Using sample aerial surveys to estimate the abundance of the endangered Grevy’s zebra in northern Kenya

The effective management of endangered mammals requires reliable estimates of population size. This is challenging for species such as Grevy’s zebra (Equus grevyi) that are distributed over large areas at low densities. Less than 2500 Grevy’s zebra remain in the wild, scattered across 85,000 km² of savannah in northern Kenya and Ethiopia. An efficient, accurate and repeatable survey method is required to guide conservation planning for the species. Currently, total aerial counts are used to census endangered species within Kenya, but are costly in terms of resources. In this study, we evaluated the suitability of sample survey methods for Grevy’s zebra. We estimated population size using sample aerial counts for a known population of Grevy’s zebra in Lewa Wildlife Conservancy (LWC), providing the opportunity to test the accuracy of sample methods, while comparing resource costs with total count methods. We sampled one‐third of LWC using parallel 500‐ m strip transects at 1500‐ m intervals. The population estimate was comparable to the known population size and was less than half as expensive as the equivalent total count survey. Our results suggest sample aerial surveys provide an accurate and cost‐effective means of monitoring Grevy’s zebra and other endangered species in open habitats.     

Estimating indices of range shifts in birds using dynamic models when detection is imperfect

There is intense interest in basic and applied ecology about the effect of global change on current and future species distributions. Projections based on widely used static modeling methods implicitly assume that species are in equilibrium with the environment and that detection during surveys is perfect. We used multiseason correlated detection occupancy models, which avoid these assumptions, to relate climate data to distributional shifts of Louisiana Waterthrush in the North American Breeding Bird Survey (BBS) data. We summarized these shifts with indices of range size and position and compared them to the same indices obtained using more basic modeling approaches. Detection rates during point counts in BBS surveys were low, and models that ignored imperfect detection severely underestimated the proportion of area occupied and slightly overestimated mean latitude. Static models indicated Louisiana Waterthrush distribution was most closely associated with moderate temperatures, while dynamic occupancy models indicated that initial occupancy was associated with diurnal temperature ranges and colonization of sites was associated with moderate precipitation. Overall, the proportion of area occupied and mean latitude changed little during the 1997–2013 study period. Near‐term forecasts of species distribution generated by dynamic models were more similar to subsequently observed distributions than forecasts from static models. Occupancy models incorporating a finite mixture model on detection – a new extension to correlated detection occupancy models – were better supported and may reduce bias associated with detection heterogeneity. We argue that replacing phenomenological static models with more mechanistic dynamic models can improve projections of future species distributions. In turn, better projections can improve biodiversity forecasts, management decisions, and understanding of global change biology.     

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.     

Development and field validation of a regional,management‐scale habitat model: A koala Phascolarctos cinereus case study

Species distribution models have great potential to efficiently guide management for threatened species, especially for those that are rare or cryptic. We used MaxEnt to develop a regional‐scale model for the koala Phascolarctos cinereus at a resolution (250 m) that could be used to guide management. To ensure the model was fit for purpose, we placed emphasis on validating the model using independently‐collected field data. We reduced substantial spatial clustering of records in coastal urban areas using a 2‐km spatial filter and by modeling separately two subregions separated by the 500‐m elevational contour. A bias file was prepared that accounted for variable survey effort. Frequency of wildfire, soil type, floristics and elevation had the highest relative contribution to the model, while a number of other variables made minor contributions. The model was effective in discriminating different habitat suitability classes when compared with koala records not used in modeling. We validated the MaxEnt model at 65 ground‐truth sites using independent data on koala occupancy (acoustic sampling) and habitat quality (browse tree availability). Koala bellows (n = 276) were analyzed in an occupancy modeling framework, while site habitat quality was indexed based on browse trees. Field validation demonstrated a linear increase in koala occupancy with higher modeled habitat suitability at ground‐truth sites. Similarly, a site habitat quality index at ground‐truth sites was correlated positively with modeled habitat suitability. The MaxEnt model provided a better fit to estimated koala occupancy than the site‐based habitat quality index, probably because many variables were considered simultaneously by the model rather than just browse species. The positive relationship of the model with both site occupancy and habitat quality indicates that the model is fit for application at relevant management scales. Field‐validated models of similar resolution would assist in guiding management of conservation‐dependent species.     

