of them. Additionally, we applied our model to a data set of Odonata occurrence records that were partially observed due to imperfect detection and quantified the proportion of rare species on a national scale across waterbodies in the United Kingdom. Main conclusions: Nowadays, biodiversity conservation involves monitoring programmes that target multiple species within a community where individual species responses may vary widely. This high variability makes the task of identifying the ecological processes that drive distributions of rare species difficult. Thus, our method represents a new approach to characterize the composition of a community in terms of species rarity while correcting for detectability bias. Our modelling framework also suggests lines of research and future developments for the understanding of how species rarity can be measured in a wide range of scenarios. [ABSTRACT FROM AUTHOR]

Aim: Identifying the mechanisms influencing species' distributions is critical for accurate climate change forecasts. However, current approaches are limited by correlative models that cannot distinguish between direct and indirect effects. Location: New Hampshire and Vermont, USA. Methods: Using causal and correlational models and new theory on range limits, we compared current (2014–2019) and future (2080s) distributions of ecologically important mammalian carnivores and competitors along range limits in the northeastern US under two global climate models (GCMs) and a high‐emission scenario (RCP8.5) of projected snow and forest biomass change. Results: Our hypothesis that causal models of climate‐mediated competition would result in different distribution predictions than correlational models, both in the current and future periods, was well‐supported by our results; however, these patterns were prominent only for species pairs that exhibited strong interactions. The causal model predicted the current distribution of Canada lynx (Lynx canadensis) more accurately, likely because it incorporated the influence of competitive interactions mediated by snow with the closely related bobcat (Lynx rufus). Both modeling frameworks predicted an overall decline in lynx occurrence in the central high‐elevation regions and increased occurrence in the northeastern region in the 2080s due to changes in land use that provided optimal habitat. However, these losses and gains were less substantial in the causal model due to the inclusion of an indirect buffering effect of snow on lynx. Main conclusions: Our comparative analysis indicates that a causal framework, steeped in ecological theory, can be used to generate spatially explicit predictions of species distributions. This approach can be used to disentangle correlated predictors that have previously hampered understanding of range limits and species' response to climate change. [ABSTRACT FROM AUTHOR]

Aim: Species distribution models (SDMs) have emerged as essential tools in the equipment of many ecologists, useful to explore species distributions in space and time and answering an assortment of questions related to biogeography, climate change biology and conservation biology. Historically, most SDM research concentrated on well‐known organisms, especially vertebrates. In recent years, these tools are becoming increasingly important for predicting the distribution of understudied invertebrate taxa. Here, we reviewed the literature published on main terrestrial arthropod predators (ants, ground beetles and spiders) to explore some of the challenges and opportunities of species distribution modelling in mega‐diverse arthropod groups. Location: Global. Methods: Systematic mapping of the literature and bibliometric analysis. Results: Most SDM studies of animals to date have focused either on broad samples of vertebrates or on arthropod species that are charismatic (e.g. butterflies) or economically important (e.g. vectors of disease, crop pests and pollinators). We show that the use of SDMs to map the geography of terrestrial arthropod predators is a nascent phenomenon, with a near‐exponential growth in the number of studies over the past ten years and still limited collaborative networks among researchers. There is a bias in studies towards charismatic species and geographical areas that hold lower levels of diversity but greater availability of data, such as Europe and North America. Conclusions: Arthropods pose particular modelling challenges that add to the ones already present for vertebrates, but they should also offer opportunities for future SDM research as data and new methods are made available. To overcome data limitations, we illustrate the potential of modern data sources and new modelling approaches. We discuss areas of research where SDMs may be combined with dispersal models and increasingly available phylogenetic and functional data to understand evolutionary changes in ranges and range‐limiting traits over past and contemporary time‐scales. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Using fuzzy logic to compare species distribution models developed on the basis of expert knowledge and sampling records" published in Frontiers in Zoology. The authors reported that the link to the data repository was missing in the original article. The correction provides the correct link to access the dataset supporting the conclusions of the article. The original article has been updated accordingly. [Extracted from the article]

Aim: Information on the spatio‐temporal distribution of marine species is essential for developing proactive management strategies. However, sufficient information is seldom available at large spatial scales, particularly in polar areas. The Southern Ocean (SO) represents a critical habitat for various species, particularly migratory baleen whales. Still, the SO's remoteness and sea ice coverage disallow obtaining sufficient information on baleen whale distribution and niche preference. Here, we used presence‐only species distribution models to predict the circumantarctic habitat suitability of baleen whales and identify important predictors affecting their distribution. Location: The Southern Ocean (SO). Methods: We used Maxent to model habitat suitability for Antarctic minke, Antarctic blue, fin and humpback whales. Our models employ extensive circumantarctic data and carefully prepared predictors describing the SO's environment and two spatial sampling bias correction options. Species‐specific spatial‐block cross‐validation was used to optimize model complexity and for spatially independent model evaluation. Results: Model performance was high on cross‐validation, with generally little predicted uncertainty. The most important predictors were derived from sea ice, particularly seasonal mean and variability of sea ice concentration and distance to the sea ice edge. Main conclusions: Our models support the usefulness of presence‐only models as a cost‐effective tool in the marine realm, particularly for studying the migratory whales' distribution. However, we found discrepancies between our results and (within) results of similar studies, mainly due to using different species data quality and quantity, different study area extent and methodological reasons. We further highlight the limitations of implementing static distribution models in the highly dynamic marine realm. Dynamic models, which relate species information to environmental conditions contemporaneous to species occurrences, can predict near‐real‐time habitat suitability, necessary for dynamic management. Nevertheless, obtaining sufficient species and environmental predictors at high spatio‐temporal resolution, necessary for dynamic models, can be challenging from polar regions. [ABSTRACT FROM AUTHOR]

