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Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real‐world applications and model assessments. Prevalent threshold selection methods for presence‐only data require unproven pseudo‐absence data or subjective researchers' decisions. This study proposes a new method, Boyce‐Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo‐absence data. We summarize that the mutation point is a typical shape feature of the predicted‐to‐expected (P/E) curve after reviewing relevant articles. Analysis based on source‐sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point. To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods. [ABSTRACT FROM AUTHOR]

Aim: Spatial sampling bias (SSB) is a feature of opportunistically sampled species records. Species distribution models (SDMs) built using these data (i.e. presence‐background models) can produce biased predictions of suitability across geographic space, confounding species occurrence with the distribution of sampling effort. A wide range of SSB correction methods have been developed but simulations suggest effects on predictive performance are highly variable. Here, we aim to identify the SSB correction methods that have the highest likelihood of improving true predictive performance and evaluation strategies that provide a reliable indicator of model performance when independent test data are unavailable. Location: Global, simulation. Time Period: Current, simulation. Methods: A meta‐analysis was used to evaluate the performance of SSB correction methods in studies where there were direct comparisons between corrected and uncorrected SDMs. A simulation model was then developed to test evaluation strategies against a known truth using four common SSB correction methods. Results: Effect sizes from published studies suggest some support for small positive effects of SSB correction on predictive performance when assessed using independent test data, but this was not evident using internal cross‐validation and no single method stood out as consistently effective. Simulations support these findings and show that evaluation using internal test data was generally a poor indicator of the true effect of SSB correction. Methods that adjust models relative to a known driver of SSB produced the largest performance gains, but were also the most inconsistent. Main Conclusions: Correcting SSB in presence‐background SDMs without independent test data to evaluate the effect on model performance requires careful implementation. We recommend clearer documentation of SSB correction effects on SDMs, presenting results from models with and without correction, evaluating effects of different assumptions of SSB implementation on predictions, as well as greater efforts to collect independent test datasets to validate model predictions. [ABSTRACT FROM AUTHOR]

Abstract Selecting thresholds to convert continuous predictions of species distribution models proves critical for many real‐world applications and model assessments. Prevalent threshold selection methods for presence‐only data require unproven pseudo‐absence data or subjective researchers' decisions. This study proposes a new method, Boyce‐Threshold Quantile Regression (BTQR), to determine thresholds objectively without pseudo‐absence data. We summarize that the mutation point is a typical shape feature of the predicted‐to‐expected (P/E) curve after reviewing relevant articles. Analysis based on source‐sink theory suggests that this mutation point may represent a transition in habitat types and serve as an appropriate threshold. Threshold regression is introduced to accurately locate the mutation point. To validate the effectiveness of BTQR, we used four virtual species of varying prevalence and a real species with reliable distribution data. Six different species distribution models were employed to generate continuous suitability predictions. BTQR and nine other traditional methods transformed these continuous outputs into binary results. Comparative experiments show that BTQR has advantages in terms of accuracy, applicability, and consistency over the existing methods.

Species distribution modeling is a widely used technique for estimating the potential habitats of target organisms based on their environmental preferences. These methods serve as valuable tools for resource managers and conservationists, and their utilization is increasing, particularly in marine environments where data limitations persist as a challenge. In this study, we employed the global distribution predictions of six cold-water coral species as a case study to investigate various factors influencing predictions, including modeling algorithms, background points sampling strategies and sizes, and the collinearity of environmental datasets, using both discriminative and functional performance metrics. The choice of background sampling method exhibits a stronger influence on model performance compared to the effects of modeling algorithms, background point sampling size, and the collinearity of the environmental dataset. Predictions that utilize kernel density backgrounds, maintain an equal number of presences and background points for algorithms of BRT, RF, and MARS, and employ a substantial number of background points for MAXENT, coupled with a collinearity-filtered environmental dataset in species distribution modeling, yield higher levels of discriminative and functional performance. Overall, BRT and RF outperformed MAXENT, a conclusion that is further substantiated by the analysis of smoothed residuals and the uncertainty associated with the predicted habitat suitability of Madrepora oculata. This study offers valuable insights for enhancing species distribution modeling in marine benthic environments, thereby benefiting resource management and conservation strategies for benthic species. [ABSTRACT FROM AUTHOR]

Biodiversity community science projects are growing rapidly in popularity. The enormous amounts of data generated by these programs are transforming how we conduct ecological research and conservation management. However, as with other biodiversity surveys, community science datasets suffer from biases in time and locations of observations. To better use these data, we modeled the spatial biases present in the popular community science platform, iNaturalist. iNaturalist uses crowdsourcing to collect georeferenced and time‐stamped observations of all taxa worldwide. With its wealth of biodiversity data, iNaturalist is now being used to answer a broad range of questions in ecology and conservation, but little is known about the platform's spatial biases. We focus on the more than 1.75 million iNaturalist observations available (as of December 2021) from British Columbia, Canada, a region with a strong community science presence and diversity of ecosystems. Using machine learning and species distribution modeling, we examined which landscape factors (e.g., protected areas, roads, human population density, habitat zones, elevation) were most important in determining where observations are taken, and we created a predicted probability map revealing how likely different regions are to be sampled by community scientists. We found strong road biases for observations in iNaturalist, with over 94% of observations within 1 km of roads. In addition, human population density and broad habitat ecosystem zones played a large role in predicting where iNaturalist observations occur across the landscape. These methods demonstrate tools for modeling the effects of spatial biases in large opportunistic datasets that can then be used to produce more accurate species distribution and biodiversity models from community science data. [ABSTRACT FROM AUTHOR]

