Biology subjects

Sollmann, R.

Publications and source records attributed to Sollmann, R..

8 recordsLinked to original sources

bioRxiv2025

The camtrapR R package: From data management to interactive ecological analysis of camera trap data

O_LICamera trapping has become an indispensable tool in wildlife ecology, generating vast datasets that require efficient and robust analytical workflows. The R package camtrapR was originally developed for preparing and managing camera trap data for subsequent analysis in external modeling packages like unmarked. It has since become a standard tool in the field for this purpose. C_LIO_LIHere, we introduce a major update that transforms camtrapR from a data preparation tool into a comprehensive, end-to-end analytical platform. The centerpiece of this evolution is the surveyDashboard(), a novel code-free graphical user interface that guides users through the entire analysis pipeline, from data import to final predictions. This update also incorporates enhanced data import functionalities for major standards like Wildlife Insights and Camtrap DP, a complete workflow for fitting community occupancy models, and streamlined tools for environmental covariate extraction. C_LIO_LIThe interactive dashboard provides an integrated environment for the entire analytical process. Users can perform essential exploratory analyses, such as generating species accumulation curves and mapping species detections, before proceeding to model fitting. The interface supports the interactive construction of both single-species and multi-species (community) occupancy models. The dashboards covariate preparation tools generate inputs for both model fitting and for creating spatial predictions of species occupancy. C_LIO_LIFurthermore, the update introduces a comprehensive workflow for fitting Bayesian community occupancy models using JAGS or NIMBLE. This allows for hierarchical modeling of species- and community-level responses to environmental drivers, providing deeper insights into wildlife communities. The workflow includes tools for model assessment, such as convergence diagnostics and posterior predictive checks for goodness-of-fit. C_LIO_LIBy integrating a powerful, code-free interface with advanced backend modeling functions, this major update to camtrapR aims to make robust and reproducible camera trap data analysis accessible to a wider audience, including ecologists, wildlife managers, and students. This paper serves as the new definitive reference for the expanded functionality of camtrapR as a comprehensive tool for modern camera trap studies. C_LI

ecology↗

bioRxiv2023

Accounting for observation biases associated with counts of young when estimating fecundity: case study on the arboreal-nesting red kite (Milvus milvus)

AO_SCPLOWBSTRACTC_SCPLOWCounting the number of young in a brood from a distance is common practice, for example in tree-nesting birds. These counts can, however, suffer from over and undercounting, which can lead to biased estimates of fecundity (average number of nestlings per brood). Statistical model development to account for observation bias has focused on false negatives (undercounts), yet it has been shown that these models are sensitive to the presence of false positives (overcounts) when they are not accounted for. Here, we develop a model that estimates fecundity while accounting for both false positives and false negatives in brood counts. Its parameters can be estimated using a calibration approach that combines uncertain counts with certain ones, which can be obtained by accessing the brood, for example during ringing. The model uses multinomial distributions to estimate the probabilities of observing y young conditional on the true state of a brood z (i.e., true number of young) from paired uncertain and certain counts. These classification probabilities are then used to estimate the true state of broods for which only uncertain counts are available. We use a simulation study to investigate bias and precision of the model and parameterize the simulation with empirical data from 26 red kite nests visited with ground and nest-based counts during 2021 and 2022 in central Europe. In these data, bias in counts was at most 1 in either direction, more common in larger broods, and undercounting was more common than overcounting. This led to an overall 5% negative bias in fecundity in uncertain counts. The model produced essentially unbiased estimates (relative bias < 2%) of fecundity across a range of sample sizes. This held true whether or not fecundity was the same for nests with paired counts and those with uncertain-only counts. But the model could not estimate parameters when true states were missing from the paired data, which happened frequently in small sample sizes (n = 10 or 25). Further, we projected populations 50 years into the future using fecundity estimates corrected for observation biases from the multinomial model, and based on "raw" uncertain observations. We found that ignoring observation bias led to strong negative bias in projected population size for growing populations, but only minor negative bias in declining populations. Accounting for apparently minor biases associated with ground counts is important for ensuring accurate estimates of abundance and population dynamics especially for increasing populations. This could be particularly important for informing conservation decisions in projects aimed at recovering depleted populations.

ecology↗

bioRxiv2023

Mt or not Mt: Temporal variation in detection probability in spatial capture-recapture and occupancy models

