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Large carnivore conservation in human-dominated landscapes is a complex issue, often marked by the stark contrast between those who hold deep-rooted animosity towards these animals and those who welcome their presence. The survival of the Eurasian lynx Lynx lynx in Europe relies on effective coexistence with humans in multi-use areas. We explored the experiences and perceptions of local hunters and pastoralists regarding the return of the lynx to the Giffre Valley, France, and mapped lynx distribution based on the probability of site use while accounting for detection probability. We conducted in-depth interviews with 29 respondents to gather data on lynx sightings, rationale for hunting and pastoralism, and perceptions of lynxes. We found that 45% of respondents had detected lynxes in the last 40 years, with an estimated site use of 0.66 ± SE 0.33 over the last decade, indicating there was a 66% probability of lynxes using the sites during that time period. Our results suggest that hunting and pastoralism in the region are rooted in a desire to carry on local traditions and connect with the natural world. Respondents generally tolerated the presence of lynxes, perceiving few threats to their livelihoods and activities, and expressing a willingness to coexist peacefully. However, some identified future challenges that could arise with the return of large carnivores to the valley and highlighted scenarios that could lead to a decline in tolerance. This study emphasizes the valuable knowledge of local hunters and pastoralists and their potential role in lynx population monitoring and conservation. Integrating stakeholder values in decision-making processes is crucial for inclusive and sustainable responses to promote biodiversity.

Habitat selection is strongly scale‐dependent, and inferring the characteristic scale at which an organism responds to environmental variation is necessary to obtain reliable predictions. The occupancy framework is frequently used to model species distribution with the advantage of accounting for imperfect observation, but occupancy studies typically do not define the characteristic scale of the modelled variables. We used camera trap data from winter wildlife surveys in the Swiss part of the Jura Mountains to model occupancy of wild boar (Sus scrofa) and roe deer (Capreolus capreolus). We used a three‐step approach: (1) first, we identified factors influencing detectability; (2) second, we optimized the characteristic scale of each candidate explanatory variable; and (3) third, we fit multivariable, multiscale occupancy models in relation to land cover, human presence and topography. Wild boar occupancy was mainly influenced by the interaction between elevation within 2500 m and the proportion of forested areas within a 2500 m, with a nonsignificant additional effect of the interaction between ruggedness within 1900 m and the proportion of forested areas within 2500 m as well as the distance to urban areas. Roe deer occupancy was mainly associated with the interaction between ruggedness within 900 m and the proportion of open landscape within 900 m, with an additional nonsignificant effect of the interaction between elevation within 1500 m and the proportion of open landscape within 900 m as well as the distance to urban areas. Incorporating scale optimization in occupancy modelling of camera trap data can greatly improve the understanding of species‐environment relationships by combining the possibility of occupancy models to correct for detection bias and simultaneously allowing to infer the characteristic scale at which certain factors influence the distribution of the organisms studied. While occupancy models deal effectively with the imperfect detection probability that is typical for camera trap data, few occupancy studies address the inherent scale‐dependency of habitat selection. Using camera trap data from winter wildlife surveys in the Swiss part of the Jura Mountains, we modelled the scale‐optimised occupancy of wild boar (Sus scrofa) and roe deer (Capreolus capreolus) to unravel the influence of land cover, topography and human disturbance on their habitat selection. This scale optimisation approach can greatly enhance traditional occupancy modelling by accounting for the characteristic scale at which environmental factors drive habitat selection, thereby revealing the appropriate scale at which to model habitat use.

Wolves have large spatial requirements and their expansion in Europe is occurring over national boundaries, hence the need to develop monitoring programs at the population level. Wolves in the Alps are defined as a functional population and management unit. The range of this wolf Alpine population now covers seven countries: Italy, France, Austria, Switzerland, Slovenia, Liechtenstein and Germany, making the development of a joint and coordinated monitoring program particularly challenging. In the framework of the Wolf Alpine Group (WAG), researchers developed uniform criteria for the assessment and interpretation of field data collected in the frame of different national monitoring programs. This standardization allowed for data comparability across borders and the joint evaluation of distribution and consistency at the population level. We documented the increase in the number of wolf reproductive units (packs and pairs) over 21 years, from 1 in 1993–1994 up to 243 units in 2020–2021, and examined the pattern of expansion over the Alps. This long-term and large-scale approach is a successful example of transboundary monitoring of a large carnivore population that, despite administrative fragmentation, provides robust indexes of population size and distribution that are of relevance for wolf conservation and management at the transnational Alpine scale.

