Bayesian estimation of species richness from quadrat sampling data in the presence of prior information

We consider the problem of estimating the number of species of an animal community. It is assumed that it is possible to draw up a list of species liable to be present in this community. Data are collected from quadrat sampling. Models considered in this article separate the assumptions related to the experimental protocol and those related to the spatial distribution of species in the quadrats. Our parameterization enables us to incorporate prior information on the presence, detectability, and spatial density of species. Moreover, we elaborate procedures to build the prior distributions on these parameters from information furnished by external data. A simulation study is carried out to examine the influence of different priors on the performances of our estimator. We illustrate our approach by estimating the number of nesting bird species in a forest.     

Impact of climatic variations on bird species occupancy rate in a southern European forest

Species that are affected by climatic variations can undergo modification in range and/or abundance. Knowing how individuals or species occupy their habitat is essential to understand how species use their environment, and detecting variations that might affect this use can be determinant in species management. Hierarchical modeling is regularly used to assess for occupancy rate (i.e. proportion of patches occupied in a region), particularly when it is required to consider detectability-related issues. The present study is the first application of the conditional model presented in Dupuis et al. (Biometrics 2010), which is applied in the case of a heterogeneous area that might be divided into homogeneous sub-areas. Their approach is used to study the impact of three consecutive particularly cold winters on a selected set of bird species in a forest of southern France in the context of available prior information on birds detectability. We examined a limited range of factors that might influence the response of some bird species to climate. We considered the case of sedentary, partially migrating and migrating species. We also assessed if the biogeographical origins of the different species affect their occupancy rates. Globally, changes in occupancy rates between 1985 and 1987 indicates for the first time a continentalization of the regional forest fauna, reflected by the expansion of Palearctic and Turkestano-European faunistic type species, with depletion or extinction of European, Turkestano-Mediterranean and Mediterranean sedentary species. We have also shown the importance of prior information.     

Conservation Assessments of Arboreal Mammals in Difficult Terrain: Occupancy Modeling of Pileated Gibbons (Hylobates pileatus)

Long-term monitoring programs, wildlife surveys, and other research involving species population assessment require reliable data on population status. Given the logistically challenging nature of some species’ habitats and cryptic behaviors, collecting these data can prove to be a considerable barrier. We used detection/nondetection data from pileated gibbons (Hylobates pileatus) in the Cardamom Mountains of southwest Cambodia to estimate their population occupancy and detectability. We modeled occupancy using elevation, tree height, tree density, tree diversity, and disturbance covariates. Modeling demonstrated that 83% of the sites are occupied by Hylobates pileatus and that the detectability of the species varies positively with elevation. No clear relationship between habitat quality covariates and occupancy of Hylobates pileatus emerged. Effort analysis based on model estimates demonstrated that at high elevations, less than half the number of site visits is needed to attain the same detectability estimate precision as across all elevations. We suggest that human activities at low elevations, which affect forest composition, are the central factors impacting the detectability and occupancy of Hylobates pileatus. Longer sampling durations and/or a higher number of site visits, especially at lower elevations, increase precision of the occupancy estimator for the least effort. For effective future monitoring and research for this and similar species, using this relatively simple method, applied with repeat site visits, would allow a longitudinal comparison of detection at sites in difficult terrain.     

Estimating species richness from quadrat sampling data: a general approach

We consider the problem of estimating the number of species (denoted by S) of a biological community located in a region divided into n quadrats. To address this question, different hierarchical parametric approaches have been recently developed. Despite a detailed modeling of the underlying biological processes, they all have some limitations. Indeed, some assume that n is theoretically infinite; as a result, n and the sampling fraction are not a part of such models. Others require some prior information on S to be efficiently implemented. Our approach is more general in that it applies without limitation on the size of n, and it can be used in the presence, as well as in the absence, of prior information on S. Moreover, it can be viewed as an extension of the approach of Dorazio and Royle (2005, Journal of the American Statistical Association 100, 389-398) in that n is a part of the model and a prior distribution is placed on S. Despite serious computational difficulties, we have perfected an efficient Markov chain Monte Carlo algorithm, which allows us to obtain the Bayesian estimate of S. We illustrate our approach by estimating the number of species of a bird community located in a forest.     