Aim: As parapatric distributions are traditionally studied by pairs of species, this study aims to evaluate the causes of the maintenance of parapatry, specifically to assess whether the case of the three viper species present in the Iberian Peninsula is mainly caused by environmental conditions, historical events, interspecific competition among them, or a combination of these factors. Location: The Iberian Peninsula. Taxon: Vipera aspis, V. latastei and V. seoanei.. Methods: We applied the concept of favourability of occurrence to produce commensurate distribution units unaffected by the prevalence of different species in the Iberian Peninsula. We compared the favourability of each species individually with the favourability of occurrence of more than one species co‐occurring in their overlapping ranges, and identified the areas in which sympatric coexistence, environmental segregation and competitive exclusion are predicted to occur. Results: The main driver of the parapatric pattern was competitive exclusion, which was mediated by gradual changes in the environmental conditions favouring each species. The para‐Mediterranean V. aspis presented fragmented favourable areas outside its range that seemed to be unoccupied due to competitive exclusion by the other vipers. The Mediterranean V. latastei appeared to be limited by competition with the other viper species and by the environment in north‐western Iberia. V. seoanei prevented the other two species becoming established in the north‐western quarter of the Iberian Peninsula due to its better adaption to the humid conditions in this region. Some areas of sustainable coexistence were detected, mainly in the upper reaches of the River Ebro. Main conclusions: Each species is better adapted to particular conditions. However, the existence of ecotones forces them to occur sympatrically and compete for resources. The parapatric pattern is a result of the evolutionary history of the species and of strong competitive interactions between them, which form 'competitive‐exclusion barriers'. However, these barriers are environmentally dependent, so changes in the environment could rapidly affect the limits of the species distribution. [ABSTRACT FROM AUTHOR]

Aim: As climate change presents a major threat to biodiversity in the next decades, it is critical to assess its impact on species habitat suitability to inform biodiversity conservation. Species distribution models (SDMs) are a widely used tool to assess climate change impacts on species' geographical distributions. As the name of these models suggests, the species level is the most commonly used taxonomic unit in SDMs. However, recently it has been demonstrated that SDMs considering taxonomic resolution below (or above) the species level can make more reliable predictions of biodiversity change when different populations exhibit local adaptation. Here, we tested this idea using the Japanese crayfish (Cambaroides japonicus), a threatened species encompassing two geographically structured and phylogenetically distinct genetic lineages. Location: Northern Japan. Methods: We first estimated niche differentiation between the two lineages of C. japonicus using n‐dimensional hypervolumes and then made climate change predictions of habitat suitability using SDMs constructed at two phylogenetic levels: species and intraspecific lineage. Results: Our results showed only intermediate niche overlap, demonstrating measurable niche differences between the two lineages. The species‐level SDM made future predictions that predicted much broader and severe impacts of climate change. However, the lineage‐level SDMs led to reduced climate change impacts overall and also suggested that the eastern lineage may be more resilient to climate change than the western one. Main conclusions: The two lineages of C. japonicus occupy different niche spaces. Compared with lineage‐level models, species‐level models can overestimate climate change impacts. These results not only have important implications for designing future conservation strategies for this threatened species, but also highlight the need for incorporating genetic information into SDMs to obtain realistic predictions of biodiversity change. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the potential of models based on opportunistic reporting (OR) compared to models based on data from a systematic protocol (SP) for modelling species distributions. We compared model performance for eight forest bird species with contrasting spatial distributions, habitat requirements and rarity. Differences in the reporting of species were also assessed. Finally, we tested potential improvement of models when inferring high‐quality absences from OR based on questionnaires sent to observers. Location: Both datasets cover the same large area (Sweden) and time period (2000–2013). Methods: Species distributions were modelled using logistic regression. Predictive performance of OR models to predict SP data was assessed based on AUC. We quantified the congruence in spatial predictions using Spearman's rank correlation coefficient. We related these results to species characteristics and reporting behaviour of observers. We also assessed the gain in predictive performance of OR models by adding inferred absences. Finally, we investigated the potential impact of sampling bias in OR. Results: For all species, and despite the sampling biases, results from OR overall agreed well with those of SP, for the nationwide spatial congruence of habitat suitability maps and the selection and directions of species–environment relationships. The OR models also performed well in predicting the SP data. The predictive performance of the OR models increased with species rarity and even outperformed the SP model for the rarest species. No significant impact of observer behaviour was found. Main conclusions: Relatively simple analyses with inferred absences could produce reliable spatial predictions of habitat suitability. This was especially true for rare species. OR data should be seen as a complement to SP, as the weakness of one is the strength of the other, and OR may be especially useful at large spatial scales or where no systematic data collection protocols exist. [ABSTRACT FROM AUTHOR]

Aim: Estimating species richness from a series of samples is an important and widely debated issue in ecology and biodiversity conservation. Numerous tests of respective richness estimators gave insights into the precision, the limitations and the pitfalls of richness forecasting. However, few benchmark tests used almost complete empiric census data obtained at those spatial scales where richness estimation is most useful for conservation management. Location: Japan. Methods: We use an extraordinary dataset on the spatial distribution of Japanese plants containing complete information on the occurrence of each Japanese plant species at the 10 × 10 km2 grid cell level. We link the estimates of four estimators representing different theoretical approaches, Chao2, rarefaction, species–area relationships (SAR) and species abundance distributions (SAD), to environmental data using a fully nested sampling design. Results: Chao2 and rarefaction behaved very similar in all tests and significantly underestimated true richness below 40% sampling fraction. SAR and SAD were less precise than Chao2 and rarefaction at higher sampling fraction but also less affected by low sample size. In general, SAD provided robust estimates over the whole range of sampling fraction and 67.4% of estimates ranged within the 10% error level. Higher species spatial turnover increased and high evenness in occurrence decreased the precision of the SAD estimator. Precision of the four estimators was largely unaffected by environmental variability but increased with increasing latitude. Main conclusions: Our results strongly indicate that the pattern of Japanese plant species spatial distribution is sufficiently scale invariant for richness estimators to provide precise forecasting results at the country level. The simplest process to generate such a spatial distribution is ecological drift. [ABSTRACT FROM AUTHOR]