In Switzerland, the European wildcat (Felis silvestris), a native felid, is protected by national law. In recent decades, the wildcat has slowly returned to much of its original range and may have even expanded into new areas that were not known to be occupied before. For the implementation of efficient conservation actions, reliable information about the status and trend of population size and density is crucial. But so far, only one reliable estimate of density in Switzerland was produced in the northern Swiss Jura Mountains. Wildcats are relatively rare and elusive, but camera trapping has proven to be an effective method for monitoring felids. We developed and tested a monitoring protocol using camera trapping in the northern Jura Mountains (cantons of Bern and Jura) in an area of 100 km2. During 60 days, we obtained 105 pictures of phenotypical wildcats of which 98 were suitable for individual identification. We identified 13 individuals from both sides and, additionally, 5 single right‐sided flanks and 3 single left‐sided flanks that could not be matched to unique individuals. We analyzed the camera‐trap data using the R package multimark, which has been extended to include a novel spatial capture–recapture model for encounter histories that include multiple “noninvasive” marks, such as bilaterally asymmetrical left‐ and right‐sided flanks, that can be difficult (or impossible) to reliably match to individuals. Here, we present this model in detail for the first time. Based on a “semi‐complete” data likelihood, the model is less computationally demanding than Bayesian alternatives that rely on a data‐augmented complete data likelihood. The spatially explicit capture–recapture model estimated a wildcat density (95% credible interval) of 26 (17–36) per 100 km2 suitable habitat. Our integrated model produced higher abundance and density estimates with improved precision compared to single‐sided analyses, suggesting spatially explicit capture–recapture methods with multiple “noninvasive” marks can improve our ability to monitor wildcat population status. In this study, we developed and tested a monitoring protocol for the European wildcat using camera trapping. Reliable population estimates as density and abundance are often lacking for this elusive species. We analyzed our camera trap data using the open‐source R package multimark, which has been extended to include a novel spatial capture–recapture model.

Background Sarcoptic mange is a contagious skin disease of wild and domestic mammals caused by the mite Sarcoptes scabiei . Reports of sarcoptic mange in wildlife increased worldwide in the second half of the 20th century, especially since the 1990s. The aim of this study was to provide new insights into the epidemiology of mange by (i) documenting the emergence of sarcoptic mange in the red fox ( Vulpes vulpes ) in the last decades in Switzerland; and (ii) describing its spatiotemporal spread combining data obtained through different surveillance methods. Methods Retrospective analysis of archived material together with prospective data collection delivered a large dataset from the 19th century to 2018. Methods included: (i) a review of historical literature; (ii) screening of necropsy reports from general health surveillance (1958–2018); (iii) screening of data on mange (1968–1992) collected during the sylvatic rabies eradication campaign; (iv) a questionnaire survey (<1980–2017) and (v) evaluation of camera-trap bycatch data (2005–2018). Results Sarcoptic mange in red foxes was reported as early as 1835 in Switzerland. The first case diagnosed in the framework of the general health surveillance was in 1959. Prior to 1980, sarcoptic mange occurred in non-adjacent surveillance districts scattered all over the country. During the period of the rabies epidemic (1970s-early 1990s), the percentage of foxes tested for rabies with sarcoptic mange significantly decreased in subregions with rabies, whereas it remained high in the few rabies-free subregions. Sarcoptic mange re-emerged in the mid-1990s and continuously spread during the 2000–2010s, to finally extend to the whole country in 2017. The yearly prevalence of mange in foxes estimated by camera-trapping ranged from 0.1–12%. Conclusions Sarcoptic mange has likely been endemic in Switzerland as well as in other European countries at least since the mid-19th century. The rabies epidemics seem to have influenced the pattern of spread of mange in several locations, revealing an interesting example of disease interaction in free-ranging wildlife populations. The combination of multiple surveillance tools to study the long-term dynamics of sarcoptic mange in red foxes in Switzerland proved to be a successful strategy, which underlined the usefulness of questionnaire surveys.