There is increasing availability and use of unstructured and semi‐structured citizen science data in biodiversity research and conservation. This expansion of a rich source of 'big data' has sparked numerous research directions, driving the development of analytical approaches that account for the complex observation processes in these datasets.We review outstanding challenges in the analysis of citizen science data for biodiversity monitoring. For many of these challenges, the potential impact on ecological inference is unknown. Further research can document the impact and explore ways to address it. In addition to outlining research directions, describing these challenges may be useful in considering the design of future citizen science projects or additions to existing projects.We outline challenges for biodiversity monitoring using citizen science data in four partially overlapping categories: challenges that arise as a result of (a) observer behaviour; (b) data structures; (c) statistical models; and (d) communication. Potential solutions for these challenges are combinations of: (a) collecting additional data or metadata; (b) analytically combining different datasets; and (c) developing or refining statistical models.While there has been important progress to develop methods that tackle most of these challenges, there remain substantial gains in biodiversity monitoring and subsequent conservation actions that we believe will be possible by further research and development in these areas. The degree of challenge and opportunity that each of these presents varies substantially across different datasets, taxa and ecological questions. In some cases, a route forward to address these challenges is clear, while in other cases there is more scope for exploration and creativity. [ABSTRACT FROM AUTHOR]

Species distribution modeling (SDM) plays a crucial role in investigating habitat suitability and addressing various ecological issues. While likelihood analysis is commonly used to draw ecological conclusions, its statistical performance is not robust when confronted with slight deviations due to model misspecification in SDM. We proposed a new robust estimation method based on a novel divergence for the Poisson point process model. The method is characterized by weighting the log-likelihood equation to mitigate the impact of heterogeneous observations in the presence-only data, which can result from model misspecification. We demonstrated that our proposed method improved the predictive performance of maximum likelihood estimation in both simulation studies and the analysis of vascular plant data in Japan. • We introduced a robust method to address model misspecification in species distribution using thinned Poisson processes. • Simulation studies revealed bias in conventional estimates for mixed data, while our method provided appropriate estimates. • Our method improved prediction performance of the likelihood approach in analyzing presence-only data of vascular plants. [ABSTRACT FROM AUTHOR]

Aim: Large databases of species records such as those generated through citizen science projects, archives or museum collections are being used with increasing frequency in species distribution modelling (SDM) for conservation and land management. Despite the broad spatial and temporal coverage of the data, its application is often limited by the issue of sampling bias and consequently, zero inflation; there are more zeros (which are potentially 'false absences') in the data than expected. Here, we demonstrate how pooling species presence data into a 'pseudo‐abundance' count can allow identification and removal of sampling bias through the use of zero‐inflated (ZI) models, and thus solve a common SDM problem. Location: All locations Taxon: All taxa Methods: We present the results of a series of simulations based on hypothetical ecological scenarios of data collection using random and non‐random sampling strategies. Our simulations assume that the locations of occurrence records are known at a high spatial resolution, but that the absence of occurrence records may reflect under‐sampling. To simulate pooling of presence–absence or presence‐only data, we count occurrence records at intermediate and coarse spatial resolutions, and use ZI models to predict the counts (species abundance per grid cell) from environmental layers. Results: ZI models can successfully identify predictors of bias in species data and produce abundance prediction maps that are free from that bias. This phenomenon holds across multiple spatial scales, thereby presenting an advantage over presence‐only SDM methods such as binomial GLMs or MaxEnt, where information about species density is lost, and model performance declines at coarser scales. Main Conclusions: Our results highlight the value of converting presence–absence or presence‐only species data to 'pseudo‐abundance' and using ZI models to address the problem of sampling bias. This method has huge potential for ecological researchers when using large species datasets for research and conservation. [ABSTRACT FROM AUTHOR]

Aim: To evaluate the utility of opportunistic data from citizen science programmes for forecasting species distributions against forecasts with a model of individual‐based population dynamics. Location: Sweden. Methods: We evaluated whether alternative methods for building habitat suitability models (HSMs) based on opportunistic data from citizen science programmes produced forecasts that were consistent with forecasts from two benchmark models: (1) a HSM based on data from systematic monitoring and (2) an individual‐based model for spatially explicit population dynamics based on empirical demographic and movement data. We forecasted population numbers and habitat suitability for three realistic, future forest landscapes for a forest bird, the Siberian jay (Perisoreus infaustus). We ranked simulated forest landscapes with respect to their benefits to Siberian jays for each modelling method and compared the agreement of the rankings among methods. Results: Forecasts based on our two benchmark models were consistent with each other and with expectations based on the species' ecology. Forecasts from logistic regression models based on opportunistic data were consistent with the benchmark models if species detections were combined with high‐quality inferred absences derived via retrospective interviews with experienced "super‐reporters." In contrast, forecasts with three other widely used methods were inconsistent with the benchmark models, sometimes with misleading rankings of future scenarios. Main conclusions: Our critical evaluation of alternative HSMs against a spatially explicit IBM demonstrates that information on species absences critically improves forecasts of species distributions using opportunistic data from citizen science programmes. Moreover, high‐quality information on species absences can be retrospectively inferred from surveys of the consistency of reporting of individual species and the identification skills of participating reporters. We recommend that citizen science projects incorporate procedures to evaluate reporting behaviour. Inferred absences may be especially useful for improving forecasts for species and regions poorly covered by systematic monitoring schemes. [ABSTRACT FROM AUTHOR]

The current dearth of long‐term insect population trends is a major obstacle to conservation. Occupancy models have been proposed as a solution, but it remains unclear whether they can yield long‐term trends from natural history collections, since specimen records are normally very sparse. A common approach for sparse data is to coarsen its spatial and/or temporal resolution, although coarsening risks violating model assumptions.We (i) test whether occupancy trends of three social wasp (Hymenoptera: Vespidae: Vespinae) species – the common wasp (Vespula vulgaris), the German wasp (Vespula germanica) and the European hornet (Vespa crabro) – have changed in England between 1900 and 2016, and (ii) test the effect of spatiotemporal resolution on the performance of occupancy models using very sparse data. All models are based on an integrated dataset of occurrence records and natural history collection specimen records.We show that occupancy models can yield long‐term species‐specific trends from very sparse natural history collection specimens. We present the first quantitative trends for three Vespinae species in England over 116 years. Vespula vulgaris and V. germanica show stable trends over the time series, whilst V. crabro's occupancy decreased from 1950 to 1970 and increased since 1970. Moreover, we show that spatiotemporal resolution has little effect on model performance, although coarsening the spatial grain is an appropriate method for achieving enough records to estimate long‐term changes.With the increasing availability of biological records, the model formulation used here has the potential to provide novel insights by making use of natural history collections' unique specimen assemblages. [ABSTRACT FROM AUTHOR]

