Spatially explicit estimation of occupancy,detection probability and survey effort needed to inform conservation planning

Aim It is increasingly recognized the importance of accounting for imperfect detection in species distribution modelling and conservation planning. However, the integration of detectability into a spatially explicit frame has received little attention. We aim (1) to show how to develop distribution maps of both detection probability and survey effort required to reliably determine a species presence/absence and (2) to increase awareness of the spatial variation of detection error inherent in studies of species occurrence. Location North‐western Spain. Methods  We registered the presence/absence of the endangered Egyptian vulture (Neophron percnopterus) in 213 surveys performed in 40 of 104 territories once known to be occupied. We model simultaneously both detection probability and occurrence, using site occupancy modelling. With the resulting regression equations, we developed distribution maps of both detection probability and required sampling effort throughout the area. Results Of the studied territories, 72.5% were detected as occupied, but after accounting for imperfect detection, the proportion of sites truly occupied was 79%. Detectability decreased in territories with higher topographical irregularity and increased with both the time of day of the survey and the progress of the season. Spatial distribution of detectability showed a mainly north–south gradient following the distribution of slope in the area. The likelihood of occupancy increased with rockier, less forested surface and less topographical irregularity within the territory. A minimum of five surveys, on average, are needed to assess, with 95% probability, the occupancy status of a site, ranging from ≤ 3 to > 24 visits/territory depending on survey‐ and site‐specific features. Main conclusions Accounting for detectability and its sources of variation allows us to elaborate distribution maps of detectability‐based survey effort. These maps are useful tools to reliably assess (e.g. with 95% probability) occupancy status throughout a landscape and provide guidance for species conservation planning.     

Designing occupancy surveys and interpreting non‐detection when observations are imperfect

Aim Conservation practitioners use biological surveys to ascertain whether or not a site is occupied by a particular species. Widely used statistical methods estimate the probability that a species will be detected in a survey of an occupied site. However, these estimates of detection probability are alone not sufficient to calculate the probability that a species is present given that it was not detected. The aim of this paper is to demonstrate methods for correctly calculating (1) the probability a species occupies a site given one or more non‐detections, and (2) the number of sequential non‐detections necessary to assert, with a pre‐specified confidence, that a species is absent from a site. Location Occupancy data for a tree frog in eastern Australia serve to illustrate methods that may be applied anywhere species’ occupancy data are used and detection probabilities are < 1. Methods Building on Bayesian expressions for the probability that a site is occupied by a species when it is not detected, and the number of non‐detections necessary to assert absence with a pre‐specified confidence, we estimate occupancy probabilities across tree frog survey locations, drawing on information about where and when the species was detected during surveys. Results We show that the number of sequential non‐detections necessary to assert that a species is absent increases nonlinearly with the prior probability of occupancy, the probability of detection if present, and the desired level of confidence about absence. Main conclusions If used more widely, the Bayesian analytical approaches illustrated here would improve collection and interpretation of biological survey data, providing a coherent way to incorporate detection probability estimates in the design of minimum survey requirements for monitoring, impact assessment and distribution modelling.     

Inferring red squirrel (<Emphasis Type="Italic">Sciurus vulgaris</Emphasis>) absence with hair tubes surveys: a sampling protocol

Hair tubes are often used to monitor red squirrel in fragmented landscapes, where presence/absence data are gathered to determine its distribution and factors affecting it. Despite many applications and evaluation of this technique for density estimation, the reliability of absence data has been overlooked, as no rigorous statistical estimate has been attempted both on the survey duration and on the reliability of absences. Accurate determination of the duration of a survey (e.g. how many visits should be carried out to consider the species absent rather than non-detected) will affect total costs and number of monitored sites; moreover, false absences will bias the distribution estimates. By applying some recently developed occupancy models, we estimated detection probability and sampling size required to infer red squirrel absence. Application of this sampling and data analysis protocol allows to infer the species absence at a reasonable cost and thus to evaluate the reliability of a presence/absence dataset.     

How do species interactions affect species distribution models?

