Javier Fernández-López

The choice of appropriate methods to detect species is crucial for biodiversity monitoring. Camera trapping is currently one of the most widely used methods for characterizing mammal communities, although it requires substantial investment in equipment and personnel. In contrast, questionnaires administered to local populations provide a faster and more cost-effective alternative for assessing community composition, but may be influenced by respondent-related biases that compromise data reliability. This study evaluates the concordance between these approaches for characterizing the carnivore community in the Sierra de Segura (Jaén, southern Spain), using Cohen’s kappa coefficient, while also examining the individual and social factors shaping Local Ecological Knowledge (LEK). We deployed 24 camera-trap stations (144 trap nights) across a 25 km² area to record carnivore presence. In parallel, we conducted two types of surveys with local residents (n = 103): (i) free-listing and (ii) image-based species recognition, while recording individual and social characteristics of respondents. Free-listing surveys tended to underreport species, whereas image-based surveys showed higher agreement with camera-trap data, although occasionally overestimating species presence. Higher concordance was associated with social factors indicative of closer and prolonged contact with the environment, such as permanent residence and ownership of agricultural land. Mammal communities differed between methods; however, agreement improved when respondents had higher LEK, while species-specific behavioral traits could also influence perception. Our findings demonstrate that image-based questionnaires can provide results comparable to camera trapping when respondents have strong connections to their natural surroundings. These results highlight the importance of both survey design and respondent selection in improving the accuracy of biodiversity monitoring, offering a transferable framework for integrating LEK into conservation protocols across diverse ecosystems.

The wild boar population in Europe has been growing in recent decades prior to the arrival of African swine fever (ASF), which has now spread across much of eastern Europe. We obtained two independent sources of wild boar data: occurrence sightings and hunting harvest. We combined these with environmental predictors and used them in two species distribution modelling approaches, a Maxent approach for occurrence data and a GLMM for the hunting harvest, to produce output at the European level. The output of these models was then combined with robust and comparable density estimates from 77 sites across Europe to produce a density estimate and total population size for each country prior to documenting ASF in that country (from 2007 for Georgia to 2022 for most EU countries that are still free from disease). The output indicates a total population of wild boar in Europe between 13.5 and 19.6 million individuals prior to the hunting season each year in the core wild boar range. Population estimates of wild boar in Europe based on occurrence sightings and hunting harvest are highly similar yet vary substantially among countries. Although the output may need to be adjusted where local factors affect the population (e.g., areas of range spread) the output can be used for assessing risk of disease spread and effect of management. We propose that the availability of density estimates from the European Observatory of Wildlife will permit robust population estimates for other species of interest since the methodology is consistent between species and habitats.

Knowledge about species abundance across broad spatial areas is crucial for unraveling ecological processes. Yet, abundance estimation often demands extensive sampling effort associated with logistical challenges. Using suitability values obtained from species distribution models (based on species' presence data) as a proxy for abundance has garnered interest during the last decades. Previous studies suggest a triangular relationship between species abundance and suitability. Specifically, higher suitability can display both low and high abundances, while low suitability only low abundances. This triangular pattern arises because suitability models often fail to consider limiting factors that drive abundance. In this study, we investigate the effect of spatial scale and pattern shaping this relationship. We use a simulation study and a case study to explore how these factors affect the abundance–suitability relationship. The effects of spatial scale are represented by three model levels: 1) only broad‐scale covariates, 2) broad and intermediate covariates, and 3) broad, intermediate and local covariates. The effects of spatial patterns are characterized by two different species distribution shapes: aggregated and uniform. Our findings reveal that models integrating local‐scale covariates and species exhibiting more aggregated spatial patterns show a stronger relationship. Additionally, we observe an interaction between a species' spatial pattern and model scale. For aggregated species, the abundance–suitability relationship benefits most notably from the addition of intermediate‐scale covariates. In contrast, for uniform species, the benefit remains consistent regardless of whether intermediate‐ or local‐scale covariates are added. Our results underscore the importance of considering both methodological and ecological factors to improve proxies for abundance derived from suitability models. We highlight the need for considering information operating at a local scale to make reliable inferences about species abundance from suitability models and suggest potential strategies for doing it.

Harvest data have the potential to be used as an abundance index due to its widespread availability and long‐term collection across large geographical areas. However, challenges such as the lack of hunting effort information, varying data resolutions and reporting biases hinder its direct use as an abundance proxy. Here, we present the game target‐group, a statistical approach based on a thinned inhomogeneous Poisson point process, to estimate animal abundance at fine‐scale resolution from hunting data. We employ a Bayesian hierarchical framework to borrow information from harvest data on related species to overcome issues due to the lack of hunting management information. We conducted a simulation study to explore model performance and parameter identifiability under different scenarios (sample size, species catchability/abundance and unmodelled heterogeneity) and assessed the method on a real case study with four species in central Spain. The simulation study confirmed that with a large enough sample size (n > 5000), high catchability and lack of unmodelled heterogeneity in the abundance process, the model was able to obtain unbiased estimations for total abundance parameters. In the case study, our model successfully captured species‐habitat relationships and produced reliable estimates of total abundance at regional scale. Internal validation with independent test data and external validation with fieldwork data confirmed the model's ability to predict hunting yields and estimate species total abundance accurately. Our approach provides a flexible and valuable tool for large‐scale monitoring programs relying on harvest data with potential applications in wildlife management and conservation. However, the method should be applied with caution when there is unmodelled heterogeneity, low catchability or the sample size is small (<5000).

Modelos de distribución de especies integrando diferentes fuentes de datos (software)

Agent‐based modelling (ABM) shows promise for animal movement studies. However, a robust, open‐source and spatially explicit ABM coding platform is currently lacking. We present abmR, an R package for conducting continental‐scale ABM simulations across animal taxa. The package features two movement functions, each of which relies on the Ornstein–Uhlenbeck (OU) process. The theoretical background for abmR is discussed and the main functionalities are illustrated using example populations. Potential future additions to this open‐source package may include the ability to specify multiple environmental variables or to model interactions between agents. Additionally, updates may offer opportunities for disease ecology and integration with other R movement modelling packages.