Drivers of habitat availability for terrestrial mammals: Unravelling the role of livestock, land conversion and intrinsic traits in the past 50 years.

The global decline of terrestrial species is largely due to the degradation, loss and fragmentation of their habitats. The conversion of natural ecosystems for cropland, rangeland, forest products and human infrastructure are the primary causes of habitat deterioration. Due to the paucity of data on the past distribution of species and the scarcity of fine-scale habitat conversion maps, however, accurate assessment of the recent effects of habitat degradation, loss and fragmentation on the range of mammals has been near impossible. We aim to assess the proportions of available habitat within the lost and retained parts of mammals' distribution ranges, and to identify the drivers of habitat availability. We produced distribution maps for 475 terrestrial mammals for the range they occupied 50 years ago and compared them to current range maps. We then calculated the differences in the percentage of 'area of habitat' (habitat available to a species within its range) between the lost and retained range areas. Finally, we ran generalized linear mixed models to identify which variables were more influential in determining habitat availability in the lost and retained parts of the distribution ranges. We found that 59% of species had a lower proportion of available habitat in the lost range compared to the retained range, thus hypothesizing that habitat loss could have contributed to range declines. The most important factors negatively affecting habitat availability were the conversion of land to rangeland and high density of livestock. Significant intrinsic traits were those related to reproductive timing and output, habitat breadth and medium body size. Our findings emphasize the importance of implementing conservation strategies to mitigate the impacts caused by human activities on the habitats of mammals, and offer evidence indicating which species have the potential to reoccupy portions of their former range if other threats cease to occur.

Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence-Belgium as a study case.

BACKGROUND: The COVID-19 pandemic is affecting nations globally, but with an impact exhibiting significant spatial and temporal variation at the sub-national level. Identifying and disentangling the drivers of resulting hospitalisation incidence at the local scale is key to predict, mitigate and manage epidemic surges, but also to develop targeted measures. However, this type of analysis is often not possible because of the lack of spatially-explicit health data and spatial uncertainties associated with infection. METHODS: To overcome these limitations, we propose an analytical framework to investigate potential drivers of the spatio-temporal heterogeneity in COVID-19 hospitalisation incidence when data are only available at the hospital level. Specifically, the approach is based on the delimitation of hospital catchment areas, which allows analysing associations between hospitalisation incidence and spatial or temporal covariates. We illustrate and apply our analytical framework to Belgium, a country heavily impacted by two COVID-19 epidemic waves in 2020, both in terms of mortality and hospitalisation incidence. RESULTS: Our spatial analyses reveal an association between the hospitalisation incidence and the local density of nursing home residents, which confirms the important impact of COVID-19 in elderly communities of Belgium. Our temporal analyses further indicate a pronounced seasonality in hospitalisation incidence associated with the seasonality of weather variables. Taking advantage of these associations, we discuss the feasibility of predictive models based on machine learning to predict future hospitalisation incidence. CONCLUSION: Our reproducible analytical workflow allows performing spatially-explicit analyses of data aggregated at the hospital level and can be used to explore potential drivers and dynamic of COVID-19 hospitalisation incidence at regional or national scales.

VectAbundance: a spatio-temporal database of Aedes mosquitoes observations.

Modelling approaches play a crucial role in supporting local public health agencies by estimating and forecasting vector abundance and seasonality. However, the reliability of these models is contingent on the availability of standardized, high-quality data. Addressing this need, our study focuses on collecting and harmonizing egg count observations of the mosquito Aedes albopictus, obtained through ovitraps in monitoring and surveillance efforts across Albania, France, Italy, and Switzerland from 2010 to 2022. We processed the raw observations to obtain a continuous time series of ovitraps observations allowing for an extensive geographical and temporal coverage of Ae. albopictus population dynamics. The resulting post-processed observations are stored in the open-access database VectAbundance.This initiative addresses the critical need for accessible, high-quality data, enhancing the reliability of modelling efforts and bolstering public health preparedness.

The Effect of Habitat on Insect Movements: Experimental Evidence from Wild-Caught Butterflies.