One of the most promising recent advances in biogeography has been the increased interest and understanding of species distribution models – estimates of the probability that a species is present given environmental data. Unfortunately, such analyses ignore many aspects of ecology, and so are difficult to interpret. In particular, we know that species interactions have a profound influence on distributions, but it is not usually possible to incorporate this knowledge into species distribution models. What is needed is a rigorous understanding of how unmeasured biotic interactions affect the inferences generated by species distribution models. To fill this gap, we develop a general mathematical approach that uses probability theory to determine how unmeasured biotic interactions affect inferences from species distribution models. Using this approach, we reanalyze one of the most important classes of mechanistic models of competition: models of consumer resource dynamics. We determine how measurements of one aspect of the environment – a single environmental variable – can be used to estimate the probability that an environment is suitable with species distribution models. We show that species distribution models, which ignore numerous facets of consumer resource dynamics such as the presence of a competitor or the dynamics of depletable resources, can furnish useful predictions for the probability that an environment is suitable in some circumstances. These results provide a rigorous link between complex mechanistic models of species interactions and species distribution models. In so doing they demonstrate that unmeasured biotic interactions can have strong and counterintuitive consequences on species distribution models.     

Predicting the Geographic Distribution of a Species from Presence‐Only Data Subject to Detection Errors

Summary Several models have been developed to predict the geographic distribution of a species by combining measurements of covariates of occurrence at locations where the species is known to be present with measurements of the same covariates at other locations where species occurrence status (presence or absence) is unknown. In the absence of species detection errors, spatial point‐process models and binary‐regression models for case‐augmented surveys provide consistent estimators of a species’ geographic distribution without prior knowledge of species prevalence. In addition, these regression models can be modified to produce estimators of species abundance that are asymptotically equivalent to those of the spatial point‐process models. However, if species presence locations are subject to detection errors, neither class of models provides a consistent estimator of covariate effects unless the covariates of species abundance are distinct and independently distributed from the covariates of species detection probability. These analytical results are illustrated using simulation studies of data sets that contain a wide range of presence‐only sample sizes. Analyses of presence‐only data of three avian species observed in a survey of landbirds in western Montana and northern Idaho are compared with site‐occupancy analyses of detections and nondetections of these species.     

A systematic re-sampling approach to assess the probability of detecting otters Lutra lutra using spraint surveys on small lowland rivers

Assessing and monitoring populations of elusive species frequently rely on the identification of indirect signs such as faeces. The absence of signs does not necessarily denote the absence of a species, thus, the ability to determine the presence/absence is susceptible to false negative results. The probability of detection is central to the interpretation and utility of data from field sign surveys. A low probability of detection may introduce considerable error into distribution patterns, resulting in inaccurate ecological conclusions.We used a systematic resampling approach, based on sequential spatial replication of spraint surveys, to investigate the probability of detecting Eurasian otters (Lutra lutra L.) with different survey designs. This included the standard otter transect survey methodology, which is widely used in conservation and scientific studies. In particular, we focus on the impact of applying broad scale population assessment techniques at smaller spatial scales. Fortnightly catchment-level otter surveys were undertaken on four lowland rivers in South Wales, over a period of two years. GIS was used to construct binary vectors for each survey, denoting the presence (1) or absence (0) of otters at each 50 m section of river. Vectors from all study rivers were pooled and resampled to test the different survey designs. The mean probability of detecting otters based on the standard protocol of a single 600 m transect survey was very low (0.26 ± 0.01 SE). The best way of obtaining a detection probability of 0.8 was to undertake three repeat surveys at two separate sites, using a transect of 800–1000 m.We demonstrate how sequentially collected spatial data can be analysed to determine the reliability of field sign surveys. Increasing the number of visits and study sites was a more efficient means of improving detection power than increasing transect length alone. The study emphasises the importance of determining detection probabilities and designing field sign surveys according to study scale and objectives. Our findings question the value of survey designs that aim to provide an instantaneous assessment of species presence/absence.     