Aim: The boundaries of species distributions are often shaped by natural barriers, such as mountains and rivers, but species distribution models usually fail to include these constraints. We tested several approaches that include barriers as explanatory variables in species distribution models. Location: Africa and South America. Time period: Current. Major taxa studied: Primates. Methods: We modelled the ranges of pairs of species separated by a river, taking into account three explanatory components: the environment (ecosystems, topohydrography, climate and human pressure), the spatial structure shaped by history and population dynamics (using a trend‐surface approach) and rivers as naturals barriers to dispersal (using a binary cis–trans variable that describes both sides of the river). To assess how the addition of a spatial structure and the barrier could improve distribution models, we used a nested approach by comparing models based on: (a) the environment; (b) the environment and the spatial structure; and (c) the environment, the spatial structure and the river. These models were constructed using favourability functions. Results: There was a decreased occurrence of high‐favourability values on the opposite side of the rivers in models that included the spatial structure of distributions compared with models based on the environment alone. This decrease was more marked when the description of the spatial structure was made more flexible. However, model performance was significantly improved by the inclusion of cis–trans variables that identified areas on the opposite side as totally unfavourable. Main conclusions: The performance of distribution models can be improved by the use of approaches that describe barriers. Although adding the location of geographical units in relationship to a river appears to be the most accurate way to define the presence of a barrier, defining this variable may be challenging. A suitable alternative is to analyse the spatial structure of distributions using a flexible approach. [ABSTRACT FROM AUTHOR]

Aim: Sample size and species characteristics, including prevalence and habitat specialization, can influence the predictive performance of species distribution models (SDMs). There is little agreement, however, on which metric of model performance to use. Here, we directly compare AUC and partial ROC as metrics of SDM performance through analyses on the effects of species traits and sample size on SDM performance. Location: Three counties dominated by agricultural lands and coniferous forest in Oregon's Willamette Valley and Coast Range ecoregions. Methods: We systematically reduced a large avian point count dataset to alter sample sizes of 22 species of songbird. We used boosted regression trees to run SDMs for each species, quantified habitat specialization, and used mixed effects models to compare the influence of sample size, prevalence, and habitat specialization on SDM performance, calculated as AUC and partial ROC, across species. We calculated AUC and partial ROC with subset and independent evaluation data separately to more comprehensively investigate differences in metrics. Results: We found a positive quadratic effect of sample size and a strongly positive effect of habitat specialization on both metrics of model performance. We found a weak effect of prevalence on partial ROC and no effect in AUC. Contrary to expectations, when evaluated with a subset evaluation data, partial ROC was consistently highest in models with the smallest sample sizes. These small sample size models had correspondingly small sample sizes in subset evaluation datasets. Partial ROC evaluated with independent data and AUC evaluated with subset or independent data showed the expected positive correlation between sample size and model performance. Main Conclusions: We found that small evaluation datasets can artificially inflate partial ROC. With literature recommended minimum SDM sample sizes as low as three, attention must be given to the effects of correspondingly low sample sizes in evaluation datasets. [ABSTRACT FROM AUTHOR]

The fin whale (Balaenoptera physalus) is a cosmopolitan species with a resident population in the Mediterranean Sea. Due to its habitat, open seas often far from ports and airfields, and its long-distance migratory behaviour, studying and monitoring its distribution is costly. Currently, many opportunistic sightings (OS) reports are available, which provide a source of potentially useful, low-cost information about the spatio-temporal distribution of this species. Since 1993, the Spanish Institute of Oceanography has compiled a dataset comprising 874 records of OS of nine species of cetaceans in the western Mediterranean Sea and adjacent waters. The aim of this study was to use this dataset to investigate the differential use of these waters by the fin whale when compared with other cetaceans. We compared the presence of fin whales with the presence of any other cetacean species in the dataset. Binary logistic regression was then used to model these occurrences according to several spatio-temporal variables expected to reflect their habitat use. Several significant models reveal that fin whales are more prone than other cetaceans to use the waters over the slope of the Gulf of Lion in summer. This finding confirms that the Gulf of Lion is an area of importance for this species and suggests that the slope of the continental shelf could be particularly important. Our study shows how OS can be a source of useful information when appropriately analyzed. [ABSTRACT FROM AUTHOR]

The evaluation of protected area networks on their capacity to preserve species distributions is a key topic in conservation biology. There are different types of protected areas, with National Parks those with highest level of protection. National Parks can be declared attending to many ecological features that include the presence of certain animal species. Here, we selected 37 vertebrate species that were highlighted as having relevant natural value for at least one of the 10 National Parks of mainland Spain. We modelled species distributions with the favourability function, and applied the Insecurity Index to detect the degree of protection of favourable areas for each species. Two metrics of Insecurity Index were defined for each species: the Insecurity Index in each of the cells, and the Overall Insecurity Index of a species. The former allows the identification of insecure areas for each species that can be used to establish spatial conservation priorities. The latter gives a value of Insecurity for each species, which we used to calculate the Representativeness of favourable areas for the species in the network. As expected, due to the limited extension of the National Park network, all species have high values of Insecurity; i.e., just a narrow proportion of their favourable areas are covered by a National Park. However, the majority of species favourable areas are well represented in the network, i.e., the percentage of favourable areas covered by the National Park network is higher than the percentage of mainland Spain covered by the network (result also supported by a randomization approach). Even if a reserve network only covers a low percentage of a country, the Overall Insecurity Index allows an objective assessment of its capacity to represent species. Beyond the results presented here, the Insecurity Index has the potential to be extrapolated to other areas and to cover a wide range of species. [ABSTRACT FROM AUTHOR]

