Model selection with overdispersed distance sampling data

We thank the Robert Bosch Foundation, the Max Planck Society and the University of St Andrews for funding.1. Distance sampling (DS) is a widely used framework for estimating animal abundance. DS models assume that observations of distances to animals are independent. Non‐independent observations introduce overdispersion, causing model selection criteria such as AIC or AICc to favour overly complex models, with adverse effects on accuracy and precision. 2. We describe, and evaluate via simulation and with real data, estimators of an overdispersion factor (ĉ), and associated adjusted model selection criteria (QAIC) for use with overdispersed DS data. In other contexts, a single value of ĉ is calculated from the “global” model, that is the most highly parameterised model in the candidate set, and used to calculate QAIC for all models in the set; the resulting QAIC values, and associated ΔQAIC values and QAIC weights, are comparable across the entire set. Candidate models of the DS detection function include models with different general forms (e.g. half‐normal, hazard rate, uniform), so it may not be possible to identify a single global model. We therefore propose a two‐step model selection procedure by which QAIC is used to select among models with the same general form, and then a goodness‐of‐fit statistic is used to select among models with different forms. A drawback of thi approach is that QAIC values are not comparable across all models in the candidate set. 3. Relative to AIC, QAIC and the two‐step model selection procedure avoided overfitting and improved the accuracy and precision of densities estimated from simulated data. When applied to six real datasets, adjusted criteria and procedures selected either the same model as AIC or a model that yielded a more accurate density estimate in five cases, and a model that yielded a less accurate estimate in one case. 4. Many DS surveys yield overdispersed data, including cue counting surveys of songbirds and cetaceans, surveys of social species including primates, and camera‐trapping surveys. Methods that adjust for overdispersion during the model selection stage of DS analyses therefore address a conspicuous gap in the DS analytical framework as applied to species of conservation concern.PostprintPeer reviewe

Deer Behavior Affects Density Estimates With Camera Traps, but Is Outwighted by Spatial Variability

Density is a key trait of populations and an essential parameter in ecological research, wildlife conservation and management. Several models have been developed to estimate population density based on camera trapping data, including the random encounter model (REM) and camera trap distance sampling (CTDS). Both models need to account for variation in animal behavior that depends, for example, on the species and sex of the animals along with temporally varying environmental factors. We examined whether the density estimates of REM and CTDS can be improved for Europe’s most numerous deer species, by adjusting the behavior-related model parameters per species and accounting for differences in movement speeds between sexes, seasons, and years. Our results showed that bias through inadequate consideration of animal behavior was exceeded by the uncertainty of the density estimates, which was mainly influenced by variation in the number of independent observations between camera trap locations. The neglection of seasonal and annual differences in movement speed estimates for REM overestimated densities of red deer in autumn and spring by ca. 14%. This GPS telemetry-derived parameter was found to be most problematic for roe deer females in summer and spring when movement behavior was characterized by small-scale displacements relative to the intervals of the GPS fixes. In CTDS, density estimates of red deer improved foremost through the consideration of behavioral reactions to the camera traps (avoiding bias of max. 19%), while species-specific delays between photos had a larger effect for roe deer. In general, the applicability of both REM and CTDS would profit profoundly from improvements in their precision along with the reduction in bias achieved by exploiting the available information on animal behavior in the camera trap data.Deer Behavior Affects Density Estimates With Camera Traps, but Is Outwighted by Spatial VariabilitypublishedVersio

Predicting Range Shifts of African Apes and Effectiveness of Protected Areas under Global Change Scenarios

First paragraph: Given a burgeoning human population and rapidly-growing global demand for natural resources, reconciling biodiversity conservation and human-related activities is a fundamental challenge. Tropical forests support at least two-thirds of the world's biodiversity, providing important ecosystem services at both global and local scales. However, a decline of 3% in global forest cover was reported between 2010 and 2015, with the highest rates of land-use change and degradation found in the tropics, where deforestation rates exceeded five million hectares per year. Africa had an annual rate of net forest loss at 3.9 million hectares between 2010 and 2020, and has up to 400 million hectares of forest that could potentially be used for agricultural expansion. Therefore, continued widespread expansion of agriculture is likely. Moreover, the African continent is the most vulnerable to the effects of climate change, and future droughts, floods and other extreme weather events will lead to the expansion of agriculture into more humid tropical areas. These areas are where great apes live and are generally high in biodiversity

Sustainable protected areas: Synergies between biodiversity conservation and socioeconomic development

1. Reconciling conservation and socioeconomic development goals is key to sus- tainability but remains a source of fierce debate. Protected areas (PAs) are be- lieved to play an essential role in achieving these seemingly conflicting goals. Yet, there is limited evidence as to whether PAs are actually achieving the two goals simultaneously. 2. Here, we investigate when and to what extent synergies or trade- offs between biodiversity conservation and local socioeconomic development occur. To ex- plore these relationships, we collected data across a wide range of socioeco- nomic settings through face-to-face survey with PA managers from 114 African and European PAs using structured questionnaire. 3. We found synergies between biodiversity conservation and socioeconomic development for 62% of the PAs, albeit with significant differences between African (55%) and European PAs (75%). Moreover, the sustainability of PAs in conserving biodiversity was strongly correlated with the empowerment of the PA management and the involvement of local communities in PA planning and decision-making processes. 4. Our results demonstrate that for PAs to promote synergies between biodiver- sity conservation and local socioeconomic development, and to enhance their long-term sustainability, they should invest in the empowerment of their respec- tive management and involvement of local communities in their planning and management activitie

