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.     

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.     

Integrating over uncertainty in spatial scale of response within multispecies occupancy models yields more accurate assessments of community composition

Species abundance and community composition are affected not only by the local environment, but also by broader landscape and regional context. Yet, determining the spatial scales at which landscapes affect species remains a persistent challenge, hindering our ability to understand how environmental gradients shape communities. This problem is amplified by rare species and imperfect species detection. Here, we present a Bayesian framework that allows uncertainty surrounding the ‘true’ spatial scale of species’ responses (i.e. changes in presence/absence) to be integrated directly into a community hierarchical model. This scale‐selecting multispecies occupancy model (ssMSOM) estimates the scale of response, and shows high accuracy and correct levels of uncertainty in parameter estimates across a broad range of simulation conditions. An ssMSOM can be run in a matter of minutes, as opposed to the many hours required to run normal multispecies occupancy models at all queried spatial scales, and then conduct model selection – a problem that up to now has prohibited scale of response from being rigorously evaluated in an occupancy framework. Alternatives to the ssMSOM, such as GLM‐based approaches frequently fail to detect the correct spatial scale and magnitude of response, and are often falsely confident by favoring the incorrect parameter estimates, especially as species’ detection probabilities deviate from perfect. We further show how trait information can be leveraged to understand how individual species’ scales of response vary within communities. Integrating spatial scale selection directly into hierarchical community models provides a means of formally testing hypotheses regarding spatial scales of response, and more accurately determining the environmental drivers that shape communities.     

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.     

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.     

Monitoring occurrence,extinction, and colonization probabilities for gibbon populations

All gibbon species (Family: Hylobatidae) are considered threatened with extinction and recognized on the International Union for Conservation of Nature Red List of Threatened Species. Because gibbons are one of the most threatened families of primates, monitoring their status is now critically important. Long-term monitoring programs applying occupancy approaches, in addition to assessing occurrence probability, improves understanding of other population parameters such as site extinction or colonization probabilities, which elucidate temporal and spatial changes and are therefore important for guiding conservation efforts. In this study, we used multiple season occupancy models to monitor occurrence, extinction, and colonization probabilities for northern yellow-cheeked crested gibbon Nomascus annamensis in three adjacent protected areas in the Central Annamites mountain range, Vietnam. We collected data at 30 listening posts in 2012, 2014, and 2016 using the auditory point count method. Occurrence probabilities were highest in 2012 (0.74, confidence interval [CI]: 0.56–0.87) but slightly lower in 2014 (0.66, CI: 0.51–0.79) and 2016 (0.67, CI: 0.49–0.81). Extinction probabilities during the 2012–2014 and 2014–2016 intervals were 0.26 (0.14–0.44) and 0.25 (0.12–0.44), respectively. Colonization probabilities during 2012–2014 were 0.44 (0.19–0.73) and between 2014 and 2016 was 0.51 (0.26–0.75). Although local site extinctions have occurred, high recolonization probability helped to replenish the unoccupied sites and kept the occurrence probability stable. Long-term monitoring programs which use occurrence probability alone might not fully reveal the true dynamics of gibbon populations. We strongly recommend including multiple season occupancy models to monitor occurrence, extinction, and colonization probabilities in long-term gibbon monitoring programs.     

Monitoring Golden-Cheeked Warblers on Private Lands in Texas

ABSTRACT A majority of North American breeding habitat for neotropical migrants exists on private lands, requiring monitoring strategies focused on habitat in these private holdings. We outline study designs and protocols using repeated Presence-Absence surveys across a gradient of patch sizes to develop a range-wide monitoring program for the endangered golden-cheeked warbler (Dendroica chrysoparia) in Texas, USA. We surveyed 200–400 point-count locations across approximately 30 private properties annually from 2005 to 2008. We used data from our surveyed patches (n = 147) and the Ψ (occupancy), p (detection), and γ = 1 - ɛ parameterization to estimate patch dynamics and associated detection probabilities for golden-cheeked warblers. Patch size had a strong association with patch occupancy, and all patches >160 ha were predicted to be occupied. We found no evidence that large golden-cheeked warbler populations located on public lands in the vicinity of our study area influenced occupancy dynamics. We conducted simulations across a range of detection probabilities to evaluate potential sample sizes for both standard- and removal-based occupancy modeling. Simulations using parameter estimates from our analysis indicated that removal-based sampling is superior to standard sampling. Based on our results, surveying golden-cheeked warbler presence in oak-juniper (Quercus-Juniperus) patches under a removal modeling framework should be considered as one alternative for range-wide monitoring programs because patch-level monitoring would be necessary to estimate proportion of range occupied. Large contiguous patches are rare across the species’ range; hence, conservation and management of the mosaic of smaller patches within a landscape context would be required for maintaining species viability. Thus, we recommend the identification of areas where smaller, contiguous patches represent a significant portion of the available habitat within the local landscape and targeting these areas for habitat maintenance and improvement.     

