The uncertain nature of absences and their importance in species distribution modelling

Species distribution models (SDM) are commonly used to obtain hypotheses on either the realized or the potential distribution of species. The reliability and meaning of these hypotheses depends on the kind of absences included in the training data, the variables used as predictors and the methods employed to parameterize the models. Information about the absence of species from certain localities is usually lacking, so pseudo‐absences are often incorporated to the training data. We explore the effect of using different kinds of pseudo‐absences on SDM results. To do this, we use presence information on Aphodius bonvouloiri, a dung beetle species of well‐known distribution. We incorporate different types of pseudo‐absences to create different sets of training data that account for absences of methodological (i.e. false absences), contingent and environmental origin. We used these datasets to calibrate SDMs with GAMs as modelling technique and climatic variables as predictors, and compare these results with geographical representations of the potential and realized distribution of the species created independently. Our results confirm the importance of the kind of absences in determining the aspect of species distribution identified through SDM. Estimations of the potential distribution require absences located farther apart in the geographic and/or environmental space than estimations of the realized distribution. Methodological absences produce overall bad models, and absences that are too far from the presence points in either the environmental or the geographic space may not be informative, yielding important overestimations. GLMs and Artificial Neural Networks yielded similar results. Synthetic discrimination measures such as the Area Under the Receiver Characteristic Curve (AUC) must be interpreted with caution, as they can produce misleading comparative results. Instead, the joint examination of ommission and comission errors provides a better understanding of the reliability of SDM results.     

Population crash in an invasive species following the recovery of a native predator: the case of the American grey squirrel and the European pine marten in Ireland

In Ireland, the UK and Italy, the invasive North American grey squirrel, Sciurus carolinensis, threatens the survival of the Eurasian red squirrel, Sciurus vulgaris, as the effects of competition and disease almost inevitably lead to total replacement of red squirrel populations. However the results of a recent national squirrel survey suggested that the normally invasive grey squirrel had gone into decline in the Irish midlands, which was anecdotally attributed to an increase in European pine marten, Martes martes, range and numbers. This study aimed to quantify changes in squirrel distribution in Ireland and to investigate the role, if any, of the pine marten in red and grey squirrel population dynamics. A distribution survey of the midlands was carried out which confirmed the grey squirrel population has crashed in approximately 9,000 km² of its former range and the red squirrel is common after an absence of up to 30 years. At landscape level, pine marten and red squirrel abundance were positively correlated, whereas a strong negative correlation between pine marten and grey squirrel presence at woodland level was found to exist. Squirrel demographics were determined by means of live trapping programs which confirmed that the red squirrel in the midlands is now in competitive release and the grey squirrel is present at unusually low density. This study provides the first evidence of a regional grey squirrel population crash and suggests that European pine marten abundance may be a critical factor in the American grey squirrel’s success or failure as an invasive species.     

Without quality presence–absence data,discrimination metrics such as TSS can be misleading measures of model performance

The discriminating capacity (i.e. ability to correctly classify presences and absences) of species distribution models (SDMs) is commonly evaluated with metrics such as the area under the receiving operating characteristic curve (AUC), the Kappa statistic and the true skill statistic (TSS). AUC and Kappa have been repeatedly criticized, but TSS has fared relatively well since its introduction, mainly because it has been considered as independent of prevalence. In addition, discrimination metrics have been contested because they should be calculated on presence–absence data, but are often used on presence‐only or presence‐background data. Here, we investigate TSS and an alternative set of metrics—similarity indices, also known as F‐measures. We first show that even in ideal conditions (i.e. perfectly random presence–absence sampling), TSS can be misleading because of its dependence on prevalence, whereas similarity/F‐measures provide adequate estimations of model discrimination capacity. Second, we show that in real‐world situations where sample prevalence is different from true species prevalence (i.e. biased sampling or presence‐pseudoabsence), no discrimination capacity metric provides adequate estimation of model discrimination capacity, including metrics specifically designed for modelling with presence‐pseudoabsence data. Our conclusions are twofold. First, they unequivocally impel SDM users to understand the potential shortcomings of discrimination metrics when quality presence–absence data are lacking, and we recommend obtaining such data. Second, in the specific case of virtual species, which are increasingly used to develop and test SDM methodologies, we strongly recommend the use of similarity/F‐measures, which were not biased by prevalence, contrary to TSS.     