Does accounting for imperfect detection improve species distribution models?

Models of species distributions are increasingly being used to address a variety of problems in conservation biology. In many applications, perfect or constant detectability of species, given presence, is assumed. While this problem has been acknowledged and addressed through the development of occupancy models, we still know little regarding whether addressing the potential for imperfect detection improves the predictive performance of species distribution models in nature. Here, we contrast logistic regression models of species occurrence that do not correct for detectability to hierarchical occupancy models that explicitly estimate and adjust for detectability, and maximum entropy models that attempt to circumvent the detectability problem by using data from known presence locations only. We use a large‐scale, long‐term monitoring database across western Montana and northern Idaho to contrast these models for nine landbird species that cover a broad spectrum in detectability. Overall, occupancy models were similar to or better than other approaches in terms of predictive accuracy, as measured by the Area Under the ROC Curve (AUC) and Kappa, with maximum entropy tending to provide the lowest predictive accuracy. Models varied in the types of errors associated with predictions, such that some model approaches may be preferred over others in certain situations. As expected, predictive performance varied across a gradient in species detectability, with logistic regression providing lower relative performance for less detectable species and Maxent providing lower performance for highly detectable species. We conclude by discussing the advantages and limitations to each approach for developing large‐scale species distribution models.     

Modeling Occupancy of Hosts by Mistletoe Seeds after Accounting for Imperfect Detectability

The detection of an organism in a given site is widely used as a state variable in many metapopulation and epidemiological studies. However, failure to detect the species does not necessarily mean that it is absent. Assessing detectability is important for occupancy (presence—absence) surveys; and identifying the factors reducing detectability may help improve survey precision and efficiency. A method was used to estimate the occupancy status of host trees colonized by mistletoe seeds of Psittacanthus plagiophyllus as a function of host covariates: host size and presence of mistletoe infections on the same or on the nearest neighboring host (the cashew tree Anacardium occidentale). The technique also evaluated the effect of taking detectability into account for estimating host occupancy by mistletoe seeds. Individual host trees were surveyed for presence of mistletoe seeds with the aid of two or three observers to estimate detectability and occupancy. Detectability was, on average, 17% higher in focal-host trees with infected neighbors, while decreased about 23 to 50% from smallest to largest hosts. The presence of mistletoe plants in the sample tree had negligible effect on detectability. Failure to detect hosts as occupied decreased occupancy by 2.5% on average, with maximum of 10% for large and isolated hosts. The method presented in this study has potential for use with metapopulation studies of mistletoes, especially those focusing on the seed stage, but also as improvement of accuracy in occupancy models estimates often used for metapopulation dynamics of tree-dwelling plants in general.     

Effectiveness of Protected Areas for biodiversity conservation: Mammal occupancy patterns in the Iguaçu National Park,Brazil

Protected areas – widely recognized as the main strategy for biodiversity conservation – have greatly expanded, covering ∼15% of the Earth; however, we still lack detailed information on biodiversity to evaluate their effectiveness. This is particularly urgent for biodiversity hotspots where protected areas are islands within human modified landscapes. We focus on mammals of the Iguaçu National Park – one of the most important parks in the Atlantic Forest hotspot – to evaluate the effectiveness of protected areas in conserving biodiversity. We monitored 300 km² with 37 cameras traps during five years to assess if (1) species occupancy declined over time, and (2) if species occupancy/detectability are spatially associated with illegal hunting, proximity to tourism infrastructure and distance from the edge, estimating the proportion of the park where these negative effects are detected. Many species that are rare in most Atlantic Forest remnants presented high occupancy within the park, and no decline in occupancy was observed over time. However, the distribution of 11 species was spatially associated primarily with the distance from the edge and proximity to tourism infrastructure, resulting in a decline, across half of the park area, from 13 to 23% in occupancy and from 19 to 35% in detectability (values averaged among species). These negative effects should be even stronger on smaller protected areas, which are the majority in highly altered hotspots. Re-establishing and properly managing buffer zones and restricting tourism to localized areas are essential to ensure the effectiveness of protected areas for biodiversity conservation.     