Citizen science, or community science, has emerged as a cost-efficient method to collect data for wildlife monitoring. To inform research and conservation, citizen science sampling designs should collect data that match the robust statistical analyses needed to quantify species and population patterns. Further increasing the contributions of citizen science, integrating citizen science data with other datasets and datatypes can improve population estimates and expand the spatiotemporal extent of inference. We demonstrate these points with a citizen science program called iSeeMammals developed in New York state in 2017 to supplement costly systematic spatial capture-recapture sampling by collecting opportunistic data from one-off observations, hikes, and camera traps. iSeeMammals has initially focused on the growing population of American black bear (Ursus americanus), with integrated analysis of iSeeMammals camera trap data with systematic data for a region with a growing bear population. The triumvirate of increased spatial and temporal coverage by at least twofold compared to systematic sampling, an 83% reduction in annual sampling costs, and improved density estimates when integrated with systematic data highlight the benefits of collecting presence-absence data in citizen science programs for estimating population patterns. Additional opportunities will come from applying presence-only data, which are oftentimes more prevalent than presence-absence data, to integrated models. Patterns in data submission and filtering also emphasize the importance of iteratively evaluating patterns in engagement, usability, and accessibility, especially focusing on younger adult and teenage demographics, to improve data quality and quantity. We explore how the development and use of integrated models may be paired with citizen science project design in order to facilitate repeated use of datasets in standalone and integrated analyses for supporting wildlife monitoring and informing conservation.

Aim: Integrated species distribution modelling has emerged as a useful tool for ecologists to exploit the range of information available on where species occur. In particular, the ability to combine large numbers of ad hoc or presence‐only (PO) records with more structured presence–absence (PA) data can allow ecologists to account for biases in PO data which often confound modelling efforts. A range of modelling techniques have been suggested to implement integrated species distribution models (IDMs) including joint likelihood models, including one dataset as a covariate or informative prior, and fitting a correlation structure between datasets. We aim to investigate the performance of different types of integrated models under realistic ecological data scenarios. Innovation: We use a virtual ecologist approach to investigate which integrated model is most advantageous under varying levels of spatial bias in PO data, sample size of PA data and spatial overlap between datasets. Main conclusions: Joint likelihood models were the best performing models when spatial bias in PO data was low, or could be modelled, but gave poor estimates when there were unknown biases in the data. Correlation models provided good model estimates even when there were unknown biases and when good quality PA data were spatially limited. Including PO data via an informative prior provided little improvement over modelling PA data alone and was inferior to using either the joint likelihood or correlation approach. Our results suggest that correlation models provide a robust alternative to joint likelihood models when covariates related to effort or detection in PO data are not available. Ecologists should be aware of the limitations of each approach and consider how well biases in the data can be modelled when deciding which type of IDM to use. [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]

Climate-only species distribution models are commonly used to evaluate a species' vulnerability to future climate change. However, these models may overestimate a species' vulnerability when occurrence data comes from only a subset of the species' historical range due to anthropogenic range contractions. This study investigated the impacts of using climate-only species distribution models with anthropogenically truncated occurrence data on future predictions, using the little-studied leopard cat (Prionailurus bengalensis) of mainland Tropical Asia as a case study. Like most other species in mainland Tropical Asia, the leopard cat's current distribution represents a non-random subset of its historical range due to the disproportionate loss of lowland forests in the region. We found that climate-only species distribution models trained on current occurrence data significantly overestimated the leopard cat's vulnerability to future climate change, predicting considerable range contractions by 2081–2100 due to rising summer temperatures. A similar range contraction was observed using a virtual species whose occurrence was solely predicated on extant forest cover and not climate. This suggested the leopard cat's apparent vulnerability to future climate change was driven by a spurious relationship with summer temperatures, as modern occurrence data primarily came from refugial forest cover in the region's cooler uplands rather than the warmer, human-dominated lowlands. This bias was not apparent, however, from our current predictions, which had high predictive accuracy and were spatially consistent with current knowledge of the species' distribution; an observation highlighting the inadequacy of predictive accuracy alone as a metric for selecting models in studies where transferability to novel environments is critical. We next considered two potential strategies for detecting and mitigating the effects of anthropogenically truncated occurrence data: 1) the use of hybrid models that incorporate both climatic and non-climatic variables, and 2) the use of climate-only models with historical datasets. Despite using different data, both proposed strategies produced spatially similar predictions of present and future leopard cat occurrence with little-to-no range contractions predicted. We thus recommend all species distribution modeling studies interested in assessing a species' vulnerability to climate change compare future predictions derived from climate-only and hybrid models as well as compare predictions from current and historical datasets, as differences in predictions can highlight the presence of potential biases and other limitations. Finally, we discuss our findings as they apply to both our focal species and to the overall field of species distribution modeling. [ABSTRACT FROM AUTHOR]

Large occurrence datasets provide a sizable resource for ecological analyses, but have substantial limitations. Phenological analyses in Fric et al. (2020) were misleading due to inadequate curation and improper statistics. Reanalysing 22 univoltine species with sufficient data for independent analysis, we found substantively different macroscale phenological patterns, including later onset at higher latitude for most species. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) have been increasingly used over the past decades to characterise the spatial distribution and the ecological niche of various taxa. Validating predicted species distribution is important, especially when producing broad-scale models (i.e. at continental or oceanic scale) based on limited and spatially aggregated presence-only records. In the present study, several model calibration methods are compared and guidelines are provided to perform relevant SDMs using a Southern Ocean marine species, the starfish Odontaster validus Koehler, 1906, as a case study. The effect of the spatial aggregation of presence-only records on modelling performance is evaluated and the relevance of a target-background sampling procedure to correct for this effect is assessed. The accuracy of model validation is estimated using k-fold random and spatial cross-validation procedures. Finally, we evaluate the relevance of the Multivariate Environmental Similarity Surface (MESS) index to identify areas in which SDMs accurately interpolate and conversely, areas in which models extrapolate outside the environmental range of occurrence records. Results show that the random cross-validation procedure (i.e. a widely applied method, for which training and test records are randomly selected in space) tends to over-estimate model performance when applied to spatially aggregated datasets. Spatial cross-validation procedures can compensate for this over-estimation effect but different spatial cross-validation procedures must be tested for their ability to reduce over-fitting while providing relevant validation scores. Model predictions show that SDM generalisation is limited when working with aggregated datasets at broad spatial scale. The MESS index calculated in our case study show that over half of the predicted area is highly uncertain due to extrapolation. Our work provides methodological guidelines to generate accurate model assessments at broad spatial scale when using limited and aggregated presence-only datasets. We highlight the importance of taking into account the presence of spatial aggregation in species records and using non-random cross-validation procedures. Evaluating the best calibration procedures and correcting for spatial biases should be considered ahead the modelling exercise to improve modelling relevance. [ABSTRACT FROM AUTHOR]