Effects of point‐count duration on estimated detection probabilities and occupancy of breeding birds

Increasingly, point‐count data are used to estimate occupancy, the probability that a species is present at a given location; occupancy accounts for imperfect detection, the probability that a species is detected given that it is present. To our knowledge, effects of sampling duration on inferences from models of bird occupancy have not been evaluated. Our objective was to determine whether changing count duration from 5 to 8 min affected inferences about the occupancy of birds sampled in the Chesapeake Bay Lowlands (eastern United States) and the central and western Great Basin (western United States) in 2012 and 2013. We examined the proportion of species (two doves, one cuckoo, two swifts, five hummingbirds, 11 woodpeckers, and 122 passerines) for which estimates of detection probability were ≥ 0.3. For species with single‐season detection probabilities ≥ 0.3, we compared occupancy estimates derived from 5‐ and 8‐min counts. We also compared estimates for three species sampled annually for 5 yr in the central Great Basin. Detection probabilities based on both the 5‐ and 8‐min counts were ≥ 0.3 for 40% ± 3% of the species in an ecosystem. Extending the count duration from 5 to 8 min increased the detection probability to ≥ 0.3 for 5% ± 0.5% of the species. We found no difference in occupancy estimates that were based on 5‐ versus 8‐min counts for species sampled over two or five consecutive years. However, for 97% of species sampled over 2 yr, precision of occupancy estimates that were based on 8‐min counts averaged 12% ± 2% higher than those based on 5‐min counts. We suggest that it may be worthwhile to conduct a pilot season to determine the number of locations and surveys needed to achieve detection probabilities that are sufficiently high to estimate occupancy for species of interest.     

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.     

Using a species distribution model to guide NSW surveys of the long‐footed potoroo (Potorous longipes)

Knowledge of threatened species’ distributions is essential for effective conservation decision‐making. Species distribution models (SDMs) are widely used to map species’ geographic ranges, identify new areas of suitable habitat and guide field surveys. In New South Wales (NSW), Australia, there are grave doubts about whether populations of the critically endangered long‐footed potoroo (Potorous longipes) remain extant, and identification of occupied sites is a high priority for its conservation. We used an SDM (Maxent) to identify regions in NSW that may have suitable habitat for the potoroo. The SDM was built with seven climate layers and had strong predictive performance (cross‐validated AUC = 0.94). We then combined this information on habitat suitability with vegetation and topography, to identify 58 survey sites across NSW. From April 2016 to May 2017, we undertook six field trips deploying six to eight cameras at each site for 52–63 days, resulting in 25 120 camera trap nights. A total of 215 759 images captured 43 native and feral animal species, but no long‐footed potoroos. Following the survey, newly available, independent presence and absence data were used to validate our model. A Kruskal–Wallis H test indicated that habitat suitability values were significantly higher at presence locations than absence locations (H = 58.66, d.f. = 1, P < 0.001). Finally, we refitted the Maxent model with the new data and identified additional regions that future surveys could explore. We conclude, however, that if the long‐footed potoroo remains extant in NSW, it is extremely rare.     

Logistic regression when covariates are random effects from a non‐linear mixed model

In many studies, the association of longitudinal measurements of a continuous response and a binary outcome are often of interest. A convenient framework for this type of problems is the joint model, which is formulated to investigate the association between a binary outcome and features of longitudinal measurements through a common set of latent random effects. The joint model, which is the focus of this article, is a logistic regression model with covariates defined as the individual‐specific random effects in a non‐linear mixed‐effects model (NLMEM) for the longitudinal measurements. We discuss different estimation procedures, which include two‐stage, best linear unbiased predictors, and various numerical integration techniques. The proposed methods are illustrated using a real data set where the objective is to study the association between longitudinal hormone levels and the pregnancy outcome in a group of young women. The numerical performance of the estimating methods is also evaluated by means of simulation.     