Predicting species distributions from checklist data using site‐occupancy models

Aim (1) To increase awareness of the challenges induced by imperfect detection, which is a fundamental issue in species distribution modelling; (2) to emphasize the value of replicate observations for species distribution modelling; and (3) to show how ‘cheap’ checklist data in faunal/floral databases may be used for the rigorous modelling of distributions by site‐occupancy models. Location Switzerland. Methods We used checklist data collected by volunteers during 1999 and 2000 to analyse the distribution of the blue hawker, Aeshna cyanea (Odonata, Aeshnidae), a common dragonfly in Switzerland. We used data from repeated visits to 1‐ha pixels to derive ‘detection histories’ and apply site‐occupancy models to estimate the ‘true’ species distribution, i.e. corrected for imperfect detection. We modelled blue hawker distribution as a function of elevation and year and its detection probability of elevation, year and season. Results The best model contained cubic polynomial elevation effects for distribution and quadratic effects of elevation and season for detectability. We compared the site‐occupancy model with a conventional distribution model based on a generalized linear model, which assumes perfect detectability (p = 1). The conventional distribution map looked very different from the distribution map obtained using site‐occupancy models that accounted for the imperfect detection. The conventional model underestimated the species distribution by 60%, and the slope parameters of the occurrence–elevation relationship were also underestimated when assuming p = 1. Elevation was not only an important predictor of blue hawker occurrence, but also of the detection probability, with a bell‐shaped relationship. Furthermore, detectability increased over the season. The average detection probability was estimated at only 0.19 per survey. Main conclusions Conventional species distribution models do not model species distributions per se but rather the apparent distribution, i.e. an unknown proportion of species distributions. That unknown proportion is equivalent to detectability. Imperfect detection in conventional species distribution models yields underestimates of the extent of distributions and covariate effects that are biased towards zero. In addition, patterns in detectability will erroneously be ascribed to species distributions. In contrast, site‐occupancy models applied to replicated detection/non‐detection data offer a powerful framework for making inferences about species distributions corrected for imperfect detection. The use of ‘cheap’ checklist data greatly enhances the scope of applications of this useful class of models.     

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.     

Teasing out biological effects and sampling artifacts when using occupancy rate in monitoring programs

ABSTRACT.   Recent declines in biodiversity stress the need for rigorous and reliable multispecies monitoring programs. A potential weakness of monitoring programs is a reliance on raw counts and the assumption either of complete detection or of constant detection probability for each species, regardless of the sampling situation. Until recently, these assumptions have largely remained untested and, therefore, to help insure accuracy, bird-monitoring programs have depended on standardization of counts and counts of longer duration. We tested the effectiveness of these strategies for providing unbiased occupancy rates using a method designed to accommodate situations where species detection probabilities are less than one and heterogeneous. We tested the effect of potential sources of heterogeneity in detection probability (vegetation structure, wind velocity, cloud cover, date, and time) on occupancy rate estimates of 13 bird species in southern France. We compared adjusted and raw occupancy rates for two sampling durations (10 and 20 min). Differences between raw and adjusted occupancy rates were low even for the shorter count duration, suggesting that standardized long counts should produce reliable estimates of occupancy rates even in the absence of correction by an appropriate method. This enhances the value of past monitoring programs where long standardized counts were used, but with designs that do not allow corrected estimates. However, we found that detection probability was heterogeneous for most species and that vegetation structure was an important source of heterogeneity. The possible effects of habitat on detection probability should be of special concern for long-term monitoring programs conducted in landscapes where habitats vary across time or space.     

Quantifying site-level usage and certainty of absence for an invasive species through occupancy analysis of camera-trap data

Efficient implementation of management programs for invasive species depends on accurate surveillance for guiding prioritization of surveillance and control resources in space and time. Occupancy probabilities can be used to determine where surveillance should occur. Conversely, knowledge of the certainty of site-level absence is of special interest in situations where the objective is to completely remove populations despite substantial risk of re-invasion. Indeed, the decision to shift from emphasizing control activities over the full range to emphasizing reinvasion prevention, surveillance, and response near the borders, depends on accurate knowledge of absence across space. We used a dynamic occupancy model to monitor changes in the distribution of an invasive species, feral swine (Sus scrofa), based on camera-trap data collected as part of a management program from June 2014 to January 2016 in San Diego County, California. Site usage of feral swine declined overall. The most informative predictors of site usage were spatial (latitude and longitude). Site-level non-usage rates increased over time and in response to management removal efforts; and site-level usage rates were heavily impacted by having neighboring sites that were used. Combining the detection probability estimated from the occupancy model and Bayes Theorem, we demonstrated how certainty of local (site-level) absence can be estimated iteratively in time in areas with negative surveillance (no detections) data. Our framework provides a means for using management-based surveillance data to quantify certainty of site-level absence of an invasive species, allowing for adaptive prioritization of surveillance and control resources. Our approach is flexible for application to other species and types of surveillance (e.g., track-plates, eDNA).     