Aim: We present a novel strategy for species distribution models (SDMs) aimed at predicting the potential distributions of range‐expanding invasive non‐native species (INNS). The strategy combines two established perspectives on defining the background region for sampling "pseudo‐absences" that have hitherto only been applied separately. These are the accessible area, which accounts for dispersal constraints, and the area outside the environmental range of the species and therefore assumed to be unsuitable for the species. We tested an approach to combine these by fitting SDMs using background samples (pseudo‐absences) from both types of background. Location: Global. Taxon: Invasive non‐native plants: Humulus scandens, Lygodium japonicum, Lespedeza cuneata, Triadica sebifera, Cinnamomum camphora. Methods: Presence‐background (or presence‐only) SDMs were developed for the potential global distributions of five plant species native to Asia, invasive elsewhere and prioritised for risk assessment as emerging INNS in Europe. We compared models where the pseudo‐absences were selected from the accessible background, the unsuitable background (defined using biological knowledge of the species' key limiting factors) or from both types of background. Results: Combining the unsuitable and accessible backgrounds expanded the range of environments available for model fitting and caused biological knowledge about ecological unsuitability to influence the fitted species‐environment relationships. This improved the realism and accuracy of distribution projections globally and, generally, within the species' ranges. Main conclusions: Correlative SDMs remain valuable for INNS risk mapping and management, but are often criticised for a lack of biological underpinning. Our approach partly addresses this concern by using prior knowledge of species' requirements or tolerances to define the unsuitable background for modelling, while also accommodating dispersal constraints through considerations of accessibility. It can be implemented with current SDM software and results in more accurate and realistic distribution projections. As such, wider adoption has potential to improve SDMs that support INNS risk assessment. [ABSTRACT FROM AUTHOR]

The Mahalanobis distance is a statistical technique that can be used to measure how distant a point is from the centre of a multivariate normal distribution. By measuring Mahalanobis distances in environmental space ecologists have also used the technique to model: ecological niches, habitat suitability, species distributions, and resource selection functions. Unfortunately, the original description of the Mahalanobis distance technique for ecological modelling contained an error describing how Mahalanobis distances could be converted into probabilities using a chi-squared distribution. This error has been repeated in the literature, and is present in popular modelling software. In the hope of correcting this error to maximise the potential application of the Mahalanobis distance technique within the ecological modelling community, I explain how Mahalanobis distances are calculated, and through a virtual ecology experiment demonstrate how to correctly produce probabilities and discuss the implications of the error for previous Mahalanobis distance studies. [ABSTRACT FROM AUTHOR]

Spatial records of species are commonly misidentified, which can change the predicted distribution of a species obtained from a species distribution model (SDM). Experiments were undertaken to predict the distribution of real and simulated species using MaxEnt and presence-only data “contaminated” with varying rates of misidentification error. Additionally, the difference between the niche of the target and contaminating species was varied. The results show that species misidentification errors may act to contract or expand the predicted distribution of a species while shifting the predicted distribution towards that of the contaminating species. Furthermore the magnitude of the effects was positively related to the ecological distance between the species’ niches and the size of the error rates. Critically, the magnitude of the effects was substantial even when using small error rates, smaller than common average rates reported in the literature, which may go unnoticed while using a standard evaluation method, such as the area under the receiver operating characteristic curve. Finally, the effects outlined were shown to impact negatively on practical applications that use SDMs to identify priority areas, commonly selected for various purposes such as management. The results highlight that species misidentification should not be neglected in species distribution modeling.

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Abstract: The African elephant, Loxodonta africana, is under threat from habitat loss, poaching and human–elephant conflict. To mitigate for impact of habitat loss and reduce conflict, connectivity between elephant habitats can be improved through the protection of corridor areas. This study looks at elephant distribution and movement patterns within the Kasigau Wildlife Corridor (KWC) within the Tsavo Conservation Area in South‐east Kenya. Elephant presence data were obtained from observations by rangers during routine patrols across KWC, and were analysed in MaxEnt. The environmental factors predicting elephant distribution and density were tested, as well as the relationship between elephant maximum entropy and the presence and abundance of other wildlife. Seasonal variations in temperature and precipitation, plus presence of waterholes were found to play significant roles in elephant distribution across KWC. Higher elephant densities were not found to correlate with lower densities of other wildlife species; indeed, during the dry seasons, elephant presence was associated with greater wild herbivore densities. Besides illustrating the importance of the KWC for elephant conservation in the Tsavo ecosystem, both as a key corridor and habitat, this study also hopes to highlight the untapped utility of routine ranger patrol data, and encourage the use of such presence‐only data for deducing important knowledge for conservation of biodiversity. [ABSTRACT FROM AUTHOR]