Effects of Rain on Palm Springs Ground Squirrel Occupancy in the Sonoran Desert

ABSTRACT Long-term monitoring programs must use informative yet cost-effective methods. Occupancy estimates that incorporate detection probabilities are used with increasing frequency to describe species status and make management recommendations. Estimating changes in the occupancy of points over time in response to management actions or environmental changes may be especially useful for management of the Palm Springs round-tailed ground squirrel (Spermophilus tereticaudus chlorus), a subspecies covered under the Coachella Valley Multiple Species Habitat Conservation Plan and Natural Community Conservation Plan. In 2002 and 2003, we estimated occupancy and detection probability of ground squirrels across lands modeled as ground squirrel habitat by the Scientific Advisory Committee for the Habitat Conservation Plan and tested a priori hypotheses about how occupancy varied among vegetation and substrate types. In the 2003 study, we asked whether these associations were affected by winter rains after the 2002 drought year. Occupancy in 2003 was estimated at 0.99 (SE = 0.01) in Western honey mesquite (Prosopis glandulosa) on dunes and hummocks, and occupancy of the remaining modeled habitat was best described by distance to mesquite, with the occupancy probability decreasing with increasing distance from mesquite on dunes or hummocks. The best-supported model in 2002 described the distribution of ground squirrels as a function of only vegetation and substrate type. However, the best-supported models in 2003 suggested that distance to mesquite was a component of the occupancy of non-mesquite vegetation. Mesquite seems to provide high-quality habitat that can support ground squirrels at high occupancy probabilities that may breed successfully every year. In contrast, other vegetation types provide low-quality habitat that can only support ground squirrels at low occupancy probabilities that may only breed occasionally. Mesquite could be an essential refugium during drought years, and the 4 best-supported models in 2003 suggest that restoration of mesquite beginning near currently occupied mesquite patches could be critical for maintaining ground squirrel populations on the preserves.     

Using multiple methods to assess detection probabilities of forest-floor wildlife

Many previous comparisons of multiple sampling methods have assumed that detection probabilities for each method are either constant or equal to one. We used 4 sampling methods to estimate detection probabilities for forest-floor dwelling amphibians, reptiles, and small mammals. We investigated associations between seasonality and precipitation on species detection and explored sample design tradeoffs for future studies. Although we captured 25 species, we could reliably detect (detection probability >0.15) only northern short-tailed shrews (Blarina brevicauda) and pygmy and masked shrews (Sorex spp.) using drift fences and red-backed salamanders (Plethodon cinereus) using visual encounter surveys (VES). The use of multiple sampling methods improved detection probabilities for only red-backed salamanders ( = 0.32, 95% CI: 0.24–0.38, = 0.38, 95% CI: 0.32−0.44). Parameter estimates indicated detection of both shrew species was positively related to increased precipitation. Detection probabilities for pygmy and masked shrews and red-backed salamanders were positively and negatively associated with date, respectively. Our power analysis revealed that sampling during rain events increased the power of detecting a change in sorid occupancy by ≥40% (α = 0.05). Our results demonstrate the need to incorporate species detectability when comparing the effectiveness of different trapping methodologies. Furthermore, our study highlights the utility of power analyses for exploring study design tradeoffs for research and monitoring programs. © 2011 The Wildlife Society.     

Multi-species monitoring of rare wetland fishes should account for imperfect detection of sampling devices

Accuracy in estimating occupancy of a threatened species is important for conservation but false absences bias many monitoring programs. Imperfect detection is especially relevant to surveys of rare wetland fishes which are often small-bodied and cryptic. Many factors influence probability of detection, including fish size and abundance, habitat characteristics and sampling devices. Imperfect detection can be addressed by accounting for probability of detection when estimating occupancy by modelling detection/non-detection data collected in replicate surveys. Three ecological specialists were once common in habitats associated with Lake Alexandrina at the terminus of the Murray–Darling Basin, Australia. The threatened Murray Hardyhead (Craterocephalus fluviatilis), Southern Pygmy Perch (Nannoperca australis) and Yarra Pygmy Perch (N. obscura) are now rare in the region following population collapses during a prolonged drought, and ongoing monitoring aims to assess their statuses for management purposes. This study compares probability of detection of the rare wetland fishes and cohabiting species during 2 years of multi-species monitoring using contrasting sampling devices (fyke and seine). The findings suggest large variations in estimated probability of detection can occur between devices for Murray Hardyhead and Southern Pygmy Perch. Yarra Pygmy Perch was undetected during the study. Overall, the findings show multi-species monitoring programs using a single sampling device may wrongly estimate the occupancy of a target fish. By accounting for imperfect detection, multi-species monitoring programs will improve inferences regarding population status, recovery and habitat quality of fishes to more accurately inform wetland management.     