There is broad evidence that the main driver of the ongoing biodiversity crisis is land-use change, which reduces and fragments habitats. The consequence of habitat fragmentation on behavioural responses of fitness-related traits in insects have been so far understudied. In herbivorous insects, oviposition-related behaviours determine access to larval food, and the fate of the next generation. We present a pilot study to assess differences in behaviours related to movement and oviposition in Limenitis camilla butterflies from Wallonia (Belgium), one of the most fragmented regions in Europe. We first quantified variation in functional habitat connectivity across Wallonia and found that fragmented habitats had more abundant, but less evenly distributed host plants of L. camilla. Secondly, we quantified the behaviours of field-caught L. camilla females originating from habitats with contrasted landscape connectivity in an outdoor experimental setting. We found differences in behaviours related to flight investment: butterflies from fragmented woodlands spent more time in departing flight, which we associated with dispersal, than butterflies from homogenous woodlands. Although results from this study should be interpreted with caution given the limited sample size, they provide valuable insights for the advancement of behavioural research that aims to assess the effects of global changes on insects.

A quixotic view of spatial bias in modelling the distribution of species and their diversity.

Ecological processes are often spatially and temporally structured, potentially leading to autocorrelation either in environmental variables or species distribution data. Because of that, spatially-biased in-situ samples or predictors might affect the outcomes of ecological models used to infer the geographic distribution of species and diversity. There is a vast heterogeneity of methods and approaches to assess and measure spatial bias; this paper aims at addressing the spatial component of data-driven biases in species distribution modelling, and to propose potential solutions to explicitly test and account for them. Our major goal is not to propose methods to remove spatial bias from the modelling procedure, which would be impossible without proper knowledge of all the processes generating it, but rather to propose alternatives to explore and handle it. In particular, we propose and describe three main strategies that may provide a fair account of spatial bias, namely: (i) how to represent spatial bias; (ii) how to simulate null models based on virtual species for testing biogeographical and species distribution hypotheses; and (iii) how to make use of spatial bias - in particular related to sampling effort - as a leverage instead of a hindrance in species distribution modelling. We link these strategies with good practice in accounting for spatial bias in species distribution modelling.

Modelling habitat suitability of the invasive tick Rhipicephalus microplus in West Africa.

Ticks have medical and economic importance due to their ability to transmit pathogens to humans and animals. In tropical and sub-tropical countries, tick-borne diseases (TBD) are among the most important diseases affecting livestock and humans. The fast spread of ticks and TBD requires a quick development and application of efficient prevention and/or control programs. Therefore, prior investigations on TBD and related vectors epidemiology, for instance, through accurate epidemiological models, are mandatory. This study aims to develop models to forecast suitable habitat for Rhipicephalus microplus distribution in West Africa. Tick occurrences were assembled from 10 different studies carried out in six West African countries in the past decade. Six statistical models (maximum entropy in a single model and generalised linear model, generalised additive model, random forest, boosted regression tree and support vector machine model in an ensemble model) were applied and compared to predict the habitat suitability of R. microplus distribution in West Africa. Each model was evaluated with the area under the receiver operating characteristic curve (AUC), the true skill statistic (TSS) and the Boyce index (BI). The selected models had good performance according to their AUC (above .8), TSS (above .7) and BI (above .8). Temperature played a key role in MaxEnt model, whereas normalised difference vegetation index (NDVI) was the most important variable in the ensemble model. The model predictions showed coastal countries of West Africa as more suitable for R. microplus. However, some Sahelian areas seems also favourable. We stress the importance of vector surveillance and control in countries that have not yet detected R. microplus but are in the areas predicted to host suitable habitat. Indeed, awareness-raising and training of different stakeholders must be reinforced for better prevention and control of this tick in these different countries according to their status.

dynamAedes: a unified modelling framework for invasive Aedes mosquitoes.

Mosquito species belonging to the genus Aedes have attracted the interest of scientists and public health officers because of their capacity to transmit viruses that affect humans. Some of these species were brought outside their native range by means of trade and tourism and then colonised new regions thanks to a unique combination of eco-physiological traits. Considering mosquito physiological and behavioural traits to understand and predict their population dynamics is thus a crucial step in developing strategies to mitigate the local densities of invasive Aedes populations. Here, we synthesised the life cycle of four invasive Aedes species (Ae. aegypti, Ae. albopictus, Ae. japonicus and Ae. koreicus) in a single multi-scale stochastic modelling framework which we coded in the R package dynamAedes. We designed a stage-based and time-discrete stochastic model driven by temperature, photo-period and inter-specific larval competition that can be applied to three different spatial scales: punctual, local and regional. These spatial scales consider different degrees of spatial complexity and data availability by accounting for both active and passive dispersal of mosquito species as well as for the heterogeneity of the input temperature data. Our overarching aim was to provide a flexible, open-source and user-friendly tool rooted in the most updated knowledge on the species' biology which could be applied to the management of invasive Aedes populations as well as to more theoretical ecological inquiries.