Context. Research on species' habitat associations is strengthened if it combines coarse-grained landscape data with finer-scale parameters. However, due to the effort required to measure fine-scale parameters, studies on threatened species that unite these two scales remain relatively rare. Aim. This study aimed to undertake a multi-scale analysis of the habitat association of the threatened Petrogale lateralis (MacDonnell Ranges race) in the Anangu Pitjantjatjara Yankunytjatjara (APY) Lands, South Australia. Method. Analyses were conducted at four spatial scales: (1) across the Central Ranges IBRA Region (regional scale); (2) on hills in the APY Lands at which P. lateralis is extant and extinct (site scale); (3) at 'core' and 'non-core' areas within those hills (hillside scale); and (4) at rocky refuges. The maximum entropy approach through the software MaxEnt was used for the analysis at the regional scale. At the remaining scales, fieldwork was used to collect, and regression modelling to analyse, data. Key results. At the regional scale, presence was associated with slope and geology. At the site scale, aspect, rock abundance and habitat type are likely to have facilitated animal persistence at extant sites. At the hillside scale, the aspect, vegetation type and rock complexity of core areas are likely to have contributed to their higher use. Size, exposure and accessibility were significant predictors of the use of rocky refuges. Conclusions. All four spatial scales yielded novel information on the habitat associations of P. lateralis, supporting previous researchers' suggestions that habitat modelling should be conducted at multiple spatial scales. Implications. The study exemplifies the utility of combining MaxEnt modelling with fieldwork-derived data. The results may have conservation implications for this threatened race, and may also provide a model for other studies of faunal habitat associations. [ABSTRACT FROM AUTHOR]

This study evaluates the performances of the presence-only approach, Maximum Entropy, in assessing landslide triggering-thickness susceptibility within the Mili catchment (Sicily, Italy). This catchment underwent several meteorological stresses, resulting in hundreds of shallow rapid mass movements between 2007 and 2011. In particular, the area has become known for two disasters, which occurred in 2009 and 2010; the first weather system did not pass directly over the catchment; however, peak rainfall was registered over the basin during the second meteorological event. Field data were collected to associate the depth from the slope surface that material was mobilised at the triggering zone to each mass movement within the catchment. This information has been used to model the landslide susceptibility for two classes of processes, divided into shallow failures for maximum depths of 1 m and deep ones in case of values equal or greater than 1 m. Topographic attributes from a 2-m DEM were used as predictors, together with medium resolution vegetation indexes derived from ASTER scenes and geological, land use and tectonic maps. The presence-only approach discriminated between the two depth classes at the landslide trigger zone, producing excellent prediction skills associated with relatively low variances across a set of 50 randomly generated replicates. The role of each predictor was assessed to ascertain the significance to the final model output. This work uses simple field measurements to produce triggering-thickness susceptibility, which is a novel approach and may perform better as a proxy for landslide hazard assessments with respect to more common susceptibility practises. [ABSTRACT FROM AUTHOR]

Ensemble habitat selection modeling is becoming a popular approach among ecologists to answer different questions. Since we are still in the early stages of development and application of ensemble modeling, there remain many questions regarding performance and parameterization. One important gap, which this paper addresses, is how the number of background points used to train models influences the performance of the ensemble model. We used an empirical presence-only dataset and three different selections of background points to train scale-optimized habitat selection models using six modeling algorithms (GLM, GAM, MARS, ANN, Random Forest, and MaxEnt). We tested four ensemble models using different combinations of the component models: (a) equal numbers of background points and presences, (b) background points equaled ten times the number of presences, (c) 10,000 background points, and (d) optimized background points for each component model. Among regression-based approaches, MARS performed best when built with 10,000 background points. Among machine learning models, RF performed the best when built with equal presences and background points. Among the four ensemble models, AUC indicated that the best performing model was the ensemble with each component model including the optimized number of background points, while TSS increased as the number of background points models increased. We found that an ensemble of models, each trained with an optimal number of background points, outperformed ensembles of models trained with the same number of background points, although differences in performance were slight. When using a single modeling method, RF with equal number of presences and background points can perform better than an ensemble model, but the performance fluctuates when the number of background points is not properly selected. On the other hand, ensemble modeling provides consistently high accuracy regardless of background point sampling approach. Further, optimizing the number of background points for each component model within an ensemble model can provide the best model improvement. We suggest evaluating more models across multiple species to investigate how background point selection might affect ensemble models in different scenarios. [ABSTRACT FROM AUTHOR]

Context: The scale of environmental relationships is often inferred through the use of species distribution models. Yet such models are frequently developed at two distinct scales. Coarse-scale models typically use information-poor (e.g., presence-only) data to predict relative distributions across geographic ranges, whereas fine-scale models often use richer information (e.g., presence-absence data) to predict distributions at local to landscape scales. Objectives: We unite presence-absence and presence-only data to predict occurrence of species, what we refer to as integrated distribution models. We determine if integrated models improve predictions of species distributions and identification of characteristic spatial scales of environmental relationships relative to presence-absence modeling and ensemble modeling that averages predictions from separate presence-only and presence-absence models. Methods: We apply recent advances in integrated distribution models to predict Sherman's fox squirrel ( Sciurus niger shermani) distribution in north-central Florida. Presence-only data were collected through a citizen-science program across its geographic range, while presence-absence data were collected using camera trapping surveys across 40 landscapes. Results: Integrated models estimated environmental relationships with greater precision and identified larger characteristic scales for environmental relationships than using presence-absence data alone. In addition, integrated models tended to have greater predictive performance, which was more robust to the amount of presence-absence and presence-only data used in modeling, than presence-absence and ensemble models. Conclusions: Integrated distribution models hold much potential for improving our understanding of environmental relationships, the scales at which environmental relationships operate, and providing more accurate predictions of species distributions. Many avenues exist for further advancement of these modeling approaches. [ABSTRACT FROM AUTHOR]

Aim Given the paucity of data on the distribution of habitats and species for most marine species, particularly those that are rare and in need of protection, there is a need to model species distributions. Using the fan mussel, Atrina fragilis (Pennant 1777), as our case species, the aim of the study was to predict new areas of occurrence for A. fragilis, estimate the extent of potentially suitable areas, determine the proportion of these areas that are included in the recently designated nature conservation MPAs off the west of Scotland and identify possible environmental drivers in the distribution of A. fragilis. Location West coast of Scotland, UK. Methods Using a point process framework, we modelled presence-only data, including historic records. A quadrat survey employing digital still photography was conducted in areas of high and low suitability to verify the model, and subsequently, a targeted survey was undertaken in areas predicted as highly suitable by the models using towed video cameras. Results Five environmental variables were of prime importance in explaining the distribution of A. fragilis. The results from the verification survey support model performance. Atrina fragilis was found in 80% of the targeted transects undertaken. Approximately 14% of the total area predicted as suitable for A. fragilis occurred within recently designated marine protected areas ( MPAs) indicating considerable potential for recolonization given suitable protection. Main conclusions The verified model suggests that limited presence-only and historical records of rare species can perform well within a SDM framework allowing the identification of further suitable areas. The prominence of bathymetric ruggedness in the models is unexpected, given the understood ecological niche for A. fragilis and pinnids in general, but is consistent with the fact that seabed topography can offer protection from fishing pressure. These results will inform restoration objectives of the MPA network. [ABSTRACT FROM AUTHOR]