Efficiency of hair snares and camera traps to survey mesocarnivore populations

Mammalian carnivore communities affect entire ecosystem functioning and structure. However, their large spatial requirements, preferred habitats, low densities, and elusive behavior deem them difficult to study. In recent years, noninvasive techniques have become much more common as they can be used to monitor multiple carnivore species across large areas at a relatively modest cost. Hair snares have the potential to fulfill such requirements, but have rarely been tested in Europe. Our objective was to quantitatively assess the effectiveness of hair snares for surveying mesocarnivores in the Iberian Peninsula (Southwestern Europe), by comparison with camera-trapping. We used an occupancy modeling framework to assess method-specific detectability and occupancy estimates and hypothesized that detection probabilities would be influenced by season, sampling method, and habitat-related variables. A total of 163 hair samples were collected, of which 136 potentially belonged to mesocarnivores. Genetic identification success varied with diagnostic method: 25.2 % using mitochondrial CR, and 9.9 % using the IRBP nuclear gene. Naïve occupancy estimates were, in average, 5.3?±?1.2 times higher with camera-trapping than with hair-snaring, and method-specific detection probabilities revealed that camera traps were, in average, 6.7?±?1.1 times more effective in detecting target species. Overall, few site-specific covariates revealed significant effects on mesocarnivore detectability. Camera traps were a more efficient method for detecting mesocarnivores and estimating their occurrence when compared to hair snares. To improve hair snares' low detection probabilities, we suggest increasing the number of sampling occasions and the frequency at which hair snares are checked. With some refinements to increase detection rates and the success of genetic identification, hair-snaring methods may be valuable for providing deeper insights into population parameters, attained through adequate analysis of genetic information, that is not possible with camera traps.     

Does variation between dry and wet seasons affect tropical forest mammals' occupancy and detectability by camera traps? Case study from the Udzungwa Mountains,Tanzania

The increasing use of camera trapping coupled to occupancy analysis to study terrestrial mammals has opened the way to inferential studies that besides estimating the probability of presence explicitly consider detectability. This in turn allows considering factors that can potentially confound the estimation of occupancy and detection probability, including seasonal variations in rainfall. To address this, we conducted a systematic camera trapping survey in the Udzungwa Mountains of Tanzania by deploying twenty camera traps for 30 days in dry and wet seasons and used dynamic occupancy modelling to determine the effect of season on estimated occupancy and detection probability for species with >10 capture events. The sampling yielded 7657 and 6015 images in dry and wet seasons, respectively, belonging to 21 mammal species. Models with no season dependency and with season‐dependent detectability were best supported, indicating that neither colonization nor extinction varied with seasons and hence occupancy did not vary. Only bush pig (Potamochoerus larvatus) showed a significant decrease in detectability from dry to wet seasons. Our study indicates that seasonal variation in rainfall may have limited effect on occupancy and detectability of resident mammals in Udzungwa rainforests; however, it remains a factor to consider when designing future studies.     

Abundance,density and activity of Salvator merianae (Reptilia: Teiidae) and the effect of poaching on the site occupancy by the lizard in an Atlantic Forest Reserve,Brazil

We aimed to estimate the density, occupancy and detectability of Salvator merianae (Tegu) in one of the largest Atlantic rainforest remnants in Espírito Santo, Brazil, the VNR. Species patch occupancy was modelled and used to predict the response direction of six covariates based on prior knowledge of the Tegu's ecology. A priori, we expected that the covariates measured should represent key habitat features for the species (i.e. temperature, forest edge, open habitats) or elements possibly avoided by the species, based on the hypothesis that poaching would have a negative effect on patch occupancy. We used line‐transect surveys to estimate density and abundance. Camera‐traps were used to estimate patch occupancy by the Tegu. Estimated density for S. merianae was 0.21 ± 0.02 Tegus/ha and estimated population size was 4990 ± 521 individuals. Patch occupancy was best described by two covariates: poaching intensity and distance to the forest edge. Detectability was affected by three covariates: poaching intensity, tree density and temperature. Our study presents robust information on abundance and density, habitat use, and activity of S. merianae in the VNR and is the first study providing data on the effects that poaching has on patch occupancy of this lizard. The data indicated that the occupancy and detectability of this species were influenced by a set of factors, providing information that can be useful in management plans in areas where this species can potentially decline and in areas where it may be introduced.     