Sustainable protected areas: Synergies between biodiversity conservation and socioeconomic development

1. Reconciling conservation and socioeconomic development goals is key to sustainability but remains a source of fierce debate. Protected areas (PAs) are believed to play an essential role in achieving these seemingly conflicting goals. Yet, there is limited evidence as to whether PAs are actually achieving the two goals simultaneously. 2. Here, we investigate when and to what extent synergies or trade‐offs between biodiversity conservation and local socioeconomic development occur. To explore these relationships, we collected data across a wide range of socioeconomic settings through face‐to‐face survey with PA managers from 114 African and European PAs using structured questionnaire. 3. We found synergies between biodiversity conservation and socioeconomic development for 62% of the PAs, albeit with significant differences between African (55%) and European PAs (75%). Moreover, the sustainability of PAs in conserving biodiversity was strongly correlated with the empowerment of the PA management and the involvement of local communities in PA planning and decision‐making processes. 4. Our results demonstrate that for PAs to promote synergies between biodiversity conservation and local socioeconomic development, and to enhance their long‐term sustainability, they should invest in the empowerment of their respective management and involvement of local communities in their planning and management activities

Analysis of differences and commonalities in wildlife hunting across the Africa-Europe South-North gradient

Hunting and its impacts on wildlife are typically studied regionally, with a particular focus on the Global South. Hunting can, however, also undermine rewilding efforts or threaten wildlife in the Global North. Little is known about how hunting manifests under varying socioeconomic and ecological contexts across the Global South and North. Herein, we examined differences and commonalities in hunting characteristics across an exemplary Global South-North gradient approximated by the Human Development Index (HDI) using face-to-face interviews with 114 protected area (PA) managers in 25 African and European countries. Generally, we observed that hunting ranges from the illegal, economically motivated, and unsustainable hunting of herbivores in the South to the legal, socially and ecologically motivated hunting of ungulates within parks and the illegal hunting of mainly predators outside parks in the North. Commonalities across this Africa-Europe South-North gradient included increased conflict-related killings in human-dominated landscapes and decreased illegal hunting with beneficial community conditions, such as mutual trust resulting from community involvement in PA management. Nevertheless, local conditions cannot outweigh the strong effect of the HDI on unsustainable hunting. Our findings highlight regional challenges that require collaborative, integrative efforts in wildlife conservation across actors, while identified commonalities may outline universal mechanisms for achieving this goal.publishedVersio

Deforestation projections imply range-wide population decline for critically endangered Bornean orangutan

Assessing where wildlife populations are at risk from future habitat loss is particularly important for land-use planning and avoiding biodiversity declines. Combining projections of future deforestation with species density information provides an improved way to anticipate such declines. Using the critically endangered Bornean orangutan (Pongo pygmaeus) as a case study we applied a spatio-temporally explicit deforestation model to forest loss data from 2001-2017 and projected future impacts on orangutans to the 2030s. Our projections point to continued deforestation across the island, amounting to a potential loss of forest habitat for 26,200 orangutans. Populations currently persisting in forests gazetted for industrial timber and oil palm concessions, or unprotected forests outside of concessions, were projected to experience the worst losses within the next 15 years, amounting to 15,400 individuals. Our analysis indicates the importance of protecting orangutan habitat in plantation landscapes, maintaining protected areas and efforts to prevent the conversion of logged forests for the survival of highly vulnerable wildlife. The modeling framework could be expanded to other species with available density or occurrence data. Our findings highlight that species conservation should not only act on the current information, but also anticipate future changes to be effective

PanAf20K : a large video dataset for wild ape detection and behaviour recognition

The work that allowed for the collection of the dataset was funded by the Max Planck Society, Max Planck Society Innovation Fund, and Heinz L. Krekeler. This work was supported by the UKRI CDT in Interactive AI under grant EP/S022937/1.We present the PanAf20K dataset, the largest and most diverse open-access annotated video dataset of great apes in their natural environment. It comprises more than 7 million frames across ∼20,000 camera trap videos of chimpanzees and gorillas collected at 18 field sites in tropical Africa as part of the Pan African Programme: The Cultured Chimpanzee. The footage is accompanied by a rich set of annotations and benchmarks making it suitable for training and testing a variety of challenging and ecologically important computer vision tasks including ape detection and behaviour recognition. Furthering AI analysis of camera trap information is critical given the International Union for Conservation of Nature now lists all species in the great ape family as either Endangered or Critically Endangered. We hope the dataset can form a solid basis for engagement of the AI community to improve performance, efficiency, and result interpretation in order to support assessments of great ape presence, abundance, distribution, and behaviour and thereby aid conservation efforts. The dataset and code are available from the project website: PanAf20KPeer reviewe

Threat of mining to African great apes

The rapid growth of clean energy technologies is driving a rising demand for critical minerals. In 2022 at the 15th Conference of the Parties to the Convention on Biological Diversity (COP15), seven major economies formed an alliance to enhance the sustainability of mining these essential decarbonization minerals. However, there is a scarcity of studies assessing the threat of mining to global biodiversity. By integrating a global mining dataset with great ape density distribution, we estimated the number of African great apes that spatially coincided with industrial mining projects. We show that up to one-third of Africa's great ape population faces mining-related risks. In West Africa in particular, numerous mining areas overlap with fragmented ape habitats, often in high-density ape regions. For 97% of mining areas, no ape survey data are available, underscoring the importance of increased accessibility to environmental data within the mining sector to facilitate research into the complex interactions between mining, climate, biodiversity, and sustainability