Presence-only data present challenges for selecting thresholds to transform species distribution modeling results into binary outputs. In this article, we compare two recently published threshold selection methods (max SSS and max Fpb) and examine the effectiveness of the threshold-based prevalence estimation approach. Six virtual species with varying prevalence were simulated within a real landscape in southeastern Australia. Presence-only models were built with DOMAIN, generalized linear model, Maxent, and Random Forest. Thresholds were selected with two methods max SSS and max Fpb with four presence-only datasets with different ratios of the number of known presences to the number of random points ( KP- RPratio). Sensitivity, specificity, true skill statistic, and F measure were used to evaluate the performance of the results. Species prevalence was estimated as the ratio of the number of predicted presences to the total number of points in the evaluation dataset. Thresholds selected with max Fpb varied as the KP- RPratio of the threshold selection datasets changed. Datasets with the KP- RPratio around 1 generally produced better results than scores distant from 1. Results produced by We conclude that maxFpb had specificity too low for very common species using Random Forest and Maxent models. In contrast, max SSS produced consistent results whichever dataset was used. The estimation of prevalence was almost always biased, and the bias was very large for DOMAIN and Random Forest predictions. We conclude that max Fpb is affected by the KP- RPratio of the threshold selection datasets, but max SSS is almost unaffected by this ratio. Unbiased estimations of prevalence are difficult to be determined using the threshold-based approach. [ABSTRACT FROM AUTHOR]

Spatial records of species are commonly misidentified, which can change the predicted distribution of a species obtained from a species distribution model (SDM). Experiments were undertaken to predict the distribution of real and simulated species using MaxEnt and presence-only data "contaminated" with varying rates of misidentification error. Additionally, the difference between the niche of the target and contaminating species was varied. The results show that species misidentification errors may act to contract or expand the predicted distribution of a species while shifting the predicted distribution towards that of the contaminating species. Furthermore the magnitude of the effects was positively related to the ecological distance between the species' niches and the size of the error rates. Critically, the magnitude of the effects was substantial even when using small error rates, smaller than common average rates reported in the literature, which may go unnoticed while using a standard evaluation method, such as the area under the receiver operating characteristic curve. Finally, the effects outlined were shown to impact negatively on practical applications that use SDMs to identify priority areas, commonly selected for various purposes such as management. The results highlight that species misidentification should not be neglected in species distribution modeling. [ABSTRACT FROM AUTHOR]

Presence-only records may provide data on the distributions of rare species, but commonly suffer from large, unknown biases due to their typically haphazard collection schemes. Presence-absence or count data collected in systematic, planned surveys are more reliable but typically less abundant., We proposed a probabilistic model to allow for joint analysis of presence-only and survey data to exploit their complementary strengths. Our method pools presence-only and presence-absence data for many species and maximizes a joint likelihood, simultaneously estimating and adjusting for the sampling bias affecting the presence-only data. By assuming that the sampling bias is the same for all species, we can borrow strength across species to efficiently estimate the bias and improve our inference from presence-only data., We evaluate our model's performance on data for 36 eucalypt species in south-eastern Australia. We find that presence-only records exhibit a strong sampling bias towards the coast and towards Sydney, the largest city. Our data-pooling technique substantially improves the out-of-sample predictive performance of our model when the amount of available presence-absence data for a given species is scarce, If we have only presence-only data and no presence-absence data for a given species, but both types of data for several other species that suffer from the same spatial sampling bias, then our method can obtain an unbiased estimate of the first species' geographic range. [ABSTRACT FROM AUTHOR]

Species distribution models ( SDMs) are used to inform a range of ecological, biogeographical and conservation applications. However, users often underestimate the strong links between data type, model output and suitability for end-use. We synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process (i.e. imperfect detection and sampling bias) determine the quantity that is estimated by a SDM. We then draw upon the published literature and simulations to illustrate and evaluate the information needs of the most common ecological, biogeographical and conservation applications of SDM outputs. We find that, while predictions of models fitted to the most commonly available observational data (presence records) suffice for some applications, others require estimates of occurrence probabilities, which are unattainable without reliable absence records. Our literature review and simulations reveal that, while converting continuous SDM outputs into categories of assumed presence or absence is common practice, it is seldom clearly justified by the application's objective and it usually degrades inference. Matching SDMs to the needs of particular applications is critical to avoid poor scientific inference and management outcomes. This paper aims to help modellers and users assess whether their intended SDM outputs are indeed fit for purpose. [ABSTRACT FROM AUTHOR]

There is an increasing demand for biodiversity mapping to address new challenges in the management of marine ecosystems. Species distribution models are a key tool in supplying part of this information. However, the use of these models in the marine environment is still developing and the reasons for the underlying use of different methodological approaches are not always clear. In this work, we compared four different statistical techniques: the ecological niche factor analysis (ENFA), the MAXimun ENTropy algorithm (MAXENT), general additive Models (GAMs), and Random Forest. ENFA and MAXENT were applied using presence-only data whereas GAM and Random Forest used presence-absence data. As a case study, we used four deep sea urchin species: Centrostephanus longispinus, Coelopleurus floridanus, Stylocidaris affinis, and Cidaris cidaris. The distribution of the studied sea urchins showed strong bathymetric segregation. Depth was the most important variable, followed by reflectivity and slope. The correlations between the predictive outputs of the models were similar between GAM, Random Forest and MAXENT, and lower for ENFA. Models using presence/absence data showed the highest scores in the four species, significantly outperforming ENFA in most of the cases, although differences with MAXENT were significant in only one species. [ABSTRACT FROM AUTHOR]