Effect of detection heterogeneity in occupancy‐detection models: an experimental test of time‐to‐first‐detection methods

Imperfect detection can bias estimates of site occupancy in ecological surveys but can be corrected by estimating detection probability. Time‐to‐first‐detection (TTD) occupancy models have been proposed as a cost–effective survey method that allows detection probability to be estimated from single site visits. Nevertheless, few studies have validated the performance of occupancy‐detection models by creating a situation where occupancy is known, and model outputs can be compared with the truth. We tested the performance of TTD occupancy models in the face of detection heterogeneity using an experiment based on standard survey methods to monitor koala Phascolarctos cinereus populations in Australia. Known numbers of koala faecal pellets were placed under trees, and observers, uninformed as to which trees had pellets under them, carried out a TTD survey. We fitted five TTD occupancy models to the survey data, each making different assumptions about detectability, to evaluate how well each estimated the true occupancy status. Relative to the truth, all five models produced strongly biased estimates, overestimating detection probability and underestimating the number of occupied trees. Despite this, goodness‐of‐fit tests indicated that some models fitted the data well, with no evidence of model misfit. Hence, TTD occupancy models that appear to perform well with respect to the available data may be performing poorly. The reason for poor model performance was unaccounted for heterogeneity in detection probability, which is known to bias occupancy‐detection models. This poses a problem because unaccounted for heterogeneity could not be detected using goodness‐of‐fit tests and was only revealed because we knew the experimentally determined outcome. A challenge for occupancy‐detection models is to find ways to identify and mitigate the impacts of unobserved heterogeneity, which could unknowingly bias many models.     

Use of classical bird census transects as spatial replicates for hierarchical modeling of an avian community

New monitoring programs are often designed with some form of temporal replication to deal with imperfect detection by means of occupancy models. However, classical bird census data from earlier times often lack temporal replication, precluding detection‐corrected inferences about occupancy. Historical data have a key role in many ecological studies intended to document range shifts, and so need to be made comparable with present‐day data by accounting for detection probability. We analyze a classical bird census conducted in the region of Murcia (SE Spain) in 1991 and 1992 and propose a solution to estimating detection probability for such historical data when used in a community occupancy model: the spatial replication of subplots nested within larger plots allows estimation of detection probability. In our study, the basic sample units were 1‐km transects, which were considered spatial replicates in two aggregation schemes. We fit two Bayesian multispecies occupancy models, one for each aggregation scheme, and evaluated the linear and quadratic effect of forest cover and temperature, and a linear effect of precipitation on species occupancy probabilities. Using spatial rather than temporal replicates allowed us to obtain individual species occupancy probabilities and species richness accounting for imperfect detection. Species‐specific occupancy and community size decreased with increasing annual mean temperature. Both aggregation schemes yielded estimates of occupancy and detectability that were highly correlated for each species, so in the design of future surveys ecological reasons and cost‐effective sampling designs should be considered to select the most suitable aggregation scheme. In conclusion, the use of spatial replication may often allow historical survey data to be applied formally hierarchical occupancy models and be compared with modern‐day data of the species community to analyze global change process.     

Using species distribution and occupancy modeling to guide survey efforts and assess species status

Habitat loss and fragmentation continue to be major issues affecting the persistence and conservation of species, but identification of critical habitat remains a challenge. Species distribution modeling and occupancy modeling are both approaches that have been used to predict species distributions and can identify critical habitat characteristics associated with species occurrence. Additionally, occupancy sampling can provide measures of detectability, increasing the confidence that a species is truly absent when not detected. While increasingly popular, these methods are infrequently used in synergy, and rarely at fine spatial scales. We provide a case study of using distribution and occupancy modeling in unison to direct survey efforts, provide estimates of species presence/absence, and to identify local and landscape features important for species occurrence. The focal species for our study was Ambystoma jeffersonianum, a threatened salamander in the state of Illinois, U.S.A. We found that fine-scale distribution models accurately discriminated occupied from unoccupied breeding ponds (78–91% accuracy), and surveys could be effectively guided using a well-fit model. We achieved a high detection rate (0.774) through occupancy sampling, and determined that A. jeffersonianum never used ponds inhabited by fish, and the probability of a pond being used for breeding increased as canopy cover increased. When faced with limited resources, combining fine-scale distribution modeling with a robust occupancy sampling design can expedite survey efforts, confidently designate species occupancy status, prioritise habitat for future surveys and/or restoration, and identify critical habitat features. This approach is broadly applicable to other taxa that have specific habitat requirements.     