An accurate estimation of the expected consequences of climate change requires the proper quantification of the effect of climate on current species distributions. Several interrelated sources of uncertainty may affect the likelihood of species distribution models (SDMs) to determine the relative importance of climate. Our aim was to assess the relationship between the influence of geographical background (GB) delimitation and that of subtracting the non-climate effects from the weight of climatic predictors to estimate the combined influence of these two factors on predictions in climate change scenarios. The distribution of 40 endemic mammals in Western Europe have been modeled by (i) using the whole territory of Western Europe as the GB and also specifically delimiting the GB with a geographical criterion and (ii) considering climatic predictors in addition to other non-climatic variables in order to extract the pure effect of climate. The models were used to quantify species' sensitivity to new climate scenarios. Results showed discrepancies among the analytical approaches. Changes in distribution obtained by considering the pure effect of climate were lower than those obtained by considering the apparent effect, and GB-delimited models yielded higher changes than those trained in Western Europe. We evidence that climate weighting and GB delimitation have dramatic influences on the projections of models when transferred to new scenarios. We emphasize that scientific studies and derived adaptation policies based on SDMs without an explicit consideration of the GB and the weighting of the climate-related variables may be misleading and in need of revision. [ABSTRACT FROM AUTHOR]

Species distributions are typically represented by records of their observed occurrence at a given spatial and temporal scale. Such records are inevitably incomplete and contingent on the spatial-temporal circumstances under which the observations were made. Moreover, organismsmay respond differently to similar environmental conditions at different places or moments, so their distribution is, in principle, not completely predictable. We argue that this uncertainty exists, and warrants considering species distributions as analogous to coherent quantum objects, whose distributions are better described by a wavefunction rather than by a set of locations. We use this to extend the existing concept of "dark diversity", which incorporates into biodiversity metrics those species that could, but which have not yet been observed to, inhabit a region--thereby developing the idea of "potential biodiversity". We show how conceptualizing species' distributions in this way could help overcome important weaknesses in current biodiversity metrics, both in theory and by using a worked case study of mammal distributions in Spain over the last decade. We propose that considerable theoretical advances could eventually be gained through interdisciplinary collaboration between biogeographers and quantum physicists. [ABSTRACT FROM AUTHOR]

Winters have become warmer under the impact of climate change, which has modified the phenology as well as the distribution ranges of birds. The African Long-legged Buzzard Buteo rufinus cirtensis has recently colonized Europe via the Strait of Gibraltar. We aim to explain the native distribution of this species and to predict favourable areas in newly colonized parts of Europe using geospatial modelling to identify the most influential factors in this process. We applied the favourability function, a generalized linear model describing environmental favourability, for the presence/absence of breeding areas in northern Morocco and the southern Iberian Peninsula, according to a set of variables describing climate, topography, human activity, vegetation and purely spatial trends. A model was built using some known breeding sites in northern Morocco, and was used to forecast future suitable breeding areas in Europe. A second model was built with the available data for northern Morocco and Europe to explain the current distribution of breeding sites. Both models were assessed according to discrimination, calibration and parsimony criteria, and the influence of each factor was analysed using variation partitioning. We conclude that the Iberian Peninsula could provide new suitable areas for the species and facilitate its northward expansion. This result, together with the increasing number of records available, suggests that this species could soon spread throughout Europe. Steady temperatures and abundant but seasonally distributed precipitation showed the strongest predictive power in the models. This indicates a close relationship between the species' distribution and climate in the study area, and suggests that this species finds its most favourable environments in the Mediterranean biome. Topography and vegetation, specifically cliffs and woods near hunting zones, point to a fine-scale habitat selection for breeding. As the case of the African Long-legged Buzzard is not a unique event, our results may be useful to determine whether a northward expansion of the Mediterranean biome could be followed by distribution shifts of bird species that have so far been restricted to Africa. [ABSTRACT FROM AUTHOR]

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Distribution models should take into account the different limiting factors that simultaneously influence species ranges. Species distribution models built with different explanatory variables can be combined into more comprehensive ones, but the resulting models should maximize complementarity and avoid redundancy. Our aim was to compare the different methods available for combining species distribution models. We modelled 19 threatened vertebrate species in mainland Spain, producing models according to three individual explanatory factors: spatial constraints, topography and climate, and human influence. We used five approaches for model combination: Bayesian inference, Akaike weight averaging, stepwise variable selection, updating, and fuzzy logic. We compared the performance of these approaches by assessing different aspects of their classification and discrimination capacity. We demonstrated that different approaches to model combination give rise to disparities in the model outputs. Bayesian integration was systematically affected by an error in the equations that are habitually used in distribution modelling. Akaike weights produced models that were driven by the best single factor and therefore failed at combining the models effectively. The updating and the stepwise approaches shared recalibration as the basic concept for model combination, were very similar in their performance, and showed the highest sensitivity and discrimination capacity. The fuzzy-logic approach yielded models with the highest classification capacity according to Cohen's kappa. In conclusion: 1) Bayesian integration, employing the currently used equation, and the Akaike weight procedure should be avoided; 2) the updating and stepwise approaches can be considered minor variants of the same recalibrating approach; and 3) there is a trade-off between this recalibrating approach, which has the highest sensitivity, and fuzzy logic, which has the highest overall classification capacity. Recalibration is better if unfavourable conditions in one environmental factor may be counterbalanced with favourable conditions in a different factor, otherwise fuzzy logic is better. [ABSTRACT FROM AUTHOR]