Integrating variability in detection probabilities when designing wildlife surveys: a case study of amphibians from south-eastern Australia

Occupancy-based monitoring programs rely on survey data to infer presence or absence of the target species. However, species may occupy a site and go undetected, leading to erroneous inference of absence (‘false absence’). If detectability is influenced by the time of year or weather conditions, survey protocols can be adjusted to minimize the chance of false absences. In this study, detection probabilities for three amphibian species from south-eastern Australia were modelled using a Bayesian approach. For aural surveys, we compared basic models, which only included effects of survey date, duration and time of day on detection, to models including additional effects of weather. Model selection using deviance information criterion (DIC) suggested that the basic model was the most parsimonious for Crinia signifera, while models including relative humidity and water temperature were most supported for Limnodynastes dumerilii and L. tasmaniensis respectively. When predictive performance was assessed by cross validation, DIC results were largely matched for C. signifera and L. dumerilii, while models of detection for L. tasmaniensis were indistinguishable, AUC scores suggesting inadequate performance. We show how results such as these can be used to design surveys, developing protocols for individual surveys and estimating the number of surveys required under those protocols to achieve a threshold cumulative probability of detection. Conservation managers can use these models to maximize the efficiency of surveys. This will improve the accuracy of occupancy data, and reduce the risk of misdirected conservation actions resulting from false absences.     

Morphological diversity of the red squirrel, <Emphasis Type="Italic">Sciurus vulgaris</Emphasis>, in Ireland

The red squirrel in Britain and Ireland has been described as a separate subspecies, Sciurus vulgaris leucourus, based on bleaching of the tail and ear tufts. However, recent investigations in northern England found this light colour confined to one area, probably due to the rapid spread of introduced continental European red squirrels. This study reports the first detailed survey of tail colour and cranial measurements in the Irish red squirrel population to (1) investigate the distribution of the light colour morph in Ireland and (2) determine whether the Irish red squirrel population is morphologically divergent from populations elsewhere in the species range. The light tail colour was found in 57% of individuals and in all regions, although it was most common in the northwest. The mixture of different colour morphs indicates the Irish population is a mixture of different subspecies, including S. vulgaris leucourus, while the cranial measurements suggest the Irish squirrel may be morphologically divergent from populations elsewhere. Combined, these results support previous suggestions that conservation measures seek to maintain the diversity within the Irish red squirrel population.     

Optimising camera trap surveys for the Carpentarian Pseudantechinus (Pseudantechinus mimulus) in northwest Queensland

The Carpentarian Pseudantechinus (Pseudantechinus mimulus) is a poorly studied dasyurid marsupial that inhabits rocky outcrops in the Mount Isa Inlier bioregion in Queensland and the Gulf Coastal and Gulf Fall and Uplands bioregions in the Northern Territory. It is readily detected by passive infrared triggered camera traps (‘camera traps’). Camera trap data can be used to develop detection probability estimates from which activity patterns can be inferred, but no effort has previously been made to determine changes in the detectability of P. mimulus throughout the year. We undertook a 13-month baited camera trap survey across nine sampling periods at 60 locations of known historic presence or nearby suitable habitat to assess the change in detection rates and detection probabilities of P. mimulus across a year. Detection probabilities were calculated from camera trap data within a single-species, multi-season occupancy framework to determine optimal survey timing. Detection probability data were used to calculate the likelihood of false absences to determine optimal survey duration. We recorded 2493 detections of P. mimulus over 10 966 camera days. Detection probability ranged from 0.009 to 0.179 and was significantly higher from April to October than from November to March. The likelihood of false absences varied by sampling period and desired level of confidence. We find that camera trap surveys for P. mimulus are best conducted from April to October, but optimal survey duration is dependent upon the time of year and desired level of confidence that an observed absence from a given site reflects a true absence at that site. Attaining a minimum of 80% confidence of absence requires as few as 9 days of survey effort in May to 16 days of survey effort in October.     