Poultry farm distribution models developed along a gradient of intensification.

Efficient planning of measures limiting epidemic spread requires information on farm locations and sizes (number of animals per farm). However, such data are rarely available. The intensification process which is operating in most low- and middle-income countries (LMICs), comes together with a spatial clustering of farms, a characteristic epidemiological models are sensitive to. We developed farm distribution models predicting both the location and the number of animals per farm, while accounting for the spatial clustering of farms in data-poor countries, using poultry production as an example. We selected four countries, Nigeria, Thailand, Argentina and Belgium, along a gradient of intensification expressed by the per capita Gross Domestic Product (GDP). First, we investigated the distribution of chicken farms along the spectrum of intensification. Second, we built farm distribution models (FDM) based on censuses of commercial farms of each of the four countries, using point pattern and random forest models. As an external validation, we predicted farm locations and sizes in Bangladesh. The number of chicken per farm increased gradually in line with the gradient of GDP per capita in the following order: Nigeria, Thailand, Argentina and Belgium. Interestingly, we did not find such a gradient for farm clustering. Our modelling procedure could only partly reproduce the observed datasets in each of the four sample countries in internal validation. However, in the external validation, the clustering of farms could not be reproduced and the spatial predictors poorly explained the number and location of farms and farm sizes in Bangladesh. Further improvements of the methodology should explore other covariates of the intensity of farms and farm sizes, as well as improvements of the methodology. Structural transformation, economic development and environmental conditions are essential characteristics to consider for an extrapolation of our FDM procedure, as generalisation appeared challenging. We believe the FDM procedure could ultimately be used as a predictive tool in data-poor countries.

rasterdiv-An Information Theory tailored R package for measuring ecosystem heterogeneity from space: To the origin and back.

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow.In this paper, we present a new R package-rasterdiv-to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns.The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms.

Validation of particulate dispersion models by native lichens as point receptors: a case study from NE Italy.

Atmospheric dispersion model (ADM) simulations are increasingly used as management tools in air pollution monitoring programs, even in the absence of proper validation. Biomonitors can provide important information for ADM validation, but an open question is their temporal frame of application, particularly when native organisms are used. In this study, we tested two alternative ADM simulating the total suspended particulate (TSP) released by a coal power station, against the element content of two native lichens collected at 40 sites, integrated by soil samples. The ADM simulations differed by the time references: the 6-month period preceding lichen sampling, approximately corresponding to the estimated age of the samples (Mod. A), and the whole year 2005, representative of the local average conditions and used in the plant authorization processes (Mod. B). A generalized regression model analysis clearly showed that the Cr, Pb and V content of lichen samples was spatially associated to the outcomes of Mod. A, but not with Mod. B. Interestingly, the Cr content of lichen samples consistently correlated to TSP concentration predicted by Mod. A along two transects placed downwind from the coal power station. This result was corroborated by an air particulate matter sampling which pointed out that air Cr concentrations increased during the operative period of the source. Overall, our results suggest that lichen bioaccumulation data can proficiently be used to validate ADM simulations if the exposure time of the biological samples is consistent with the temporal domain of the ADM simulations.

Downscaling livestock census data using multivariate predictive models: Sensitivity to modifiable areal unit problem.

The analysis of census data aggregated by administrative units introduces a statistical bias known as the modifiable areal unit problem (MAUP). Previous researches have mostly assessed the effect of MAUP on upscaling models. The present study contributes to clarify the effects of MAUP on the downscaling methodologies, highlighting how a priori choices of scales and shapes could influence the results. We aggregated chicken and duck fine-resolution census in Thailand, using three administrative census levels in regular and irregular shapes. We then disaggregated the data within the Gridded Livestock of the World analytical framework, sampling predictors in two different ways. A sensitivity analysis on Pearson's r correlation statistics and RMSE was carried out to understand how size and shapes of the response variables affect the goodness-of-fit and downscaling performances. We showed that scale, rather than shapes and sampling methods, affected downscaling precision, suggesting that training the model using the finest administrative level available is preferable. Moreover, datasets showing non-homogeneous distribution but instead spatial clustering seemed less affected by MAUP, yielding higher Pearson's r values and lower RMSE compared to a more spatially homogenous dataset. Implementing aggregation sensitivity analysis in spatial studies could help to interpret complex results and disseminate robust products.