Utilizing occupancy-detection models with museum specimen data: promise and pitfalls

Historical museum records provide potentially useful data for identifying drivers of change in species occupancy. However, because museum records are typically obtained via many collection methods, methodological developments are needed in order to enable robust inferences. Occupancy-detection models, a relatively new and powerful suite of methods, are a potentially promising avenue because they can account for changes in collection effort through space and time. Here we present a methodological road-map for using occupancy models to analyze historical museum records. We use simulated data-sets to identify how and when patterns in data and/or modelling decisions can bias inference. We focus primarily on the consequences of contrasting methodological approaches for dealing with species’ ranges and inferring species’ non-detections in both space and time. We find that not all data-sets are suitable for occupancy-detection analysis but, under the right conditions (namely, data-sets that span long durations and contain a high fraction of community-wide collections, or collection events that focus on communities of organisms), models can accurately estimate trends. Finally, we present a case-study on eastern North American odonates where we calculate long-term trends of occupancy by using our most robust workflow.

Community, conflict and conservation: response of mammalian fauna to ecological and anthropological correlates ‒ a critical habitat in Indo-Bhutan transboundary landscape urges multiagency cooperation

The impacts of conflict on nature are devastatingly adverse but differ widely in different socio-political regimes. Armed conflict often facilitates illegal plunder and unsustainable use of natural resources, variously by rebel groups and impoverished or displaced people challenged with limited subsistence options. We studied the response of mammals in Ripu Reserve Forest (Assam) that suffers prolonged anthropogenic pressure due to armed conflict instigated by social unrest. We used standard single-season (spatial-dependence) occupancy models using sign survey to assess the factors affecting the space use of mammals and subsequently build capacity of conservation volunteers for long-term sustenance of Ripu. Our study revealed that Ripu has a high proportion of occupied area by prey species of large carnivores. Asian elephant, barking deer, and wild pig occupied most of the habitat, whereas gaur, sambar and spotted deer restricted themselves to selected patches within the Ripu. Common leopards found to be positively associated with prey occupancy. The studied mammals responded variably to different ecological and anthropological covariates and urge for species-specific management alongside landscape scale conservation approach. Our ground effort to strengthen community patrolling and operational execution of various alternative livelihood has helped to empower the economic condition of patrolling staff. Strategic implementation of law enforcement could support dispersal of tigers from Phibsoo WLS (Bhutan), potentially linked with the larger tiger and elephant landscape far west (Buxa Tiger Reserve) in the Terai region of India. Community-based conservation initiatives required continuous support from various agencies, including national, international, and local bodies, to restore this critical habitat.

Biogeographic multi-species occupancy models for large-scale survey data

Ecologists often seek to infer patterns of species occurrence or community structure from survey data. Hierarchical models, including multi-species occupancy models (MSOMs), can improve inference by pooling information across multiple species via random effects. Originally developed for local-scale survey data, MSOMs are increasingly applied to larger spatial scales that transcend major abiotic gradients and dispersal barriers. At biogeographic scales, the benefits of partial pooling in MSOMs trade off against the difficulty of incorporating sufficiently complex spatial effects to account for biogeographic variation in occupancy across multiple species simultaneously. We show how this challenge can be overcome by incorporating pre-existing range information into MSOMs, yielding a 'biogeographic multi-species occupancy model' (bMSOM). We illustrate the bMSOM using two published datasets: Parulid warblers in the United States Breeding Bird Survey, and entire avian communities in forests and pastures of Colombia's West Andes. Compared to traditional MSOMs, the bMSOM provides dramatically better predictive performance at lower computational cost. The bMSOM avoids severe spatial biases in predictions of the traditional MSOM and provides principled species-specific inference even for never-observed species. Incorporating pre-existing range data enables principled partial pooling of information across species in large-scale MSOMs. Our biogeographic framework for multi-species modeling should be broadly applicable in hierarchical models that predict species occurrences, whether or not false-absences are modeled in an occupancy framework.