The importance of incorporating imperfect detection in biodiversity assessments: a case study of small mammals in an Australian region

Aim Assessments of biodiversity are an essential requirement of conservation management planning. Species distributional modelling is a popular approach to quantifying biodiversity whereby occurrence data are related to environmental covariates. An important confounding factor that is often overlooked in the development of such models is uncertainty due to imperfect detection. Here, I demonstrate how an analytical approach that accounts for the bias due to imperfect detection can be applied retrospectively to an existing biodiversity survey data set to produce more realistic estimates of species distributions and unbiased covariate relationships. Location Pilbara biogeographic region, Australia. Methods As a component of the Pilbara survey, presence/absence (i.e. undetected) data on small ground‐dwelling mammals were collected. I applied a multiseason occupancy modelling approach to six of the most common species encountered during this survey. Detection and occupancy rates, as well as extinction and colonization probabilities, were determined, and the influence of covariates on these parameters was examined using the multi‐model inference approach. Results Detection probabilities for all six species were considerably lower than 1.0 and varied across time and species. Naïve estimates of occupancy underestimated occupancy rates corrected for species detectability by up to 45%. Seasonal variation in occupancy status was attributed to changes in detection for two of the focal species, while reproductive events explained variation in occupancy in three others. Covariates describing the substrate strongly influenced site occupancy for most of the species modelled. A comparison of the occupancy model with a generalized linear model, assuming perfect detection, showed that the effects of the covariates were underestimated in the latter model. Main conclusions The application of the multiseason occupancy modelling approach to the Pilbara mammal data set demonstrated a powerful framework for examining changes in site occupancy, as well as local colonization and extinction rates of species which are not confounded by variable species detection rates.     

Species richness estimation and determinants of species detectability in butterfly monitoring programmes

Abstract 1. Species richness is the most widely used biodiversity index, but can be hard to measure. Many species remain undetected, hence raw species counts will often underestimate true species richness. In contrast, capture–recapture methods estimate true species richness and correct for imperfect and varying detectability. 2. Detectability is a crucial quantity that provides the link between a species count and true species richness. For insects, it has hardly ever been estimated, although this is required for the interpretation of species counts. 3. In the Swiss butterfly monitoring programme about 100 transect routes are surveyed seven times a year using a highly standardised protocol. In July 2003, control observers made two additional surveys on 38 transects. Data from these 38 quadrats were analysed to see whether currently available capture–recapture models can provide quadrat‐specific estimates of species richness, and to estimate species detectability in relation to transect, observer, survey, region, and abundance. 4. Species richness over the entire season cannot be estimated using current capture–recapture methods. The species pool was open, preventing use of closed population models, and detectability varied by species, preventing use of current open population models. Assuming a closed species pool during two mid‐season (July) surveys, a Jackknife capture–recapture method was used that accounts for heterogeneity to estimate mean detectability and species richness. 5. In every case, more species were present than were counted. Mean species detectability was 0.61 (SE 0.01) with significant differences between observers (range 0.37–0.83). Species‐specific detection at time t+ 1 was then modelled for those species seen at t for three mid‐season surveys. Detectability averaged 0.50 (range 0.17–0.81) for individual species and 0.65, 0.44, and 0.42 for surveys. Abundant species were detected more easily, although this relationship explained only 5% of variation in species detectability. 6. These are important, although not entirely unexpected, results for species richness estimation of short‐lived animals. Raw counts of species may be misleading species richness indicators unless many surveys are conducted. Monitoring programmes should be calibrated, i.e. the assumption of constant detectability over dimensions of interest needs to be tested. The development of capture–recapture or similar models that can cope with both open populations and heterogeneous species detectability to estimate species richness should be a research priority.     

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.     