State variables such as abundance and occurrence of species are central to many questions in ecology and conservation, but our ability to detect and enumerate species is imperfect and often varies across space and time. Accounting for imperfect and variable detection is important for obtaining unbiased estimates of state variables. Here, I investigate whether closed spatial capture-recapture (SCR) and single season occupancy models are robust to ignoring temporal variation in detection probability. Ignoring temporal variation allows collapsing detection data across repeated sampling occasions, speeding up computations, which can be important when analyzing large datasets with complex models. I simulated data under different scenarios of temporal and spatio-temporal variation in detection, analyzed data with the data-generating model and an alternative model ignoring temporal variation in detection, and compared estimates between these two models with respect to relative bias, coefficient of variation (CV) and relative root mean squared error (RMSE). SCR model estimates of abundance, the density-covariate coefficient {beta} and the movement-related scale parameter of the detection function {sigma} were robust to ignoring temporal variation in detection, with relative bias, CV and RMSE of the two models generally being within 4% of each other. An SCR case study for brown tree snakes showed identical estimates of density and {sigma} under models accounting for or ignoring temporal variation in detection. Occupancy model estimates of the occupancy-covariate coefficient {beta} and average occupancy were also largely robust to ignoring temporal variation in detection, and differences in occupancy predictions were mostly <<0.1. But there was a slight tendency for bias in {beta} under the alternative model to increase when detection varied more strongly over time. Thus, when temporal variation in detection is extreme, it may be necessary to model that variation to avoid bias in parameter estimates in occupancy models. An occupancy case study for ten bird species with a more complex model structure showed considerable differences in occupancy parameter estimates under models accounting for or ignoring temporal variation in detection; but estimates and predictions from the latter were always within 95% confidence intervals of the former. There are cases where we cannot or may not want to ignore temporal variation in detection: a behavioral response to detection and certain SCR observation models do not allow collapsing data across sampling occasions; and temporal variation in detection may be informative of species phenology/behavior or for future study planning. But this study shows that it can be safely ignored under a range of conditions when analyzing SCR or occupancy data.

ecology↗

bioRxiv2023

Can snare removal safeguard protected areas caught in the Southeast Asian snaring crisis? Promises and perspectives

Unsustainable snaring is causing biodiversity declines across tropical protected areas, resulting in species extinctions and jeopardizing the health of forest ecosystems. Here, we used 11 years of ranger collected data to assess the impact of intensive snare removal on snaring levels in two protected areas in Viet Nam. Snare removal resulted in significant declines in snare occupancy (36.9, 95% BCI [4.6, 59.0] reduction in percent area occupied), but snaring levels nonetheless remained high (31.4, [23.6, 40.8] percent area occupied), and came with a substantial financial cost. Our results indicate that snare removal remains an important component of efforts to protect tropical protected areas, but by itself, is likely insufficient to address this threat. To stop snaring in protected areas, a multifaceted approach will be necessary that combines short-term reactive snare removal with long-term proactive programs that address the underlying drivers behind snaring.

ecology↗

bioRxiv2021

Noninvasive genetic surveys before and after a megafire detect displacement of migratory mule deer

Due to climate change and past logging and fire suppression, the western US are experiencing increasingly large and frequent wildfires. Understanding how wildlife respond to these mega-fires is becoming increasingly relevant to protect and manage these populations. However, the lack of predictability inherent in such events makes studies difficult to plan. We took advantage of a large high-severity wildfire that burned adjacent to an ongoing study of mule deer (Odocoileus hemionus) on their summer range upslope of the fire to investigate their displacement onto our study area both immediately and upon their return to summer range the following year. We used spatial capture-recapture models in conjunction with noninvasive fecal DNA sampling to estimate density and non-spatial Pradel robust-design models to estimate apparent survival and recruitment rates. Compared to density before the fire, we observed an increase in deer density and an increase in per-capita recruitment rates one month after the fire. These findings suggest that the immediate response of at least some deer was to flee the fire upslope onto the study area rather than to downslope toward their winter range. These changes did not carry over into the following year, however, suggesting that deer formerly using the burned area as summer range may have returned there despite the high severity of the fire, or may have chosen new areas for their summer range. This suggests that, at least in the short term, the fire did not negatively affect the deer population.

ecology↗

bioRxiv2020

Capturing biodiversity complexities while accounting for imperfect detection: the application of occupancy-based diversity profiles.