Thibaud et al. ( Methods in Ecology and Evolution 2014) present a framework for simulating species and evaluating the relative effects of factors affecting the predictions from species distribution models (SDMs). They demonstrate their approach by generating presence-absence data sets for different simulated species and analysing them using four modelling methods: three presence-absence methods and Maxent, which is a presence-background modelling tool. One of their results is striking: that their use of Maxent performs well in estimating occupancy probabilities and even outperforms the other methods on small sample sizes. This result is of concern to us, because it suggests that Maxent directly offers a useful alternative for modelling presence-absence data, which may prompt widespread adoption of this use of Maxent. In this paper, we explore why this would be a mistake., We draw on the theory underlying how the Maxent model operates and on simulations to discover: (i) why Maxent appears to fare as well as it does in their evaluation and (ii) why the best-suited presence-absence method for data analysis (the generating model; a GLM) does not perform as well as we would expect., We demonstrate that (i) the good performance observed for Maxent is largely a coincidence; the simulated species match well the arbitrary default parameter that Maxent applies to map its relative output into a 0-1 scale, but errors are much larger for other species we simulate; (ii) the performance of the GLM is poorer than expected because Thibaud et al. do not use model selection and fit a model that is too complex for the amount of data available., Maxent is a presence-background method and only provides estimates of relative suitability regardless of how the background sample is specified. When presence-absence data are available, one can transform Maxent's relative estimates into estimates of occupancy probability, and we provide methods to do so. However, this requires the user to post-process Maxent's output. Proper PA methods such as GLMs can perform well under small sample sizes, provided care is taken during modelling to avoid overfitting. We demonstrate an effective method using regularisation with the R package glmnet. [ABSTRACT FROM AUTHOR]

The global Aquatic Warbler ( Acrocephalus paludicola, Vieillot, 1817) population has suffered a major decline due to the large-scale destruction of its natural habitat (fen mires). The species is at risk of extinction, especially in NE Germany/NW Poland. In this study, we developed habitat suitability models based on satellite and environmental data to identify potential areas for habitat restoration on which further surveys and planning should be focused. To create a reliable model, we used all Aquatic Warbler presences in the study area since 1990 as well as additional potentially suitable habitats identified in the field. We combined the presence/absence regression tree algorithm Cubist with the presence-only algorithm Maxent since both commonly outperform other algorithms. To integrate the separate model results, we present a new way to create a metamodel using the initial model results as variables. Additionally, a histogram approach was applied to further reduce the final search area to the most promising sites. Accuracy increased when using both remote sensing and environmental data. It was highest for the integrated metamodel (Cohen's Kappa of 0.4, P < 0.001). The final result of this study supports the selection of the most promising sites for Aquatic Warbler habitat restoration. [ABSTRACT FROM AUTHOR]

A common sampling design in resource selection studies involves measuring resource attributes at sample units used by an animal and at sample units considered available for use. Few models can estimate the absolute probability of using a sample unit from such data, but such approaches are generally preferred over statistical methods that estimate a relative probability of use., The case-control model that allows for contaminated controls, proposed by Lancaster & Imbens (1996) and Lele (2009), can estimate the absolute probability of using a sample unit from use-availability data. However, numerous misconceptions have likely prevented the widespread application of this model to resource selection studies. We address common misconceptions regarding the case-control model with contaminated controls and demonstrate its ability to estimate the absolute probability of use, prevalence and parameters associated with categorical covariates from use-availability data., We fit the case-control model with contaminated controls to simulated data with varying prevalence (defined as the average probability of use across all sample units) and sample sizes ( n1 = 500 used and n a = 500 available samples; n1 = 1000 used and n a = 1000 available samples). We then applied this model to estimate the probability Ozark hellbenders ( Cryptobranchus alleganiensis bishopi) would use a location within a stream as a function of covariates., The case-control model with contaminated controls provided unbiased estimates of all parameters at N = 2000 sample size simulation scenarios, particularly at low prevalence. However, this model produced increasingly variable maximum likelihood estimates of parameters as prevalence increased, particularly at N = 1000 sample size scenarios. We thus recommend at least 500-1000 used samples when fitting the case-control model with contaminated controls to use-availability data. Our application to hellbender data revealed selection for locations with coarse substrate that are close to potential sources of cover., This study unites a disparate literature, addresses and clarifies many commonly held misconceptions and demonstrates that the case-control model with contaminated controls is a viable alternative for estimating the absolute probability of use from use-availability data. [ABSTRACT FROM AUTHOR]

Aim Species distribution models have been widely used to tackle ecological, evolutionary and conservation problems. Most species distribution modelling techniques produce continuous suitability predictions, but many real applications (e.g. reserve design, species invasion and climate change impact assessment) and model evaluations require binary outputs, and thresholds are needed for these transformations. Although there are many threshold selection methods for presence/absence data, it is unclear whether these are suitable for presence-only data. In this paper, we investigate mathematically and empirically which of the existing threshold selection methods can be used confidently with presence-only data. Location We used real spatially explicit environmental data derived from the western part of the state of Victoria, south-eastern Australia, and simulated species distributions within this area. Methods Thirteen existing threshold selection methods were investigated mathematically to see whether the same threshold can be produced using either presence/absence data or presence-only data. We further adopted a simulation approach, created many virtual species with differing prevalences in a real landscape in south-eastern Australia, generated data sets with different proportions of pseudo-absences, built eight types of models with four modelling techniques, and investigated the behaviours of four threshold selection methods in these situations. Results Three threshold selection methods were not affected by pseudo-absences, including max SSS (which is based on maximizing the sum of sensitivity and specificity), the prevalence of model training data and the mean predicted value of a set of random points. Max SSS produced higher sensitivity in most cases and higher true skill statistic and kappa in many cases than the other methods. The other methods produced different thresholds from presence-only data to those determined from presence/absence data. Main conclusions Max SSS is a promising method for threshold selection when only presence data are available. [ABSTRACT FROM AUTHOR]