Modelling wildlife distributions: Logistic Multiple Regression vs Overlap Analysis

We compare the results, benefits and disadvantages of two techniques for modelling wildlife species distribution: Logistic Regression and Overlap Analysis. While Logistic Regression uses mathematic equations to correlate variables with presence/absence of the species. Overlap Analysis simply combine variables with the presence points, eliminating the non-explanatory variables and recombining the others. Both techniques were performed in a Geographic Information System and we attempted to minimise the spatial autocorrelation of data. The species used was the Schreiber's green lizard Lacerta schreiberi and the study area was Portugal, using 10 X 10 km UTM squares. Both techniques identified the same group of variables as the most important for explaining the distribution of the species. Both techniques gave high average correct classification rates for the squares with presence of the species (79% for Logistic Regression and 92% for Overlap Analysis). Correct absence classification was higher with Logistic Regression (73%) than with Overlap Analysis (32%), Overlap Analysis tends to maximise the potential area of occurrence of the species, which induces a reduced correct classification of absences, since many absences will fall in the potential area. This is because a single presence in a given class of a variable makes all the area of that class to be considered as potential. The technique does not consider that the species may occasionally occupy an unfavourable region. Although, in Logistic Regression, modelling procedures are more complex and time-consuming, the results are more statistically robust. Moreover. Logistic Regression has the capability of associating probability of occurrence to the potential area. Overlap Analysis is very simple in building procedures and swift in obtaining reliable potential areas. It is a valid technique especially in exploratory analysis of species distributions or in the initial stages of research when data may be scarce.     

Using Historical Atlas Data to Develop High-Resolution Distribution Models of Freshwater Fishes

Understanding the spatial pattern of species distributions is fundamental in biogeography, and conservation and resource management applications. Most species distribution models (SDMs) require or prefer species presence and absence data for adequate estimation of model parameters. However, observations with unreliable or unreported species absences dominate and limit the implementation of SDMs. Presence-only models generally yield less accurate predictions of species distribution, and make it difficult to incorporate spatial autocorrelation. The availability of large amounts of historical presence records for freshwater fishes of the United States provides an opportunity for deriving reliable absences from data reported as presence-only, when sampling was predominantly community-based. In this study, we used boosted regression trees (BRT), logistic regression, and MaxEnt models to assess the performance of a historical metacommunity database with inferred absences, for modeling fish distributions, investigating the effect of model choice and data properties thereby. With models of the distribution of 76 native, non-game fish species of varied traits and rarity attributes in four river basins across the United States, we show that model accuracy depends on data quality (e.g., sample size, location precision), species’ rarity, statistical modeling technique, and consideration of spatial autocorrelation. The cross-validation area under the receiver-operating-characteristic curve (AUC) tended to be high in the spatial presence-absence models at the highest level of resolution for species with large geographic ranges and small local populations. Prevalence affected training but not validation AUC. The key habitat predictors identified and the fish-habitat relationships evaluated through partial dependence plots corroborated most previous studies. The community-based SDM framework broadens our capability to model species distributions by innovatively removing the constraint of lack of species absence data, thus providing a robust prediction of distribution for stream fishes in other regions where historical data exist, and for other taxa (e.g., benthic macroinvertebrates, birds) usually observed by community-based sampling designs.     

利用分布有/无数据预测物种空间分布的研究方法综述

详细的物种地理分布信息是生态学研究和制定保护策略的基础。相比较于直接估测种群数量,获取物种分布的有/无数据更为实用。因此,利用分布有/无数据并结合环境变量建立模型预测物种空间分布的方法在近年来得到了长足发展,并被广泛应用。利用分布有/无数据预测物种分布,关键的步骤包括：1)构建总体概念模型,2)收集物种分布有/无数据,并准备环境变量图层;3)选择合适的统计模型和算法,以及4)对模型进行评估。概念模型提出研究假设,并确定数据收集及模型方法。收集物种分布数据有系统调查及非系统调查方法。筛选并准备与物种分布相关的环境变量,利用GIS工具处理,使之成为符合模型条件的具有合适的空间尺度的数字化图层。利用环境变量和物种分布有/无的数据,选择合适的方法及软件建立模型,并对模型进行检验和评估。我们总结了用于构建物种分布模型的不同算法和软件。本文将针对以上各个环节,阐述利用物种分布有/无数据进行研究所需要的技术细节,以期望为读者提供借鉴。     

Dealing with uncertain absences in habitat modelling: a case study of a rare ground-dwelling parrot