Distribution-abundance relationship for passerines breeding in Tunisian oases: test of the sampling hypothesis

The positive relationship between local abundance and distribution of species is a widely recognized pattern in community ecology. However, it has been suggested that this relationship can simply be an artefact of sampling because locally rare species are less detectable then locally abundant ones, and hence their distribution can easily be underestimated. Here, we use count data to investigate the relationship between distribution and abundance of passerines breeding in a sample of oases from southern Tunisia, and we provide a test of the sampling artefact hypothesis. In particular, we checked for a difference in detection probability between localized and widespread species, and we tested if increasing the sampling effort affects the significance of the relationship. A significant positive relationship between the average local abundance of passerine species and the proportion of occupied oases was found. The use of a capture-recapture approach allowed us to estimate and to compare the detection probabilities of localized and widespread species subsets. We found that localized species were locally less detectable than widespread species, which is consistent with the main assumption of the sampling artefact hypothesis. However, increasing the detection probability of species by conducting more counts did not affect the significance of the relationship, which did not give support to the sampling artefact hypothesis. Our work implies that sampling contributed to the distribution-abundance relationship we found, but that it is unlikely that such a relationship could entirely be explained by an artefact of sampling. It also underlines the insight that can be gained by using probabilistic approaches of estimating species number and detection probability when attempting to disentangle sampling from ecological effects in community ecology studies.     

Spatial patch occupancy patterns of the Lower Keys marsh rabbit

Reliable estimates of presence or absence of a species can provide substantial information on management questions related to distribution and habitat use but should incorporate the probability of detection to reduce bias. We surveyed for the endangered Lower Keys marsh rabbit (Sylvilagus palustris hefneri) in habitat patches on 5 Florida Key islands, USA, to estimate occupancy and detection probabilities. We derived detection probabilities using spatial replication of plots and evaluated hypotheses that patch location (coastal or interior) and patch size influence occupancy and detection. Results demonstrate that detection probability, given rabbits were present, was <0.5 and suggest that naïve estimates (i.e., estimates without consideration of imperfect detection) of patch occupancy are negatively biased. We found that patch size and location influenced probability of occupancy but not detection. Our findings will be used by Refuge managers to evaluate population trends of Lower Keys marsh rabbits from historical data and to guide management decisions for species recovery. The sampling and analytical methods we used may be useful for researchers and managers of other endangered lagomorphs and cryptic or fossorial animals occupying diverse habitats. © 2011 The Wildlife Society.     

Seasonal variation in detectability of butterflies surveyed with Pollard walks

Monitoring protocols should be designed to maximize the probability of detecting target species with limited resources. Most species are imperfectly detected, hence, they will often be overlooked at sites where they actually occur, resulting in false-negative errors (i.e. false absences). Uncertain detection of target species has profound implications for conservation, but can be dealt with by using adequate survey designs and statistical models. Butterflies often are monitored with repeated, fixed-route transect counts (Pollard walks). Even though this survey method is widely used in temperate regions, its efficiency in terms of detection probability has never been rigorously assessed in part owing to a lack of suitable analysis methods. Here, I use site-occupancy models to explore the seasonal patterns in detection probability of four California butterflies using Pollard walks. In an effort to inventory the butterfly fauna in two natural areas in the eastern foothills of the Santa Cruz mountains (California), I surveyed twelve 250 m long transects weekly for 22 weeks. I estimated the detection probability (the probability of recording a species during a single transect walk, given it is present) of four species. The probability of detecting each species depended mostly on the monitoring week. Average detection probability across the season was 64% for Cercyonis pegala, 56% for Limenitis lorquini, 76% for Euphydryas chalcedona, and 50% for Lycaena arota. Based on the mean detection probability, I then inferred the number of visits necessary to be statistically confident that a given species was indeed absent from a transect where it was not observed (i.e. obtaining a false absence rate <5%). Knowledge of detection probabilities is fundamental to the optimal design of monitoring programs and the interpretation of their results. The methods applied in this study provide an efficient and evidence-based method to optimally allocate butterfly monitoring resources across space (number of transects) and time (number and timing of visits).     