Modelling occurrence and abundance of species when detection is imperfect

Relationships between species abundance and occupancy are of considerable interest in metapopulation biology and in macroecology. Such relationships may be described concisely using probability models that characterize variation in abundance of a species. However, estimation of the parameters of these models in most ecological problems is impaired by imperfect detection. When organisms are detected imperfectly, observed counts are biased estimates of true abundance, and this induces bias in stated occupancy or occurrence probability. In this paper we consider a class of models that enable estimation of abundance/occupancy relationships from counts of organisms that result from surveys in which detection is imperfect. Under such models, parameter estimation and inference are based on conventional likelihood methods. We provide an application of these models to geographically extensive breeding bird survey data in which alternative models of abundance are considered that include factors that influence variation in abundance and detectability. Using these models, we produce estimates of abundance and occupancy maps that honor important sources of spatial variation in avian abundance and provide clearly interpretable characterizations of abundance and occupancy adjusted for imperfect detection.     

When have we looked hard enough? A novel method for setting minimum survey effort protocols for flora surveys

There is now a substantial body of literature documenting the detectability of plants and animals under standard survey conditions. Despite the evidence that many flora and fauna species have detection probabilities of less than one, it is still the default assumption of most environmental impact assessment processes that if a species is present, it will be detected. Here we briefly review a number of existing studies that have estimated the survey effort necessary to detect animal species, based on what is known about their detection rates in standard surveys. We then propose a novel method, based on failure‐time analysis, for quantifying the detectability of and determining appropriate survey effort for plant species during flora surveys. We provide computer code for implementing the method in the Bayesian freeware WinBUGS. Methods for estimating detectability can be used to inform minimum survey requirements and have important applications in environmental impact assessment and monitoring.     

Dealing with detection error in site occupancy surveys: what can we do with a single survey?

Aim Site occupancy probabilities of target species are commonly used in various ecological studies, e.g. to monitor current status and trends in biodiversity. Detection error introduces bias in the estimators of site occupancy. Existing methods for estimating occupancy probability in the presence of detection error use replicate surveys. These methods assume population closure, i.e. the site occupancy status remains constant across surveys, and independence between surveys. We present an approach for estimating site occupancy probability in the presence of detection error that requires only a single survey and does not require assumption of population closure or independence. In place of the closure assumption, this method requires covariates that affect detection and occupancy.Methods Penalized maximum-likelihood method was used to estimate the parameters. Estimability of the parameters was checked using data cloning. Parametric boostrapping method was used for computing confidence intervals.Important findings The single-survey approach facilitates analysis of historical datasets where replicate surveys are unavailable, situations where replicate surveys are expensive to conduct and when the assumptions of closure or independence are not met. This method saves significant amounts of time, energy and money in ecological surveys without sacrificing statistical validity. Further, we show that occupancy and habitat suitability are not synonymous and suggest a method to estimate habitat suitability using single-survey data.     

Testing the utility of species distribution modelling using Random Forests for a species in decline

Habitat suitability estimates derived from species distribution models (SDMs) are increasingly used to guide management of threatened species. Poorly estimating species’ ranges can lead to underestimation of threatened status, undervaluing of remaining habitat and misdirection of conservation funding. We aimed to evaluate the utility of a SDM, similar to the models used to inform government regulation of habitat in our study region, in estimating the contemporary distribution of a threatened and declining species. We developed a presence‐only SDM for the endangered New Holland Mouse (Pseudomys novaehollandiae) across Victoria, Australia. We conducted extensive camera trap surveys across model‐predicted and expert‐selected areas to generate an independent data set for use in evaluating the model, determining confidence in absence data from non‐detection sites with occupancy and detectability modelling. We assessed the predictive capacity of the model at thresholds based on (1) sum of sensitivity and specificity (SSS), and (2) the lowest presence threshold (LPT; i.e. the lowest non‐zero model‐predicted habitat suitability value at which we detected the species). We detected P. novaehollandiae at 40 of 472 surveyed sites, with strong support for the species’ probable absence from non‐detection sites. Based on our post hoc optimised SSS threshold of the SDM, 25% of our detection sites were falsely predicted as non‐suitable habitat and 75% of sites predicted as suitable habitat did not contain the species at the time of our survey. One occupied site had a model‐predicted suitability value of zero, and at the LPT, 88% of sites predicted as suitable habitat did not contain the species at the time of our survey. Our findings demonstrate that application of generic SDMs in both regulatory and investment contexts should be tempered by considering their limitations and currency. Further, we recommend engaging species experts in the extrapolation and application of SDM outputs.