In the development of a species distribution model based on regression techniques such as generalized linear or additive modelling (GLM/GAM), a basic assumption is that records of species presence and absence are real. However, a common concern in many studies examining species distributions is that absences cannot be inferred with certainty. This is particularly the case where the species is rare, difficult to detect and/or does not occupy all available habitat considered suitable. The western ground parrot ( Pezoporus wallicus flaviventris ) of southern Western Australia, Australia, is a case in point, as not only is it rare and difficult to detect, but it is also unlikely to occupy all available suitable habitat. A recent survey of ground parrots provided the opportunity to develop a predictive distribution model. As the data were susceptible to false absences, these were replaced with randomly selected 'pseudo' absences and modelled using GLM. As a comparison, presence-only information was modelled using a relatively new approach, MAXENT, a machine-learning technique that has been shown to perform comparatively well. The predictive performance of both models, as assessed by the receiver operating characteristic plot (ROC) was high (AUC > 0.8), with MAXENT performing only marginally better than the GLM. These approaches both indicated that the ground parrot prefers areas relatively high in altitude, distant from rivers, gently sloping to level habitat, with an intermediate cover of vegetation and where there is a mosaic of vegetation ages. In this case, the use of presence-only information resulted in the identification of important environmental attributes defining the occurrence of the ground parrot, but additional factors that account for the inability of the bird to occupy all suitable habitat should be a component of model refinement.     

Integrating detection probabilities in species distribution models of amphibians breeding in Mediterranean temporary ponds

Aim The imperfect detection of species may lead to erroneous conclusions about species–environment relationships. Accuracy in species detection usually requires temporal replication at sampling sites, a time‐consuming and costly monitoring scheme. Here, we applied a lower‐cost alternative based on a double‐sampling approach to incorporate the reliability of species detection into regression‐based species distribution modelling. Location Doñana National Park (south‐western Spain). Methods Using species‐specific monthly detection probabilities, we estimated the detection reliability as the probability of having detected the species given the species‐specific survey time. Such reliability estimates were used to account explicitly for data uncertainty by weighting each absence. We illustrated how this novel framework can be used to evaluate four competing hypotheses as to what constitutes primary environmental control of amphibian distribution: breeding habitat, aestivating habitat, spatial distribution of surrounding habitats and/or major ecosystems zonation. The study was conducted on six pond‐breeding amphibian species during a 4‐year period. Results Non‐detections should not be considered equivalent to real absences, as their reliability varied considerably. The occurrence of Hyla meridionalis and Triturus pygmaeus was related to a particular major ecosystem of the study area, where suitable habitat for these species seemed to be widely available. Characteristics of the breeding habitat (area and hydroperiod) were of high importance for the occurrence of Pelobates cultripes and Pleurodeles waltl. Terrestrial characteristics were the most important predictors of the occurrence of Discoglossus galganoi and Lissotriton boscai, along with spatial distribution of breeding habitats for the last species. Main conclusions We did not find a single best supported hypothesis valid for all species, which stresses the importance of multiscale and multifactor approaches. More importantly, this study shows that estimating the reliability of non‐detection records, an exercise that had been previously seen as a naïve goal in species distribution modelling, is feasible and could be promoted in future studies, at least in comparable systems.     

Community ecology of absent species: hidden and dark diversity

Community ecologists have so far focused mainly on species identified at a site. I suggest that we can understand better patterns and their underlying processes in ecological communities if we also examine those species absent from the sampled community. However, there are various types of absences, which all harbour different information. Hidden diversity comprises species that are absent from our sight: dormant or locally very rare species overlooked by traditional sampling. Fortunately, modern DNA‐based techniques can help us to find hidden species when analysing environmental samples. Depending on habitat type and sampling scale, a large number of co‐existing species might be hidden. Dark diversity comprises absent species that constitute the habitat‐specific species pool. Dark diversity can be determined based on data on species distribution, dispersal potential and ecological requirements. If we know both observed and dark diversity, we can estimate community completeness and infer those processes that determine which species in the species pool actually co‐exist locally. In addition, most species in the world do not actually belong to the habitat‐specific species pool of the community: their ecological requirements differ or their distribution area is elsewhere. Such other absent species are usually not directly relevant to a particular community. However, knowing ecologically suitable species from other regions can give early warning of possible future invasion of alien species (alien dark diversity). To conclude, species presences have meaning only if there are absences (and vice versa). Methods to detect absent species are rapidly developing and will soon form a standard toolbox for community ecology.     