Camera trapping has revolutionized wildlife ecology and conservation by providing automated data acquisition, leading to the accumulation of massive amounts of camera trap data worldwide. Although management and processing of camera trap‐derived Big Data are becoming increasingly solvable with the help of scalable cyber‐infrastructures, harmonization and exchange of the data remain limited, hindering its full potential. There is currently no widely accepted standard for exchanging camera trap data. The only existing proposal, “Camera Trap Metadata Standard” (CTMS), has several technical shortcomings and limited adoption. We present a new data exchange format, the Camera Trap Data Package (Camtrap DP), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP structures camera trap data in a simple yet flexible data model consisting of three tables (Deployments, Media and Observations) that supports a wide range of camera deployment designs, classification techniques (e.g., human and AI, media‐based and event‐based) and analytical use cases, from compiling species occurrence data through distribution, occupancy and activity modeling to density estimation. The format further achieves interoperability by building upon existing standards, Frictionless Data Package in particular, which is supported by a suite of open software tools to read and validate data. Camtrap DP is the consensus of a long, in‐depth, consultation and outreach process with standard and software developers, the main existing camera trap data management platforms, major players in the field of camera trapping and the Global Biodiversity Information Facility (GBIF). Under the umbrella of the Biodiversity Information Standards (TDWG), Camtrap DP has been developed openly, collaboratively and with version control from the start. We encourage camera trapping users and developers to join the discussion and contribute to the further development and adoption of this standard. We present a new data exchange format for camera trap data, the Camera Trap Data Package (Camtrap DP; https://github.com/tdwg/camtrap-dp), designed to allow users to easily exchange, harmonize and archive camera trap data at local to global scales. Camtrap DP is being developed under the umbrella of the Biodiversity Information Standards (TDWG), and through outreach and collaboration, it is now supported by GBIF. Importantly, Camtrap DP is the consensus of a long, in depth consultation process among the main existing camera trap data management platforms, as well as some of the major global players in the field of camera trapping. As an open, evolving standard for the FAIR exchange and archive of camera trap data, Camtrap DP represents an important step towards a global data sharing workflow with rapid results and thus more timely science based wildlife management recommendations.

When wild‐caught Eurasian lynx (Lynx lynx) from the Slovak Carpathian Mountains were reintroduced to Central Switzerland in the early 1970s and spread through the north‐western Swiss Alps (NWA), they faced a largely unfamiliar landscape with strongly fragmented forests, high elevations, and intense human land use. For more than 30 years, radio‐collared lynx have been monitored during three different project periods (in the 1980s, 1990s, and 2010s). Our study explored, how lynx over generations have learned to adjust to the alpine environment. We predicted that (1) lynx nowadays select more strongly for open habitats, higher elevations, and steep slopes compared to the early stages of recolonization and that (2) consequently, there were significant changes in the Eurasian lynx’ prey spectrum. To test our predictions, we analyzed telemetry data (VHF, GPS) of 13 adult resident lynx in the NWA over 35 years, using Resource Selection Functions. Furthermore, we compared kills recorded from different individuals inhabiting the same region during three project periods. In general, lynx preferred forested areas, but over the years, they avoided open habitat less. Compared to the early stage of the recolonization, lynx in the most recent project period selected for higher elevations and the proportion of chamois in their prey spectrum surmounted that of roe deer. Potential driving factors for the observed changes could be increasing tolerance to human presence, intraspecific competition, or fitness benefits through exploitation of new resources. Long‐term studies like ours provide important insight into how animals can respond to sudden environmental changes, e.g., in the course of translocations into new areas or anthropogenic alterations of their habitats. Our study explored, whether reintroduced Eurasian lynx to the north‐western Swiss Alps, over generations have learned to adjust to the Alpine environment. Compared to the early period of recolonization, lynx nowadays selected for higher elevations and avoided open habitats less. The proportion of Alpine chamois in lynx prey spectrum surmounted that of roe deer. Driving factors for the observed changes could be increasing tolerance to human presence, intraspecific competition or fitness benefits through exploitation of new resources.