Aim The use of atlas data in combination with a variety of modelling approaches has become a common practice in species distribution studies. The spatial extent over which species distribution models ( SDMs) should be fitted (i.e. the spatial extent) is often arbitrary and coincides with the extent of the atlases. In order to develop reliable SDMs using species atlas data, we propose an approach that incorporates the taxonomy of species and therefore delineates the spatial extent for SDMs. Location Mainland Spain. Methods We used atlas data to generate taxonomically delineated datasets for 365 terrestrial species. The presence records in the datasets were identical to those in the atlas, while the absence records were delimited to the presence of at least one species in the same family or order. We also generated two randomly delineated datasets that were the same size as the taxonomically delineated datasets. We assessed the predictive performance of the SDMs specifically by studying the model calibration ( Miller's statistic) and discrimination capacity (area under the curve of the receiver operating characteristic plot), along with the geographical similarity pattern of the predicted maps. Results The models that were trained using the taxonomically delineated datasets produced significantly improved models in terms of calibration, while their discrimination capacity was no greater than that of the models trained using the atlas dataset. The improvements to the calibration of the taxonomically delineated datasets were significantly greater than those with random absence sets. Main conclusion Delineating the spatial extent using taxonomical information leads to a significant improvement in the model performance of SDMs. This restriction can reduce the effect of environmental events beyond the species history during model parameterization, thus allowing the models obtained to more precisely depict the potential distribution of the species. We therefore recommend considering the delineation of spatial extent using species taxonomy when atlas data are employed in SDMs. [ABSTRACT FROM AUTHOR]

Aim The way in which environmental conditions determine the distribution and abundance of species is a crucial topic in ecology, biogeography and conservation. It is especially important to understand the nature of this relationship regarding threatened species. The ability to forecast local densities over the geographic range of species provides a way to link population ecology and biogeography. Particularly, our aim was to test whether predictions derived from species distribution modelling data provide useful information on spatial patterns of abundance and, consequently, may act as surrogates for local density estimates. Location Andalusia (southern Spain). Methods Logistic regression and the favourability function were applied as modelling tools to presence-absence data to compare the predicted results with current abundance. This approach is useful in the identification of local priorities for the target species whenever broad-scale surveys need to be performed. Results The model included variables related to topography, vegetation and spatial location to explain the presence of Bonelli's eagle. A positive relationship was found between both probability and favourability and the density of this species, but a triangular fit only with favourability, suggesting that the physiological and ecological requirements of this species in the study area are better reflected in the favourability model. Main conclusions We suggest that favourability models derived from presence-absence data provide insights into abundance data and valuable information on carrying capacity over large scales. To minimize costs, maximize output and optimize results, priority could be given to detecting the presence of the species, instead of investing resources aimed at estimating abundance, which is more expensive. [ABSTRACT FROM AUTHOR]

The rapid ecological shifts that are occurring due to climate change present major challenges for managers and policymakers and, therefore, are one of the main concerns for environmental modelers and evolutionary biologists. Species distribution models ( SDM) are appropriate tools for assessing the relationship between species distribution and environmental conditions, so being customarily used to forecast the biogeographical response of species to climate change. A serious limitation of species distribution models when forecasting the effects of climate change is that they normally assume that species behavior and climatic tolerances will remain constant through time. In this study, we propose a new methodology, based on fuzzy logic, useful for incorporating the potential capacity of species to adapt to new conditions into species distribution models. Our results demonstrate that it is possible to include different behavioral responses of species when predicting the effects of climate change on species distribution. Favorability models offered in this study show two extremes: one considering that the species will not modify its present behavior, and another assuming that the species will take full advantage of the possibilities offered by an increase in environmental favorability. This methodology may mean a more realistic approach to the assessment of the consequences of global change on species' distribution and conservation. Overlooking the potential of species' phenotypical plasticity may under- or overestimate the predicted response of species to changes in environmental drivers and its effects on species distribution. Using this approach, we could reinforce the science behind conservation planning in the current situation of rapid climate change. [ABSTRACT FROM AUTHOR]

Aim To analyse a source of uncertainty when forecasting the possible effects of climate change on species distributions, which may appear when the species studied have doubtful taxonomy or are subdivided into subspecies with different environmental requirements. Location Mainland Spain. Methods Iberian newts ( Triturus pygmaeus and Triturus marmoratus, a recently identified species) were used as examples. Environmental favourability models were performed on the occurrence of the newts according to two taxonomic options: (1) the two species separately; and (2) the two species together, as they were considered a single species by taxonomists until recently. The models were projected to three time periods between 2011 and 2100 within a context of climate change, using two different general circulation models and two emission scenarios. We calculated the discrepancy between forecasts produced with the different taxonomic options and their consistency under the same climate change scenario. Results The model based on the two species together did not distinguish between particular environmental requirements of either of the two species. Discrepancy values between taxonomic options increased over time. A reduction in areas favourable to T. pygmaeus and its north-eastward displacement were only predicted when this species was analysed separately. Nevertheless, the uncertainty derived from taxonomic ambiguity barely affected the predictions for T. marmoratus. Main conclusions Qualitatively and quantitatively different distribution forecasts for two newt species in mainland Spain were obtained depending on the taxonomic option considered. Taxonomic uncertainty also affected other sources of uncertainty. Some guidelines are suggested to aid in similar cases. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To plan endangered species conservation and to design adequate management programmes, it is necessary to predict their distributional response to climate change, especially under the current situation of rapid change. However, these predictions are customarily done by relating de novo the distribution of the species with climatic conditions with no regard of previously available knowledge about the factors affecting the species distribution. We propose to take advantage of known species distribution models, but proceeding to update them with the variables yielded by climatic models before projecting them to the future. To exemplify our proposal, the availability of suitable habitat across Spain for the endangered Bonelli's Eagle (Aquila fasciata) was modelled by updating a pre-existing model based on current climate and topography to a combination of different general circulation models and Special Report on Emissions Scenarios. Our results suggested that the main threat for this endangered species would not be climate change, since all forecasting models show that its distribution will be maintained and increased in mainland Spain for all the XXI century. We remark on the importance of linking conservation biology with distribution modelling by updating existing models, frequently available for endangered species, considering all the known factors conditioning the species' distribution, instead of building new models that are based on climate change variables only. [ABSTRACT FROM AUTHOR]