Microhabitat selection when detection is imperfect: the case of an endemic Atlantic forest mammal

Studies on habitat selection based on the presence and absence of individuals still rarely include the uncertainty of absence. With it, one can model the probability of detection, which is a parameter of interest especially when dealing with species where non-detection is common. Here, we performed an analysis of microhabitat selection of a New World marsupial (Marmosa paraguayana, Tate 1931)??an arboreal species endemic to the Brazilian Atlantic forest??accounting for false absence. We found empirical evidence supporting one of our hypotheses: the angle of the terrain??s inclination at a site positively affects detection probability. This is probably due to the fact that, at an inclined site, the area available to the animals tends to be larger and the probability of detection of M. paraguayana would be higher, due either to greater local abundance or increased frequency of moving. The probability of resource use was heterogeneous, or rather, not constant in space, but constant in time. We found weak evidence for the correlation between the canopy volume and the probability of resource use. However, we observed a tendency in the estimates of site-specific probability of resource use: the highest values of the probability of resource use appeared in the upper part of the study grid, where the canopies were denser as well as more closed. Thus, this specie??s probability of resource use possibly diminishes in habitats such as early secondary forest with tiny canopies.     

Optimizing occupancy surveys by maximizing detection probability: application to amphibian monitoring in the Mediterranean region

• Setting up effective conservation strategies requires the precise determination of the targeted species’ distribution area and, if possible, its local abundance. However, detection issues make these objectives complex for most vertebrates. The detection probability is usually <1 and is highly dependent on species phenology and other environmental variables. The aim of this study was to define an optimized survey protocol for the Mediterranean amphibian community, that is, to determine the most favorable periods and the most effective sampling techniques for detecting all species present on a site in a minimum number of field sessions and a minimum amount of prospecting effort. We visited 49 ponds located in the Languedoc region of southern France on four occasions between February and June 2011. Amphibians were detected using three methods: nighttime call count, nighttime visual encounter, and daytime netting. The detection nondetection data obtained was then modeled using site‐occupancy models. The detection probability of amphibians sharply differed between species, the survey method used and the date of the survey. These three covariates also interacted. Thus, a minimum of three visits spread over the breeding season, using a combination of all three survey methods, is needed to reach a 95% detection level for all species in the Mediterranean region. Synthesis and applications: detection nondetection surveys combined to site occupancy modeling approach are powerful methods that can be used to estimate the detection probability and to determine the prospecting effort necessary to assert that a species is absent from a site.

     

Population size influences amphibian detection probability: implications for biodiversity monitoring programs

Monitoring is an integral part of species conservation. Monitoring programs must take imperfect detection of species into account in order to be reliable. Theory suggests that detection probability may be determined by population size but this relationship has not yet been assessed empirically. Population size is particularly important because it may induce heterogeneity in detection probability and thereby cause bias in estimates of biodiversity. We used a site occupancy model to analyse data from a volunteer-based amphibian monitoring program to assess how well different variables explain variation in detection probability. An index to population size best explained detection probabilities for four out of six species (to avoid circular reasoning, we used the count of individuals at a previous site visit as an index to current population size). The relationship between the population index and detection probability was positive. Commonly used weather variables best explained detection probabilities for two out of six species. Estimates of site occupancy probabilities differed depending on whether the population index was or was not used to model detection probability. The relationship between the population index and detectability has implications for the design of monitoring and species conservation. Most importantly, because many small populations are likely to be overlooked, monitoring programs should be designed in such a way that small populations are not overlooked. The results also imply that methods cannot be standardized in such a way that detection probabilities are constant. As we have shown here, one can easily account for variation in population size in the analysis of data from long-term monitoring programs by using counts of individuals from surveys at the same site in previous years. Accounting for variation in population size is important because it can affect the results of long-term monitoring programs and ultimately the conservation of imperiled species.     

The effect of a gradual response to the environment on species distribution modeling performance

Species distribution models (SDMs) have been widely used in ecology, biogeography, and conservation. Although ecological theory predicts that species occupancy is dynamic, the outputs of SDMs are generally converted into a single occurrence map, and model performance is evaluated in terms of success to predict presences and absences. The aim of this study was to characterize the effects of a gradual response in species occupancy to environmental gradients into the performance of SDMs. First we outline guidelines for the appropriate simulation of artificial species that allows controlling for gradualism and prevalence in the occupancy patterns over an environmental gradient. Second, we derive theoretical expected values for success measures based on presence‐absence predictions (AUC, Kappa, sensitivity and specificity). And finally we used artificial species to exemplify and test the effect of a gradual probabilistic occupancy response to environmental gradients on SDM performance. Our results show that when a species responds gradually to an environmental gradient, conventional measures of SDM predictive success based on presence‐absence cannot be expected to attain currently accepted performance values considered as good, even for a model that recovers perfectly well the true probability of occurrence. A gradual response imposes a theoretical expected value for these measures of performance that can be calculated from the species properties. However, irrespective of the statistical modeling strategy used and of how gradual the species response is, one can recover the true probability of occurrence as a function of environmental variables provided that species and sample prevalence are similar. Therefore, model performance based on presence‐absence should be judged against the theoretical expected value rather than to absolute values currently in use such as AUC > 0.8. Overall, we advocate for a wider use of the probability of occurrence and emphasize the need for further technical developments in this sense.     