Abstract: Detailed and reliable information about the spatial distribution of species provides critical information for effective conservation planning. In Victoria, Australia, this responsibility is held by the Department of Sustainability and Environment, which maintains and curates a database of site records for species: the Victorian Biodiversity Atlas. But the information is provided in point form, and therefore, it does not provide an adequate view of species distributions for all management purposes. By integrating known occurrences of species with environmental GIS data layers using a machine learning algorithm, random forest, we have built species distribution models for 523 vertebrate fauna species across the whole state, providing predictive ‘maps’ that are available for use in various conservation planning activities. In this paper, we introduce and discuss the methods we developed and implemented for producing these models. Specifically, 26 explanatory variables were used for the modelling; three versions of models were built using different sets of explanatory variables; pseudo-absences were chosen by filtering random points with a profile model; model accuracy was assessed using several measures, especially lift curve-related ones; a threshold was selected for each model to transform continuous result to binary one by maximizing the sum of sensitivity and (pseudo) specificity, which was proved to be valid for presence-only data. [Copyright &y& Elsevier]

The intra-seasonal adjustment in site selection, and hence in distribution of multi-brooded avian species, is a poorly investigated topic of potentially high relevance for conservation. This phenomenon may be especially important among multi-brooded species occurring in habitats undergoing large modifications. We used correlative species distribution models to carry out a spatially explicit assessment of variation in habitat suitability between early and late periods of the breeding season for four European farmland passerine species (woodlark Lullula arborea, skylark Alauda arvensis, cirl Emberiza cirlus and corn bunting E. calandra). We counted birds at transects (surveyed in early and late season) in Northern Apennines ( Lombardy, Italy), and modelled their spatial distribution with MaxEnt, separately for early and late season periods. A comparison of predicted environmental suitability ( ES) at occurrence sites showed significant differences between early and late period ES at both early and late occurrence sites. Three species showed differences in elevation between early and late occurrence sites. The proportion of 1-ha cells predicted as potentially suitable during the entire breeding season ranged from 29.3% (corn bunting) to 52.8% (woodlark) of the at least once suitable cells. In multi-brooded birds, a dynamic pattern of species occurrence may be likely and particularly important in farmland habitats along altitudinal gradients. The resulting within-season switches in habitat and distribution could be widespread among farmland birds and could have critical implications for their conservation. Moreover, such habitat switches provide an additional reason for preserving habitat heterogeneity. The intra-seasonal variation in habitat association/distribution should become a focal topic for researchers. [ABSTRACT FROM AUTHOR]

The article focuses on a research conducted in order to describe geographical distribution of the space-weaving spider, Chibchea salta (C. salta) in northwestern Argentina. It describes the climatic profile and the relative importance of the bioclimatic variables of C. salta. The study revealed the bioclimatic tolerance capacity of C. salta.

Abstract: Recent advances in ecological modeling have focused on novel methods for characterizing the environment that use presence-only data and machine-learning algorithms to predict the likelihood of species occurrence. These novel methods may have great potential for land suitability applications in the developing world where detailed land cover information is often unavailable or incomplete. This paper assesses the adaptation and application of the presence-only geographic species distribution model, MaxEnt, for agricultural crop suitability mapping in a rural Thailand where lowland paddy rice and upland field crops predominant. To assess this modeling approach, three independent crop presence datasets were used including a social-demographic survey of farm households, a remote sensing classification of land use/land cover, and ground control points, used for geodetic and thematic reference that vary in their geographic distribution and sample size. Disparate environmental data were integrated to characterize environmental settings across Nang Rong District, a region of approximately 1300sq. km in size. Results indicate that the MaxEnt model is capable of modeling crop suitability for upland and lowland crops, including rice varieties, although model results varied between datasets due to the high sensitivity of the model to the distribution of observed crop locations in geographic and environmental space. Accuracy assessments indicate that model outcomes were influenced by the sample size and the distribution of sample points in geographic and environmental space. The need for further research into accuracy assessments of presence-only models lacking true absence data is discussed. We conclude that the MaxEnt model can provide good estimates of crop suitability, but many areas need to be carefully scrutinized including geographic distribution of input data and assessment methods to ensure realistic modeling results. [Copyright &y& Elsevier]

Abstract: Species monitoring is one key approach to assess human impact on nature. Four main approaches have been applied in bird monitoring: comparing distribution atlases from different times, repeated monitoring programmes, checklist programmes, and species-specific surveys. We evaluate the potential of another type of data for monitoring: presence-only data on species sightings reported voluntarily by the public to open-access internet platforms, here the Species Gateway (SG) in Sweden. We regressed data on the 242 bird species observed 2001–2009 in the Swedish Bird Survey (SBS) against the corresponding SG data. We also investigated the relationship for groups of species utilizing different habitats and displaying a significant inter-annual variation, a coefficient of variation >0.3 according to the SBS. We found support for a positive relationship in inter-annual variation in population level between the two datasets. However, the species subsets displayed variation from positive to negative correlations. Restricting the analysis to species with high inter-annual variation increased the positive correlation between the data sets. The reason for the mismatch between the two datasets may be explained by temporal changes in the willingness to report certain common species to the SG. Another reason may be an imbalance in the coverage of common versus uncommon species among the two datasets. The use of voluntary citizen-based data requires great care and good knowledge of the limitation of the data. If these requirements are fulfilled, we suggest that they may be a complement to standardised programmes, especially for assessing uncommon species of conservation concern. [Copyright &y& Elsevier]

Statistical models are widely used for predicting species' geographic distributions and for analyzing species' responses to climatic and other predictor variables. Their predictive performance can be characterized in two complementary ways: discrimination, the ability to distinguish between occupied and unoccupied sites, and calibration, the extent to which a model correctly predicts conditional probability of presence. The most common measures of model performance, such as the area under the receiver operating characteristic curve (AUC), measure only discrimination. In contrast, we introduce a new tool for measuring model calibration: the presence-only calibration plot, or POC plot. This tool relies on presence-only evaluation data, which are more widely available than presence-absence evaluation data, to determine whether predictions are proportional to conditional probability of presence. We generalize the predicted/expected curves of Hirzel et al. to produce a presenceonly analogue of traditional (presence-absence) calibration curves. POC plots facilitate visual exploration .of model calibration, and can be used to recalibrate badly calibrated models. We demonstrate their use by recalibrating models made by the DOMAIN modeling method on a comprehensive set of 226 species from six regions of the world, significantly improving DOMAIN's predictive performance. [ABSTRACT FROM AUTHOR]