Aim When faced with dichotomous events, such as the presence or absence of a species, discrimination capacity (the ability to separate the instances of presence from the instances of absence) is usually the only characteristic that is assessed in the evaluation of the performance of predictive models. Although neglected, calibration or reliability (how well the estimated probability of presence represents the observed proportion of presences) is another aspect of the performance of predictive models that provides important information. In this study, we explore how changes in the distribution of the probability of presence make discrimination capacity a context-dependent characteristic of models. For the first time, we explain the implications that ignoring the context dependence of discrimination can have in the interpretation of species distribution models. Innovation In this paper we corroborate that, under a uniform distribution of the estimated probability of presence, a well-calibrated model will not attain high discrimination power and the value of the area under the curve will be 0.83. Under non-uniform distributions of the probability of presence, simulations show that a well-calibrated model can attain a broad range of discrimination values. These results illustrate that discrimination is a context-dependent property, i.e. it gives information about the performance of a certain algorithm in a certain data population. Main conclusions In species distribution modelling, the discrimination capacity of a model is only meaningful for a certain species in a given geographic area and temporal snapshot. This is because the representativeness of the environmental domain changes with the geographical and temporal context, which unavoidably entails changes in the distribution of the probability of presence. Comparative studies that intend to generalize their results only based on the discrimination capacity of models may not be broadly extrapolated. Assessment of calibration is especially recommended when the models are intended to be transferred in time or space. [ABSTRACT FROM AUTHOR]

Climate is one of the main drivers of species distribution. However, as different environmental factors tend to co-vary, the effect of climate cannot be taken at face value, as it may be either inflated or obscured by other correlated factors. We used the favourability models of four species (Alytes dickhilleni, Vipera latasti, Aquila fasciata and Capra pyrenaica) inhabiting Spanish mountains as case studies to evaluate the relative contribution of climate in their forecasted favourability by using variation partitioning and weighting the effect of climate in relation to non-climatic factors. By calculating the pure effect of the climatic factor, the pure effects of non-climatic factors, the shared climatic effect and the proportion of the pure effect of the climatic factor in relation to its apparent effect (r), we assessed the apparent effect and the pure independent effect of climate. We then projected both types of effects when modelling the future favourability for each species and combination of AOGCM-SRES (two Atmosphere-Ocean General Circulation Models: CGCM2 and ECHAM4, and two Special Reports on Emission Scenarios (SRES): A2 and B2). The results show that the apparent effect of climate can be either inflated (overrated) or obscured (underrated) by other correlated factors. These differences were species-specific; the sum of favourable areas forecasted according to the pure climatic effect differed from that forecasted according to the apparent climatic effect by about 61% on average for one of the species analyzed, and by about 20% on average for each of the other species. The pure effect of future climate on species distributions can only be estimated by combining climate with other factors. Transferring the pure climatic effect and the apparent climatic effect to the future delimits the maximum and minimum favourable areas forecasted for each species in each climate change scenario. [ABSTRACT FROM AUTHOR]

Aim The extent of the study area (geographical background, GB) can strongly affect the results of species distribution models (SDMs), but as yet we lack objective and practicable criteria for delimiting the appropriate GB. We propose an approach to this problem using trend surface analysis (TSA) and provide an assessment of the effects of varying GB extent on the performance of SDMs for four species. Location Mainland Spain. Methods Using data for four well known wild ungulate species and different GBs delimited with a TSA, we assessed the effects of GB extent on the predictive performance of SDMs: specifically on model calibration (Miller's statistic) and discrimination (area under the curve of the receiver operating characteristic plot, AUC; sensitivity and specificity), and on the tendency of the models to predict environmental potential when they are projected beyond their training area. Results In the training area, discrimination significantly increased and calibration decreased as the GB was enlarged. In contrast, as GB was enlarged, both discriminatory power and calibration decreased when assessed in the core area of the species distributions. When models trained using small GBs were projected beyond their training area, they showed a tendency to predict higher environmental potential for the species than those models trained using large GBs. Main conclusions By restricting GB extent using a geographical criterion, model performance in the core area of the species distribution can be significantly improved. Large GBs make models demonstrate high discriminatory power but are barely informative. By delimiting GB using a geographical criterion, the effect of historical events on model parameterization may be reduced. Thus purely environmental models are obtained that, when projected onto a new scenario, depict the potential distribution of the species. We therefore recommend the use of TSA in geographically delimiting the GB for use in SDMs. [ABSTRACT FROM AUTHOR]

Aim The spatial resolution of species atlases and therefore resulting model predictions are often too coarse for local applications. Collecting distribution data at a finer resolution for large numbers of species requires a comprehensive sampling effort, making it impractical and expensive. This study outlines the incorporation of existing knowledge into a conventional approach to predict the distribution of Bonelli's eagle ( Aquila fasciata) at a resolution 100 times finer than available atlas data. Location Malaga province, Andalusia, southern Spain. Methods A Bayesian expert system was proposed to utilize the knowledge from distribution models to yield the probability of a species being recorded at a finer resolution (1 × 1 km) than the original atlas data (10 × 10 km). The recorded probability was then used as a weight vector to generate a sampling scheme from the species atlas to enhance the accuracy of the modelling procedure. The maximum entropy for species distribution modelling (MaxEnt) was used as the species distribution model. A comparison was made between the results of the MaxEnt using the enhanced and, the random sampling scheme, based on four groups of environmental variables: topographic, climatic, biological and anthropogenic. Results The models with the sampling scheme enhanced by an expert system had a higher discriminative capacity than the baseline models. The downscaled (i.e. finer scale) species distribution maps using a hybrid MaxEnt/expert system approach were more specific to the nest locations and were more contrasted than those of the baseline model. Main conclusions The proposed method is a feasible substitute for comprehensive field work. The approach developed in this study is applicable for predicting the distribution of Bonelli's eagle at a local scale from a national-level occurrence data set; however, the usefulness of this approach may be limited to well-known species. [ABSTRACT FROM AUTHOR]