Estimating occupancy dynamics in an anuran assemblage from Louisiana,USA

Effective monitoring programs are designed to track changes in the distribution, occurrence, and abundance of species. We developed an extension of Royle and Kéry's (2007) single species model to estimate simultaneously temporal changes in probabilities of detection, occupancy, colonization, extinction, and species turnover using data on calling anuran amphibians, collected from 2002 to 2006 in the Lower Mississippi Alluvial Valley of Louisiana, USA. During our 5-year study, estimates of occurrence probabilities declined for all 12 species detected. These declines occurred primarily in conjunction with variation in estimates of local extinction probabilities (cajun chorus frog [Pseudacris fouquettei], spring peeper [P. crucifer], northern cricket frog [Acris crepitans], Cope's gray treefrog [Hyla chrysoscelis], green treefrog [H. cinerea], squirrel treefrog [H. squirella], southern leopard frog [Lithobates sphenocephalus], bronze frog [L. clamitans], American bullfrog [L. catesbeianus], and Fowler's toad [Anaxyrus fowleri]). For 2 species (eastern narrow-mouthed toad [Gastrophryne carolinensis] and Gulf Coast toad [Incilius nebulifer]), declines in occupancy appeared to be a consequence of both increased local extinction and decreased colonization events. The eastern narrow-mouthed toad experienced a 2.5-fold increase in estimates of occupancy in 2004, possibly because of the high amount of rainfall received during that year, along with a decrease in extinction and increase in colonization of new sites between 2003 and 2004. Our model can be incorporated into monitoring programs to estimate simultaneously the occupancy dynamics for multiple species that show similar responses to ecological conditions. It will likely be an important asset for those monitoring programs that employ the same methods to sample assemblages of ecologically similar species, including those that are rare. By combining information from multiple species to decrease the variance on estimates of individual species, our results are advantageous compared to single-species models. This feature enables managers and researchers to use an entire community, rather than just one species, as an ecological indicator in monitoring programs. © 2011 The Wildlife Society.     

Estimating site occupancy rates for aquatic plants using spatial sub-sampling designs when detection probabilities are less than one

Estimation of site occupancy rates when detection probabilities are <1 is well established in wildlife science. Data from multiple visits to a sample of sites are used to estimate detection probabilities and the proportion of sites occupied by focal species. In this article we describe how site occupancy methods can be applied to estimate occupancy rates of plants and other sessile organisms. We illustrate this approach and the pitfalls of ignoring incomplete detection using spatial data for 2 aquatic vascular plants collected under the Upper Mississippi River's Long Term Resource Monitoring Program (LTRMP). Site occupancy models considered include: a naïve model that ignores incomplete detection, a simple site occupancy model assuming a constant occupancy rate and a constant probability of detection across sites, several models that allow site occupancy rates and probabilities of detection to vary with habitat characteristics, and mixture models that allow for unexplained variation in detection probabilities. We used information theoretic methods to rank competing models and bootstrapping to evaluate the goodness-of-fit of the final models. Results of our analysis confirm that ignoring incomplete detection can result in biased estimates of occupancy rates. Estimates of site occupancy rates for 2 aquatic plant species were 19–36% higher compared to naive estimates that ignored probabilities of detection <1. Simulations indicate that final models have little bias when 50 or more sites are sampled, and little gains in precision could be expected for sample sizes >300. We recommend applying site occupancy methods for monitoring presence of aquatic species.     