European red squirrel population dynamics driven by squirrelpox at a gray squirrel invasion interface

Infectious disease introduced by non‐native species is increasingly cited as a facilitator of native population declines, but direct evidence may be lacking due to inadequate population and disease prevalence data surrounding an outbreak. Previous indirect evidence and theoretical models support squirrelpox virus (SQPV) as being potentially involved in the decline of red squirrels (Sciurus vulgaris) following the introduction of the non‐native gray squirrel (Sciurus carolinensis) to the United Kingdom. The red squirrel is a major UK conservation concern and understanding its continuing decline is important for any attempt to mitigate the decline. The red squirrel–gray squirrel system is also exemplary of the interplay between infectious disease (apparent competition) and direct competition in driving the replacement of a native by an invasive species. Time series data from Merseyside are presented on squirrel abundance and squirrelpox disease (SQPx) incidence, to determine the effect of the pathogen and the non‐native species on the native red squirrel populations. Analysis indicates that SQPx in red squirrels has a significant negative impact on squirrel densities and their population growth rate (PGR). There is little evidence for a direct gray squirrel impact; only gray squirrel presence (but not density) proved to influence red squirrel density, but not red squirrel PGR. The dynamics of red SQPx cases are largely determined by previous red SQPx cases, although previous infection of local gray squirrels also feature, and thus, SQPV‐infected gray squirrels are identified as potentially initiating outbreaks of SQPx in red squirrels. Retrospective serology indicates that approximately 8% of red squirrels exposed to SQPV may survive infection during an epidemic. This study further highlights the UK red squirrel – gray squirrel system as a classic example of a native species population decline strongly facilitated by infectious disease introduced by a non‐native species. It is therefore paramount that disease prevention and control measures are integral in attempts to conserve red squirrels in the United Kingdom.     

The importance of including imperfect detection models in eDNA experimental design

Environmental DNA (eDNA) is DNA that has been isolated from field samples, and it is increasingly used to infer the presence or absence of particular species in an ecosystem. However, the combination of sampling procedures and subsequent molecular amplification of eDNA can lead to spurious results. As such, it is imperative that eDNA studies include a statistical framework for interpreting eDNA presence/absence data. We reviewed published literature for studies that utilized eDNA where the species density was known and compared the probability of detecting the focal species to the sampling and analysis protocols. Although biomass of the target species and the volume per sample did not impact detectability, the number of field replicates and number of samples from each replicate were positively related to detection. Additionally, increased number of PCR replicates and increased primer specificity significantly increased detectability. Accordingly, we advocate for increased use of occupancy modelling as a method to incorporate effects of sampling effort and PCR sensitivity in eDNA study design. Based on simulation results and the hierarchical nature of occupancy models, we suggest that field replicates, as opposed to molecular replicates, result in better detection probabilities of target species.     

Occupancy Models for Monitoring Marine Fish: A Bayesian Hierarchical Approach to Model Imperfect Detection with a Novel Gear Combination

Occupancy models using incidence data collected repeatedly at sites across the range of a population are increasingly employed to infer patterns and processes influencing population distribution and dynamics. While such work is common in terrestrial systems, fewer examples exist in marine applications. This disparity likely exists because the replicate samples required by these models to account for imperfect detection are often impractical to obtain when surveying aquatic organisms, particularly fishes. We employ simultaneous sampling using fish traps and novel underwater camera observations to generate the requisite replicate samples for occupancy models of red snapper, a reef fish species. Since the replicate samples are collected simultaneously by multiple sampling devices, many typical problems encountered when obtaining replicate observations are avoided. Our results suggest that augmenting traditional fish trap sampling with camera observations not only doubled the probability of detecting red snapper in reef habitats off the Southeast coast of the United States, but supplied the necessary observations to infer factors influencing population distribution and abundance while accounting for imperfect detection. We found that detection probabilities tended to be higher for camera traps than traditional fish traps. Furthermore, camera trap detections were influenced by the current direction and turbidity of the water, indicating that collecting data on these variables is important for future monitoring. These models indicate that the distribution and abundance of this species is more heavily influenced by latitude and depth than by micro-scale reef characteristics lending credence to previous characterizations of red snapper as a reef habitat generalist. This study demonstrates the utility of simultaneous sampling devices, including camera traps, in aquatic environments to inform occupancy models and account for imperfect detection when describing factors influencing fish population distribution and dynamics.     

Non-invasive multi-species monitoring: real-time PCR detection of small mammal and squirrel prey DNA in pine marten (Martes martes) scats

DNA identification of mammal species occurring in the diet of a predator is potentially a useful approach to remotely monitor the distribution of multiple species. This is important in Ireland, where it has been shown that the combined presence of the introduced bank vole and greater white-toothed shrew impact the distribution of the indigenous small mammals, the wood mouse and pygmy shrew. Direct monitoring of these species and their interactions requires trapping, a labour-intensive and costly approach. In this study, we applied an indirect method by genetically testing the presence of small mammals in pine marten scats collected during the National Pine Marten Survey (2005–2007) to map their distribution. We also included additional scats to investigate if less common prey items, the red squirrel and grey squirrel, could also be detected. This study demonstrates that all target species were genetically detected from pine marten scats. This strategy could be implemented as a monitoring programme for indigenous and introduced mammal species.     