We tested two approaches to forecast species distributions while balancing the impact of climate change against the inertia promoted by other influential factors that have been forecast as not changing. Given that mountain species are presumed to be more at risk due to climate warming, we selected an amphibian, a reptile, a bird, and a mammal species present in the Spanish mountains, to model their distributional response to climate change during this century. The climatic forecasts were made according to the general circulation models CGCM2 and ECHAM4 and to the A2 and B2 emission scenarios. We modelled the response of the species to spatial, topographic, human, and climatic variables separately. In our first approach, we compared each of these single-factor models using the Akaike Information Criterion, and produced a combined model weighting each factor (spatial, topographic, human, and climatic) according to Akaike weights. This procedure overestimated the best model, and the other factors were neglected in the combined model output. In our second approach, we produced a combined model using stepwise selection of the variables previously selected within each factor. In this way every factor was effectively represented in the combined explanatory model of the distributional response of the species to environmental conditions. This enabled the construction of models that combined climate with the other explanatory factors, to be later extrapolated to the future by replacing current climatic and human values with those expected from each emission and socio-economic scenario, while preserving spatial and topographic variables in the model. [ABSTRACT FROM AUTHOR]

In recent decades, Mediterranean landscapes have been experiencing more rapid changes in land use than usual, which have affected the ecology of the species inhabiting this biodiversity hotspot. Some studies have assessed the effect of such changes on biodiversity, but most of these were diachronic studies of population dynamics, or synchronic studies of species habitat selection, whereas few studies have simultaneously taken into account temporal changes in habitat composition and changes in species distribution. This study analysed the effects of land-use changes on the distribution of wild ungulates ( Capreolus capreolus, Capra pyrenaica, Cervus elaphus and Sus scrofa). Using favourability function and Markov chain analysis combined with cellular automata, we addressed the following objectives: (i) to examine the environmental determinants of ungulate distribution in the past (1960s) and present (1990s), (ii) to model land use for 2040 to forecast future species distributions and (iii) to assess the biogeographical differences between the above-mentioned study periods (past-present and present-future). Species favourability was predicted to be more widely distributed in the present than in the past, but this increase varied across species. Areas predicted to be favourable in the present should remain stable in the future, but in addition there will be more new favourable areas not previously occupied by these species. The results are discussed from the perspective of the socio-economic relevance of wild ungulates in relation to some unfavourable areas of Mediterranean regions. [ABSTRACT FROM AUTHOR]

Distribution models are used increasingly for species conservation assessments over extensive areas, but the spatial resolution of the modeled data and, consequently, of the predictions generated directly from these models are usually too coarse for local conservation applications. Comprehensive distribution data at finer spatial resolution, however, require a level of sampling that is impractical for most species and regions. Models can be downscaled to predict distribution at finer resolutions, but this increases uncertainty because the predictive ability of models is not necessarily consistent beyond their original scale. We analyzed the performance of downscaled, previously published models of environmental favorability (a generalized linear modeling technique) for a restricted endemic insectivore, the Iberian desman (Galemys pyrenaicus ), and a more widespread carnivore, the Eurasian otter (Lutra lutra ), in the Iberian Peninsula. The models, built from presence-absence data at 10 × 10 km resolution, were extrapolated to a resolution 100 times finer (1 × 1 km). We compared downscaled predictions of environmental quality for the two species with published data on local observations and on important conservation sites proposed by experts. Predictions were significantly related to observed presence or absence of species and to expert selection of sampling sites and important conservation sites. Our results suggest the potential usefulness of downscaled projections of environmental quality as a proxy for expensive and time-consuming field studies when the field studies are not feasible. This method may be valid for other similar species if coarse-resolution distribution data are available to define high-quality areas at a scale that is practical for the application of concrete conservation measures. [ABSTRACT FROM AUTHOR]

We compared the effect of general circulation models and greenhouse gas emission scenarios on the uncertainty associated with models predicting changes in areas favourable to animal species. Given that mountain species are particularly at risk due to climate warming, we selected one amphibian (Baetic midwife toad), one reptile (Lataste's viper), one bird (Bonelli's eagle), and one mammal (Iberian wild goat) present in Spanish mountains to model their distributional response to climate change during this century. Climate forecasts for the whole century were provided by the Agencia Estatal de Meteorología (AEMET; National Meteorological Agency) of Spain, which adapted the general circulation models CGCM2 and ECHAM4 and produced expected temperature and precipitation values for Spain according to the A2 and B2 emission scenarios. We constructed separate models of the species response to spatial, topographic, human, and climate variables using current values of the corresponding variables. We predicted future areas favourable to the species by replacing the current climate values with those expected according to each climate change scenario, while keeping spatial, topographic and human variables constant. Fuzzy logic was used to compute the coincidence between predictions for different emission scenarios in the same global circulation model, and the consistency between predictions for the same emission scenario applying different general circulation models. In general, coincidences were higher than consistencies and, thus, discrepancies between predictions were more attributable to uncertainty in global circulation models, i.e. our insufficient knowledge concerning the effect of the oceans and atmosphere on climate, than to the putative effect of different emission scenarios on future climates. Our conclusion is that species distribution models in climate warming scenarios are still not useful for informing emission policy planning, although they have great potential as tools once consistencies become higher than coincidences. [ABSTRACT FROM AUTHOR]

Transferring distribution models between different geographical areas may be problematic, as the performance of models outside their original scope is hard to predict. A modelling procedure is needed that gets the gist of the environmental descriptors of a distribution area, without either overfitting to the training data or overestimating the species’ distribution potential. We tested the transferability power of the favourability function, a generalized linear model, on the distribution of the Iberian desman (Galemys pyrenaicus) in the Iberian territories of Portugal and Spain. We also tested the effects of two of the main potential constraints on model transferability: the analysed ranges of the predictor variables, and the completeness of the species distribution data. We modelled 10km×10km presence/absence data from Portugal and Spain separately, extrapolated each model to the other country, and compared predictions with observations. The Spanish model, despite arguably containing more false absences, showed good predictive ability in Portugal. The Portuguese model, whose predictors ranged between only a subset of the values observed in Spain, overestimated desman distribution when transferred. We discuss possible reasons for this differential model behaviour, and highlight the importance of this kind of models for prediction and conservation applications. [Copyright &y& Elsevier]