O_LIMeasuring the multidimensional diversity properties of a community is of great importance for ecologists, conservationists and stakeholders. Diversity profiles, a plotted series of Hill numbers, simultaneously capture all the common diversity indices. However, diversity metrics require information on species abundance. They often rely on raw count data without accounting for imperfect and varying detection, although detectability can vary between species and study sites. Hierarchical occupancy models explicitly account for variation in detectability, and Hill numbers have been expanded to allow estimation based on occupancy probability. But agreement between occupancy and abundance-based diversity profiles has not been investigated. C_LIO_LIHere, we fit community occupancy models to simulated animal communities to explore how well occupancy-based diversity profiles reflect true abundance-based diversity. Because we expect occupancy-based diversity to be overestimated, we further tested a novel occupancy thresholding approach to reduce potential biases in the estimated diversity profiles. Finally, we use empirical data from a megadiverse bird community to present how the framework can be extended to consider trait or phylogeny-based similarity when calculating diversity profiles. C_LIO_LIThe simulation study showed that occupancy-based diversity profiles produced among-community patterns in diversity similar to true abundance diversity profiles, although within-community diversity was overestimated with the exception of richness. While applying an occupancy threshold reduced this positive bias, this resulted in negative bias in species richness estimates and slightly reduced the ability to reproduce true differences among the simulated communities. Application of our approach to a large bird dataset revealed differential diversity patterns in communities of different habitat types. Accounting for phylogenetic and ecological similarities between species reduced diversity and its variability among habitats. C_LIO_LIOur framework allows investigating the complexity of diversity for incidence data, while accounting for imperfect and varying detection probabilities, as well as species similarities. Visualizing results in the form of diversity profiles facilitates comparison of diversity between sites or across time. Therefore, our extension to the diversity profile framework will be a useful tool for studying and monitoring biodiversity. C_LI

ecology↗

bioRxiv2019

Identifying conservation priorities in a defaunated tropical biodiversity hotspot

AimUnsustainable hunting is leading to widespread defaunation across the tropics. To mitigate against this threat with limited conservation resources, stakeholders must make decisions on where to focus anti-poaching activities. Identifying priority areas in a robust way allows decision-makers to target areas of conservation importance, therefore maximizing the impact of conservation interventions.\n\nLocationAnnamite mountains, Vietnam and Laos.\n\nMethodsWe conducted systematic landscape-scale surveys across five study sites (four protected areas, one unprotected area) using camera-trapping and leech-derived environmental DNA. We analyzed detections within a Bayesian multi-species occupancy framework to evaluate species responses to environmental and anthropogenic influences. Species responses were then used to predict occurrence to unsampled regions. We used predicted species richness maps and occurrence of endemic species to identify areas of conservation importance for targeted conservation interventions.\n\nResultsAnalyses showed that habitat-based covariates were uninformative. Our final model therefore incorporated three anthropogenic covariates as well as elevation, which reflects both ecological and anthropogenic factors. Conservation-priority species tended to found in areas that are more remote now or have been less accessible in the past, and at higher elevations. Predicted species richness was low and broadly similar across the sites, but slightly higher in the more remote site. Occupancy of the three endemic species showed a similar trend.\n\nMain conclusionIdentifying spatial patterns of biodiversity in heavily-defaunated landscapes may require novel methodological and analytical approaches. Our results indicate to build robust prediction maps it is beneficial to sample over large spatial scales, use multiple detection methods to increase detections for rare species, include anthropogenic covariates that capture different aspects of hunting pressure, and analyze data within a Bayesian multi-species framework. Our models further suggest that more remote areas should be prioritized for anti-poaching efforts to prevent the loss of rare and endemic species.

ecology↗

bioRxiv2018

Shifting up a gear with iDNA: from mammal detection events to standardized surveys

O_LIInvertebrate-derived DNA (iDNA), in combination with high throughput sequencing, has been proposed as a cost-efficient and powerful tool to survey vertebrate species. Previous studies, however, have only provided evidence that vertebrates can be detected using iDNA, but have not taken the next step of placing these detection events within a statistical framework that allows for robust biodiversity assessments.\nC_LIO_LIHere, we compare concurrent iDNA and camera-trap surveys. Leeches were repeatedly collected in close vicinity to 64 camera-trap stations in Sabah, Malaysian Borneo. We analyze iDNA-derived mammalian detection events in a modern occupancy model that accounts for imperfect detection and compare the results with those from occupancy models parameterized with camera-trap-derived detection events. We also combine leech-iDNA and camera-trap data in a single occupancy model.\nC_LIO_LIWe found consistent estimates of occupancy probabilities produced by our camera-trap and leech datasets. This indicates that the metabarcoding of leech-iDNA method provides reasonable estimates of occupancy and can be a suitable method for studying and monitoring mammal species in tropical rainforests. However, we also show that a more extensive collection of leeches would be needed to assess mammal biodiversity with a similar robustness as with camera traps. As certain taxa were only detected in leeches, we see great potential in complementing camera-trap studies with the iDNA approach, as long as the collection of leeches follows a similar robust and standardized sampling scheme.\nC_LIO_LISynthesis and applications. The approach we describe here is not restricted to the processing of leech samples, but can be used for the analysis of other iDNA and environmental DNA (eDNA) data. Our study is the first step to shift the application of e/iDNA studies from opportunistic ad-hoc collections to systematic surveys required for long-term wildlife populations and biodiversity monitoring programs.\nC_LI

ecology↗