The ghost of unbalanced species distribution data in geographical model predictions

Unbalanced samples are considered a drawback in predictive modelling of species' potential habitats, and a prevalence of 0.5 has been extensively recommended. We argue that unbalanced species distribution data are not such a problem from a statistical point of view, and that good models can be obtained provided that the right predictors and cut-off to convert probabilities into presence/absence are chosen. The effects of unbalanced prevalence should not be confused with those of low-quality data affected by false absences, low sample size, or unrepresentativeness of the environmental and spatial gradient. Finally, we point out the necessity of greater research effort aimed at improving both the quality of training data sets, and the processes of validating and testing of models.     

Real vs. artefactual absences in species distributions: tests for Oryzomys albigularis (Rodentia: Muridae) in Venezuela

Aim Numerous geographical information system (GIS)‐based techniques for estimating a species’ potential geographical distribution now exist. While a species’ potential distribution is more extensive than its documented range, the lack of records from some suitable regions may simply derive from inadequate sampling there. Using occurrence records of both the study species and the more inclusive overall target group, I propose a progression of statistical models to evaluate apparent absences in species distributions. Location Northern Venezuela. Methods Employing data from the Smithsonian Venezuelan Project (a large set of standardized mammalian inventories undertaken across Venezuela), I tested distributional hypotheses for the sigmodontine rodent Oryzomys albigularis ( Tomes, 1860 ). Those inventories collected O. albigularis in two of the five major montane regions of northern Venezuela (the Cordillera de Mérida/Macizo de El Tamá and Cordillera de la Costa Central). I used the Genetic Algorithm for Rule‐Set Prediction (GARP) to estimate the species’ potential distribution in northern Venezuela. Then, based on all collection localities from the Smithsonian Venezuelan Project, I determined the probability that the absence of O. albigularis from the three regions of potential presence where it was not documented (the Serranía de Perijá, Lara–Falcón highlands, and Cordillera de la Costa Oriental) could be the result of inadequate sampling. Results and main conclusions All statistical models indicated that the sampling efforts of the Smithsonian Venezuelan Project were insufficient to demonstrate conclusively the absence of O. albigularis from any of the three regions lacking records. Indeed, a subsequent compilation of specimens from ten natural history museums confirmed its presence in the Serranía de Perijá and the Lara–Falcón highlands. Tests using empirical sampling effort and taking human modification of the landscape into account most closely fulfilled the assumptions required for the tests. By providing a framework for bringing additional quantitative rigour to studies of species distributions, these methods will probably prove of wide applicability to other systems.     

An application of fuzzy set ordination to determine tree habitat suitability of sites from a regional data set

Abstract. Fuzzy set ordination is employed to evaluate sites on the basis of their suitability for particular tree species. The technique orders sites along an axis defined by the presences and absences of a given species of interest. A rationale is given in terms of noise reduction; in many situations the overall vegetation of a site will reflect habitat conditions better than the presence, absence, or quantitative performance of any single species. A data set of tree presence/absence covering a large part of the southeastern United States was analyzed and habitat suitability scores were calculated for each species. Monte-Carlo tests were used to measure the statistical power of the data set with regard to habitat preferences; 38 of the 49 species have cumulative frequency distributions showing significant departures from random expectation. Most statistically significant habitat preferences seem to be related to geographic range limits located within the study area, but some species found throughout the area also show significant departures from random expectation. The method may find applications in autecological studies of species, selection of representative site conditions for simulation modeling, and the solution of certain technical problems in ordination.     

Public engagement offers insights on the Eurasian red squirrel distribution

After centuries of absence, the Eurasian red squirrel (Sciurus vulgaris) is recolonizing and expanding throughout Portugal. We implemented an online community-based project, which enabled us to obtain unprecedented records on the Eurasian red squirrel distribution, not only allowing us to map its current extent area but also to reconstruct its southward range expansion during the past years in Portugal. Possible origins of dispersion are forested areas in the northwestern and eastern Spanish borders and sites of reintroductions in Lisbon, Porto, and Coimbra. This well-established public engagement platform sets a successful example of integrated efforts between society and academy in gathering data on species distribution.