During November 2010–February 2011, we used camera traps to estimate the population density of Eurasian lynx Lynx lynx in Ciglikara Nature Reserve, Turkey, an isolated population in southwest Asia. Lynx density was calculated through spatial capture—recapture models. In a sampling eff ort of 1093 camera trap days, we identifi ed 15 independent individuals and estimated a density of 4.20 independent lynx per 100 km2, an unreported high density for this species. Camera trap results also indicated that the lynx is likely to be preying on brown hare Lepus europaeus, which accounted for 63% of the non-target species pictured. As lagomorph populations tend to fl uctuate, the high lynx density recorded in Ciglikara may be temporary and may decline with prey fl uctuation. Therefore we recommend to survey other protected areas in southwestern Turkey where lynx is known or assumed to exist, and continuously monitor the lynx populations with reliable methods in order to understand the populations structure and dynamics, defi ne sensible measures and management plans to conserve this important species.

Use of photographic capture–recapture analyses to estimate abundance of species with distinctive natural marks has become an important tool for monitoring rare or cryptic species, or both. Two different methods are available to estimate density: nonspatial capture–recapture models where the trap polygon is buffered with the half or full mean maximum distance moved by animals captured at more than 1 trap (1/2 MMDM or MMDM, respectively); or spatial capture–recapture (SCR) models that explicitly incorporate movement into the model. We used data from radiotracked Eurasian lynx (Lynx lynx) in the northwestern Swiss Alps (NWSA) during a low (1.0 lynx/100 km2) and a high (1.9–2.1 lynx/100 km2) lynx population density to test if lynx space use was density dependent. Second, we compared lynx density estimates resulting from these 2 different methods using camera-trapping data collected during winters 2007–2008 and 2009–2010 in the NWSA. Our results indicated lynx space use was negatively correlated with density. Lynx density estimates in all habitats using MMDM (0.86 and 0.97 lynx/100 km2 in winters 2007–2008 and 2009–2010, respectively) were significantly lower than SCR model estimates, whereas there was no significant difference between SCR model (1.47 and 1.38) and 1/2 MMDM (1.37 and 1.51) density estimates. In the NWSA, which currently harbors the most abundant lynx population in Switzerland, 1/2 MMDM and SCR models provided more realistic lynx density estimates compared to the MMDM, which lies in the lower range of densities. Overall, the SCR model is preferable because it considers animal movements explicitly and is not biased by an informal estimation of the effective sampling area.

Automatically triggered cameras taking photographs or videos of passing animals (camera traps) have emerged over the last decade as one of the most powerful tool for wildlife research. In parallel, a wealth of camera trap systems and models has become commercially available, a phenomenon mainly driven by the increased use of camera traps by sport hunters. This has raised the need for developing criteria to choose the suitable camera trap model in relation to a range of factors, primarily the study aim, but also target species, habitat, trapping site, climate and any other aspect that affects camera performance. There is also fragmented information on the fundamentals of sampling designs that deploy camera trapping, such as number of sampling sites, spatial arrangement and sampling duration. In this review, we describe the relevant technological features of camera traps and propose a set of the key ones to be evaluated when choosing camera models. These features are camera specifications such as trigger speed, sensor sensitivity, detection zone, flash type and flash intensity, power autonomy, and related specifications. We then outline sampling design and camera features for the implementation of major camera trapping applications, specifically: (1) faunal inventories, (2) occupancy studies, (3) density estimation through Capture-Mark-Recapture and (4) density estimation through the Random Encounter Model. We also review a range of currently available models and stress the need for standardized testing of camera models that should be frequently updated and widely distributed. Finally we summarize the \"ultimate camera trap\", as desired by wildlife biologists, and the current technological limitations of camera traps in relation to their potential for a number of emerging applications.