Modeling stream fish distributions using interval‐censored detection times

Controlling for imperfect detection is important for developing species distribution models (SDMs). Occupancy‐detection models based on the time needed to detect a species can be used to address this problem, but this is hindered when times to detection are not known precisely. Here, we extend the time‐to‐detection model to deal with detections recorded in time intervals and illustrate the method using a case study on stream fish distribution modeling. We collected electrofishing samples of six fish species across a Mediterranean watershed in Northeast Portugal. Based on a Bayesian hierarchical framework, we modeled the probability of water presence in stream channels, and the probability of species occupancy conditional on water presence, in relation to environmental and spatial variables. We also modeled time‐to‐first detection conditional on occupancy in relation to local factors, using modified interval‐censored exponential survival models. Posterior distributions of occupancy probabilities derived from the models were used to produce species distribution maps. Simulations indicated that the modified time‐to‐detection model provided unbiased parameter estimates despite interval‐censoring. There was a tendency for spatial variation in detection rates to be primarily influenced by depth and, to a lesser extent, stream width. Species occupancies were consistently affected by stream order, elevation, and annual precipitation. Bayesian P‐values and AUCs indicated that all models had adequate fit and high discrimination ability, respectively. Mapping of predicted occupancy probabilities showed widespread distribution by most species, but uncertainty was generally higher in tributaries and upper reaches. The interval‐censored time‐to‐detection model provides a practical solution to model occupancy‐detection when detections are recorded in time intervals. This modeling framework is useful for developing SDMs while controlling for variation in detection rates, as it uses simple data that can be readily collected by field ecologists.     

virtualspecies,an R package to generate virtual species distributions

virtualspecies is a freely available package for R designed to generate virtual species distributions, a procedure increasingly used in ecology to improve species distribution models. This package combines the existing methodological approaches with the objective of generating virtual species distributions with increased ecological realism. The package includes 1) generating the probability of occurrence of a virtual species from a spatial set of environmental conditions (i.e. environmental suitability), with two different approaches; 2) converting the environmental suitability into presence–absence with a probabilistic approach; 3) introducing dispersal limitations in the realised virtual species distributions and 4) sampling occurrences with different biases in the sampling procedure. The package was designed to be extremely flexible, to allow users to simulate their own defined species–environment relationships, as well as to provide a fine control over every simulation parameter. The package also includes a function to generate random virtual species distributions. We provide a simple example in this paper showing how increasing ecological realism of the virtual species impacts the predictive performance of species distribution models. We expect that this new package will be valuable to researchers willing to test techniques and protocols of species distribution models as well as various biogeographical hypotheses.     

Persistence and colonisation as measures of success in bog restoration for aquatic invertebrates: a question of detection

1. van Duinen et al. (Freshwater Biol., 2006) raise an interesting point regarding Mazerolle et al.’s (Freshwater Biol., 2006, 51 , p. 333) conclusion on the ability of invertebrates, especially sedentary species, to colonise newly created bogs pools. We wish to clarify that Mazerolle et al. (2006) targeted large arthropods and the absence of smaller sedentary species was purely a result of sampling design. 2. van Duinen et al. (2006) postulate that colonisation rates by bog specialists should be higher in Canada than in the Netherlands, given the extensive amount of intact peatlands in Canada. Here, we emphasise the importance of taking the regional context into account when assessing restoration success as our study site occurs in a landscape where most bog pools have been drained. 3. An evaluation of restoration efforts should focus on both sedentary and vagile invertebrates, to resolve the importance of persistence and colonisation. Such patterns are difficult to interpret, however, when sampling designs and analyses do not account for the probability of detection: an absence may be due to non‐detection or true absence. We strongly urge investigators to directly estimate detection probability in addition to the parameters of interest (e.g. presence, abundance) to provide the best information possible regarding restoration success.