Estimating the abundance of burrow‐nesting species through the statistical analysis of combined playback and visual surveys

The conservation of elusive species relies on our ability to obtain unbiased estimates of their abundance trends. Many species live or breed in cavities, making it easy to define the search units (the cavity) yet hard to ascertain their occupancy. One such example is that of certain colonial seabirds like petrels and shearwaters, which occupy burrows to breed. In order to increase the chances of detection for these types of species, their sampling can be done using two independent methods to check for cavity occupancy: visual inspection, and acoustic response to a playback call. This double‐detection process allows us to estimate the probability of burrow occupancy by accounting for the probability of detection associated with each method. Here we provide a statistical framework to estimate the occupancy and population size of burrow‐dwelling species. We show how to implement the method using both maximum likelihood and Bayesian approaches, and test its precision and bias using simulated datasets. We subsequently illustrate how to extend the method to situations where two different species may occupy the burrows, and apply it to a dataset on wedge‐tailed shearwaters Puffinus pacificus and tropical shearwaters P. bailloni on Aride Island, Seychelles. The simulations showed that the single‐species model performed well in terms of error and bias except when detection probabilities and occupancies were very low. The two‐species model applied to shearwaters showed that detection probabilities were highly heterogeneous. The population sizes of wedge‐tailed and tropical shearwaters were estimated at 13 716 (95% CI: 12 909–15 874) and 25 550 (23 667–28 777) pairs respectively. The advantages of formulating the call‐playback sampling method statistically is that it provides a framework to calculate uncertainty in the estimates and model assumptions. This method is applicable to a variety of cavity‐dwelling species where two methods can be used to detect cavity occupancy.     

Space‐for‐time is not necessarily a substitution when monitoring the distribution of pelagic fishes in the San Francisco Bay‐Delta

Occupancy models are often used to analyze long‐term monitoring data to better understand how and why species redistribute across dynamic landscapes while accounting for incomplete capture. However, this approach requires replicate detection/non‐detection data at a sample unit and many long‐term monitoring programs lack temporal replicate surveys. In such cases, it has been suggested that surveying subunits within a larger sample unit may be an efficient substitution (i.e., space‐for‐time substitution). Still, the efficacy of fitting occupancy models using a space‐for‐time substitution has not been fully explored and is likely context dependent. Herein, we fit occupancy models to Delta Smelt (Hypomesus transpacificus) and Longfin Smelt (Spirinchus thaleichthys) catch data collected by two different monitoring programs that use the same sampling gear in the San Francisco Bay‐Delta, USA. We demonstrate how our inferences concerning the distribution of these species changes when using a space‐for‐time substitution. Specifically, we found the probability that a sample unit was occupied was much greater when using a space‐for‐time substitution, presumably due to the change in the spatial scale of our inferences. Furthermore, we observed that as the spatial scale of our inferences increased, our ability to detect environmental effects on system dynamics was obscured, which we suspect is related to the tradeoffs associated with spatial grain and extent. Overall, our findings highlight the importance of considering how the unique characteristics of monitoring programs influences inferences, which has broad implications for how to appropriately leverage existing long‐term monitoring data to understand the distribution of species.     

Site occupancy models with heterogeneous detection probabilities

Models for estimating the probability of occurrence of a species in the presence of imperfect detection are important in many ecological disciplines. In these "site occupancy" models, the possibility of heterogeneity in detection probabilities among sites must be considered because variation in abundance (and other factors) among sampled sites induces variation in detection probability (p). In this article, I develop occurrence probability models that allow for heterogeneous detection probabilities by considering several common classes of mixture distributions for p. For any mixing distribution, the likelihood has the general form of a zero-inflated binomial mixture for which inference based upon integrated likelihood is straightforward. A recent paper by Link demonstrates that in closed population models used for estimating population size, different classes of mixture distributions are indistinguishable from data, yet can produce very different inferences about population size. I demonstrate that this problem can also arise in models for estimating site occupancy in the presence of heterogeneous detection probabilities. The implications of this are discussed in the context of an application to avian survey data and the development of animal monitoring programs.     

Towards the automated detection and occupancy estimation of primates using passive acoustic monitoring

Recent advancements in technology have made possible the use of novel, cost-efficient biomonitoring techniques which facilitate monitoring animal populations at larger spatial and temporal scales. Here, we investigated using passive acoustic monitoring (PAM) for wild primate populations living in the forest of Taï National Park, Côte d’Ivoire. We assessed the potential of using a customized algorithm for the automated detection of multiple primate species to obtain reliable estimates of species occurrence from acoustic data. First, we applied the algorithm on continuous rainforest recordings collected using autonomous recording units (ARUs) to detect and classify three sound signals: chimpanzee buttress drumming, and the loud calls of the diana and king colobus monkey. Using an occupancy modelling approach we then investigated to what extent the automated, probabilistic output needs to be listened to, and thus manually cleaned, by a human expert, to approach occupancy probabilities derived from ARU data fully verified by a human. To do this we explored the robustness of occupancy probability estimates by simulating ARU datasets with various degrees of cleaning for false positives and false negative detections. We further validated the approach by comparing it to data collected by human observers on point transects located within the same study area. Our study demonstrates that occurrence estimates from ARU data, combined with automated processing methods such as our algorithm, can provide results comparable to data collected by humans and require less effort. We show that occupancy probabilities are quite robust to cleaning effort, particularly when occurrence is high, and suggest that for some species even naïve occupancy, as derived from ARU data without any cleaning, could provide a quick and reliable indicator to guide monitoring efforts. We found detection probabilities to be most influenced by time of day for chimpanzee drums while temperature and, likely, poaching pressure, affected detection of diana monkey loud calls. None of the covariates investigated appeared to have strongly affected king colobus loud call detection. Finally, we conclude that the semi-automated approach presented here could be used as an early-warning system for poaching activity and suggest additional techniques for improving its performance.     