Distribution of tiger salamanders in northern Sonora, Mexico: comparison of sampling methods and possible implications for an endangered subspecies

Many aquatic species in the arid USA-Mexico borderlands region are imperiled, but limited information on distributions and threats often hinders management. To provide information on the distribution of the Western Tiger Salamander (Ambystoma mavortium), including the USA-federally endangered Sonoran Tiger Salamander (Ambystoma mavortium stebbinsi), we used traditional (seines, dip-nets) and modern (environmental DNA [eDNA]) methods to sample 91 waterbodies in northern Sonora, Mexico, during 2015-2018. The endemic Sonoran Tiger Salamander is threatened by introgressive hybridization and potential replacement by another sub-species of the Western Tiger Salamander, the non-native Barred Tiger Salamander (A. m. mavortium). Based on occupancy models that accounted for imperfect detection, eDNA sampling provided a similar detection probability (0.82 [95% CI: 0.56-0.94]) as seining (0.83 [0.46-0.96]) and much higher detection than dip-netting (0.09 [0.02-0.23]). Volume of water filtered had little effect on detection, possibly because turbid sites had greater densities of salamanders. Salamanders were estimated to occur at 51 sites in 3 river drainages in Sonora. These results indicate tiger salamanders are much more widespread in northern Sonora than previously documented, perhaps aided by changes in land and water management practices. However, because the two subspecies of salamanders cannot be reliably distinguished based on morphology or eDNA methods that are based on mitochondrial DNA, we are uncertain if we detected only native genotypes or if we documented recent invasion of the area by the non-native sub-species. Thus, there is an urgent need for methods to reliably distinguish the subspecies so managers can identify appropriate interventions.

Tapirs in trouble: estimating Baird's tapir densities in the Sierra Madre de Chiapas, Mexico

Spatial capture–recapture models have been widely used to estimate densities of species where individuals can be uniquely identified, but alternatives have been developed for estimation of densities for unmarked populations. In this study we used camera-trap records from 2018 to estimate densities of a species that does not always have individually identifiable marks, Baird's tapir Tapirus bairdii, in the Sierra Madre de Chiapas, southern Mexico. We compared the performance of the spatial capture–recapture model with spatial mark–resight and random encounter models. The density of Baird's tapir did not differ significantly between the three models. The estimate of density was highest using the random encounter model (26/100 km2, 95% CI 12–41) and lowest using the capture–recapture model (8/100 km2, 95% CI 4–16). The estimate from the spatial mark–resight model was 10/100 km2 (95% CI 8–14), which had the lowest coefficient of variation, indicating a higher precision than with the other models. Using a second set of camera-trap data, collected in 2015–2016, we created occupancy models and extrapolated density to areas with potential occupancy of Baird's tapir, to generate a population estimate for the whole Sierra Madre de Chiapas. Our findings indicate the need to strengthen, and possibly expand, the protected areas of southern Mexico and to develop an action plan to ensure the conservation of Baird's tapir.

Relative species abundance and population densities of the past; developing multi-species occupancy models for fossil data

The number of individuals of species within communities varies, but estimating abundance, given incomplete and biased sampling, is challenging. Here, we describe a new occupancy model in a hierarchical Bayesian framework with random effects, where multi-species occupancy and detection are modeled as a means to estimate relative species abundance and relative population densities. The modelling framework is suited for occupancy data for temporal samples of fossil communities with repeated sampling including multiple species with similar preservation potential. We demonstrate our modelling framework using a fossil community of benthic organisms to estimate changing relative species abundance dynamics and relative population densities of focal species in 9 (geological) time-intervals over 2.3 million years. We also explored potential explanatory factors (paleoenvironmental proxies) and temporal autocorrelation that could provide extra information on unsampled time-intervals. The modelling framework is applicable across a wide range of questions on species-level dynamics in (palaeo)ecological community settings.

Modelling the Heterogeneity within Citizen Science Data for Biodiversity Research

Large amounts of species occurrence data are compiled by platforms such as the Global Biodiversity Information Facility (GBIF) but these data are collected by a diversity of methods and people. Statistical tools, such as occupancy-detection models, have been developed and tested as a way to analyze these heterogeneous data and extract information on species’ population trends. However, these models make many assumptions that might not always be met. More detailed metadata associated with occurrence records would help better describe the observation/detection submodel within occupancy models and improve the accuracy/precision of species’ trend estimates. Here, we present examples of occupancy-detection models applied to citizen science datasets, including dragonfly data in Germany, and typical approaches to account for variation in sampling effort and species detectability, including visit covariates, such as list length. Using results from a recent questionnaire in Germany asking citizen scientists about why and how they collect species occurrence data, we also characterize the different approaches that citizen scientists take to sample and report species observations. We use our findings to highlight examples of key metadata that are often missing (e.g., length of time spent searching, complete checklist or not) in data sharing platforms but would greatly aid modelling attempts of heterogeneous species occurrence data.