Quantitative assessment of species identification in aerial transect surveys for ice‐associated seals

Technological advances have facilitated collection of vast quantities of photographic data from aerial surveys of marine mammals. However, when it is difficult to distinguish species from a distance, reliable identification from aerial images can often be challenging. This is the case for ice‐associated seals, species for which global climate change has motivated intensive monitoring efforts in recent years. We assess species and age class identification from aerial images of four ice seal species (bearded seals, Erignathus barbatus; ribbon seals, Histriophoca fasciata; ringed seals, Pusa hispida; spotted seals, Phoca largha) in the Bering Sea. We also investigate the specific phenomenological and behavioral traits commonly associated with species identification and observer confidence. We generally found species and age class misidentification occurred at relatively low levels, but only 83% of spotted seals tended to be correctly identified (with 11% mistaken as ribbon seals). We also found certain traits were strong predictors for observed species, age class, or observer confidence. Our findings add to the growing body of evidence that species misidentification is pervasive in passive sampling of animal populations. Even low levels of misidentification have been demonstrated to induce substantial biases in estimators of species distribution and abundance, and it is important that statistical models account for such errors.     

The influence of non‐climate predictors at local and landscape resolutions depends on the autecology of the species

Species distribution models have come under criticism for being too simplistic for making robust future forecasts, partly because they assume that climate is the main determinant of geographical range at large spatial extents and coarse resolutions, with non‐climate predictors being important only at finer scales. We suggest that this paradigm might be obscured by species movement patterns. To explore this we used contrasting kangaroo (family Macropodidae) case studies: two species with relatively small, stable home ranges (Macropus giganteus and M. robustus) and three species with more extensive, adaptive ranging behaviour (M. antilopinus, M. fuliginosus and M. rufus). We predicted that non‐climate predictors will be most influential to model fit and predictive performance at local spatial resolution for the former species and at landscape resolution for the latter species. We compared residuals autocovariate – boosted regression tree (RAC‐BRT) model statistics with and without species‐specific non‐climate predictors (habitat, soil, fire, water and topography), at local‐ and landscape‐level spatial resolutions (5 and 50 km). As predicted, the influence of non‐climate predictors on model fit and predictive performance (compared with climate‐only models) was greater at 50 compared with 5 km resolution for M. rufus and M. fuliginosus and the opposite trend was observed for M. giganteus. The results for M. robustus and M. antilopinus were inconclusive. Also notable was the difference in inter‐scale importance of climate predictors in the presence of non‐climate predictors. In conclusion, differences in autecology, particularly relating to space use, may contribute to the importance of non‐climate predictors at a given scale, not model scale per se. Further exploration of this concept across a range of species is encouraged and findings may contribute to more effective conservation and management of species at ecologically meaningful scales.     

Sharing detection heterogeneity information among species in community models of occupancy and abundance can strengthen inference

1. The estimation of abundance and distribution and factors governing patterns in these parameters is central to the field of ecology. The continued development of hierarchical models that best utilize available information to inform these processes is a key goal of quantitative ecologists. However, much remains to be learned about simultaneously modeling true abundance, presence, and trajectories of ecological communities.
2. Simultaneous modeling of the population dynamics of multiple species provides an interesting mechanism to examine patterns in community processes and, as we emphasize herein, to improve species‐specific estimates by leveraging detection information among species. Here, we demonstrate a simple but effective approach to share information about observation parameters among species in hierarchical community abundance and occupancy models, where we use shared random effects among species to account for spatiotemporal heterogeneity in detection probability.
3. We demonstrate the efficacy of our modeling approach using simulated abundance data, where we recover well our simulated parameters using N‐mixture models. Our approach substantially increases precision in estimates of abundance compared with models that do not share detection information among species. We then expand this model and apply it to repeated detection/non‐detection data collected on six species of tits (Paridae) breeding at 119 1 km² sampling sites across a P. montanus hybrid zone in northern Switzerland (2004–2020). We find strong impacts of forest cover and elevation on population persistence and colonization in all species. We also demonstrate evidence for interspecific competition on population persistence and colonization probabilities, where the presence of marsh tits reduces population persistence and colonization probability of sympatric willow tits, potentially decreasing gene flow among willow tit subspecies.
4. While conceptually simple, our results have important implications for the future modeling of population abundance, colonization, persistence, and trajectories in community frameworks. We suggest potential extensions of our modeling in this paper and discuss how leveraging data from multiple species can improve model performance and sharpen ecological inference.