Assessing the impacts of imperfect detection on estimates of diversity and community structure through multispecies occupancy modeling

Detecting all species in a given survey is challenging, regardless of sampling effort. This issue, more commonly known as imperfect detection, can have negative impacts on data quality and interpretation, most notably leading to false absences for rare or difficult‐to‐detect species. It is important that this issue be addressed, as estimates of species richness are critical to many areas of ecological research and management. In this study, we set out to determine the impacts of imperfect detection, and decisions about thresholds for inclusion in occupancy, on estimates of species richness and community structure. We collected data from a stream fish assemblage in Algonquin Provincial Park to be used as a representation of ecological communities. We then used multispecies occupancy modeling to estimate species‐specific occurrence probabilities while accounting for imperfect detection, thus creating a more informed dataset. This dataset was then compared to the original to see where differences occurred. In our analyses, we demonstrated that imperfect detection can lead to large changes in estimates of species richness at the site level and summarized differences in the community structure and sampling locations, represented through correspondence analyses.     

Using occupancy modelling to compare environmental DNA to traditional field methods for regional‐scale monitoring of an endangered aquatic species

Environmental DNA (eDNA) monitoring approaches promise to greatly improve detection of rare, endangered and invasive species in comparison with traditional field approaches. Herein, eDNA approaches and traditional seining methods were applied at 29 research locations to compare method‐specific estimates of detection and occupancy probabilities for endangered tidewater goby (Eucyclogobius newberryi). At each location, multiple paired seine hauls and water samples for eDNA analysis were taken, ranging from two to 23 samples per site, depending upon habitat size. Analysis using a multimethod occupancy modelling framework indicated that the probability of detection using eDNA was nearly double (0.74) the rate of detection for seining (0.39). The higher detection rates afforded by eDNA allowed determination of tidewater goby occupancy at two locations where they have not been previously detected and at one location considered to be locally extirpated. Additionally, eDNA concentration was positively related to tidewater goby catch per unit effort, suggesting eDNA could potentially be used as a proxy for local tidewater goby abundance. Compared to traditional field sampling, eDNA provided improved occupancy parameter estimates and can be applied to increase management efficiency across a broad spatial range and within a diversity of habitats.     

Can Camera Traps Monitor Komodo Dragons a Large Ectothermic Predator?

Camera trapping has greatly enhanced population monitoring of often cryptic and low abundance apex carnivores. Effectiveness of passive infrared camera trapping, and ultimately population monitoring, relies on temperature mediated differences between the animal and its ambient environment to ensure good camera detection. In ectothermic predators such as large varanid lizards, this criterion is presumed less certain. Here we evaluated the effectiveness of camera trapping to potentially monitor the population status of the Komodo dragon (Varanus komodoensis), an apex predator, using site occupancy approaches. We compared site-specific estimates of site occupancy and detection derived using camera traps and cage traps at 181 trapping locations established across six sites on four islands within Komodo National Park, Eastern Indonesia. Detection and site occupancy at each site were estimated using eight competing models that considered site-specific variation in occupancy (ψ)and varied detection probabilities (p) according to detection method, site and survey number using a single season site occupancy modelling approach. The most parsimonious model [ψ (site), p (site*survey); ω = 0.74] suggested that site occupancy estimates differed among sites. Detection probability varied as an interaction between site and survey number. Our results indicate that overall camera traps produced similar estimates of detection and site occupancy to cage traps, irrespective of being paired, or unpaired, with cage traps. Whilst one site showed some evidence detection was affected by trapping method detection was too low to produce an accurate occupancy estimate. Overall, as camera trapping is logistically more feasible it may provide, with further validation, an alternative method for evaluating long-term site occupancy patterns in Komodo dragons, and potentially other large reptiles, aiding conservation of this species.