Entropy is intrinsic to the geographical distribution of a biological species. A species distribution with higher entropy involves more uncertainty, i.e., is more gradually constrained by the environment. Species distribution modelling tries to yield models with low uncertainty but normally has to reduce uncertainty by increasing their complexity, which is detrimental for another desirable property of the models, parsimony. By modelling the distribution of 18 vertebrate species in mainland Spain, we show that entropy may be computed along the forward-backwards stepwise selection of variables in Logistic Regression Models to check whether uncertainty is reduced at each step. In general, a reduction of entropy was produced asymptotically at each step of the model. This asymptote could be used to distinguish the entropy attributable to the species distribution from that attributable to model misspecification. We discussed the use of fuzzy entropy for this end because it produces results that are commensurable between species and study areas. Using a stepwise approach and fuzzy entropy may be helpful to counterbalance the uncertainty and the complexity of the models. The model yielded at the step with the lowest fuzzy entropy combines the reduction of uncertainty with parsimony, which results in high efficiency. [ABSTRACT FROM AUTHOR]

Simple Summary: We tested the usefulness of new analytical tools such as fuzzy set theory to reveal the hidden internal complexity of the geographic distribution of species. Fuzzy set theory replaces the crisp notion of presence/absence of a species, typical of species distribution atlases, with the fuzzy notion of favorability for the species occurrence. Species distribution ranges are then revealed as more complex than recorded presences suggest, and metapopulation theory, which predicts fragmented favorable patches with connectivity among them, can be operationally analyzed. We identified the favorable patches for 222 butterfly species in the Iberian Peninsula using high values of favorability. We calculated the cost of reaching any part of the territory from a favorable patch using low values of favorability and distance and computed the inverse as connectivity. Some of the favorable territories can be vacant patches but also belong to the metapopulation structure, as they may be recolonized. This information is relevant for territory management and biodiversity conservation, serving to justify the protection of new areas or the modification of contours of reserves based on the role they play for the populations of interest. Metapopulation theory considers that the populations of many species are fragmented into patches connected by the migration of individuals through an interterritorial matrix. We applied fuzzy set theory and environmental favorability (F) functions to reveal the metapopulational structure of the 222 butterfly species in the Iberian Peninsula. We used the sets of contiguous grid cells with high favorability (F ≥ 0.8), to identify the favorable patches for each species. We superimposed the known occurrence data to reveal the occupied and empty favorable patches, as unoccupied patches are functional in a metapopulation dynamics analysis. We analyzed the connectivity between patches of each metapopulation by focusing on the territory of intermediate and low favorability for the species (F < 0.8). The friction that each cell opposes to the passage of individuals was computed as 1-F. We used the r.cost function of QGIS to calculate the cost of reaching each cell from a favorable patch. The inverse of the cost was computed as connectivity. Only 126 species can be considered to have a metapopulation structure. These metapopulation structures are part of the dark biodiversity of butterflies because their identification is not evident from the observation of the occurrence data but was revealed using favorability functions. [ABSTRACT FROM AUTHOR]

The recent modification of species distribution ranges in response to a warmer climate has constituted a major and generalized biogeographic change. The main driver of the shift in distribution is the disequilibrium of the species ranges with their climatic favourability. Most species distribution modelling approaches assume equilibrium of the distribution with the environment, which hinders their applicability to the analysis of this change. Using fuzzy set theory we assessed the response to climate change of a historically African species, the Atlas Long-legged Buzzard. With this approach we were able to quantify that the Buzzard's distribution is in a latitudinal disequilibrium of the species distribution with the current climate of 4 km, which is driving the species range northwards at a speed of around 1.3 km/year, i.e., it takes 3 years for the species to occupy new climatically favourable areas. This speed is expected to decelerate to 0.5 km/year in 2060–2080. [ABSTRACT FROM AUTHOR]

• Few practical guidelines exist on the best guidelines of ecological niche models. • We present a step by step guideline with best practices for correlative models. • We focus on data preparation; model calculation, evaluation, and model application. • This guideline will help to obtain better results when using correlative models. The use of correlative ecological niche models has highly increased in the last decade. Despite all literature and textbooks in this field, few practical guidelines exist on the correct application of these techniques. We present here a step-by-step guideline explaining best practices for calculating correlative ecological niche models considering their conceptual and statistical assumptions and limitations. We divided the modelling process into four stages: 1) data collection and preparation; 2) model calculation; 3) model evaluation and validation; 4) and model application. Based on ecological niche theory, we review the concepts of ecological niche and how they can be modelled; classes of correlative models; modelling software; selection of study area; data sources for species records and environmental variables; types of species records and their influence on correlative models; errors in species records; minimum number of species records and environmental variables; effects of prevalence, sampling design, biases, and collinearity between variables; model calculation; model projection to different scenarios in time and space; ensemble modelling; model validation; classification, discrimination and calibration metrics; calculation of null models; analysis of model results; and model thresholding. This guideline is expected to help potential users to obtain better results when using correlative ecological niche models. [ABSTRACT FROM AUTHOR]

Physics and ecology focus on different domains of nature and have developed under distinct scientific paradigms. Still, both share critical features, such as dealing with systems of irreducible complexity and inherent uncertainty at a fundamental level. Physics has embraced such complexity earlier and has devised robust analytical approaches to describe general principles of its systems, a path that ecosystem ecology has tracked, but organism-based ecology has only started to. Here, we outline approaches from physics – from classical to quantum mechanics – to address ecological questions that deal with emergent patterns of biodiversity, such as species' distribution, niche, and trait variation, which are of particular interest to community ecology, biogeography, and macroecology. These approaches can be further extended, which would provide these fields with a rationale common to other scientific fields within and outside ecology. [ABSTRACT FROM AUTHOR]