Abandonment of cultural landscapes: butterfly communities track the advance of forest over grasslands

Rural landscapes in Europe have suffered considerable land-use change in the last 50 years, with agricultural intensification in western regions and land abandonment in eastern and southern regions. The negative impacts of agricultural intensification on butterflies and other insects in western Europe have been well studied. However, less is known about the impacts of abandonment on mountain and humid areas of eastern and southern Europe, where landscapes have remained more natural. We sampled butterfly communities in the Picos de Europa National Park (Spain), a region which is undergoing a process of rural abandonment. 19 hay meadows with different periods of abandonment were studied (long-term 18 years or mid-term abandoned, 3–7 years) and compared to meadows continuously managed in a traditional way. We examined how local meadow characteristics and landscape variables affected butterfly community response to abandonment. Butterfly communities were affected by abandonment, with an overall increase in the density of individuals in the long term. Community composition appears to undergo major change over time, with a species turnover of around 50% in the first few years of abandonment, rising to around 70% after 18 years of abandonment. There was a tendency for species with higher preference for closed habitats to increase their densities as time since abandonment proceeded. Landscape variables had a major impact on butterfly communities, stronger than the effect of meadow management. Community preference for closed habitats was associated with higher forest cover in the surroundings of the meadows, but heterogeneous landscapes (in their composition or configuration) mitigated this effect. Implications for insect conservation Our findings suggest that we should ensure that communities have time to react to the diverse stressors imposed by global change. Facilitating survival to all kinds of functional and taxonomic groups implies promoting landscape heterogeneity and connectivity.

Trait-environment relationship in tadpoles of the southern Atlantic Forest

Environmental predictors select individuals by their functional traits, shaping the anuran assembly patterns. Individuals respond to environmental filters that can be on a local or regional scale.In this study, we investigated the association between local (water and microhabitat) and landscape variables and the morphological traits of tadpoles of ponds and streams. The study was conducted in the southern region of the Brazilian Atlantic Forest. We sampled 28 waterbodies and recorded 22 anurans species. We performed RLQ and fourth-corner analyses to determine the patterns of trait-environment relationships and determine which environmental and landscape variables influence the morphological characteristics of tadpoles from streams and ponds. We found that the morphological traits of tadpoles are influenced mainly by physicochemical and microhabitat attributes, being distinct between ponds and streams. In ponds, water depth, pH, and the presence of vegetation influence the morphological traits of the tadpoles, while in the streams water pH, temperature, conductivity, total alkalinity, Alk HCO3, and microhabitat variables played a major role in defining the traits. Our results indicate that local components of habitat (water characteristics and microhabitat) influence functional traits of tadpoles in both ponds and streams, especially those supposedly related to locomotory, foraging and prey-detection abilities.

Data-driven and interpretable machine-learning modeling to explore the fine-scale environmental determinants of malaria vectors biting rates in rural Burkina Faso

BackgroundImproving the knowledge and understanding of the environmental determinants of malaria vectors abundances at fine spatiotemporal scales is essential to design locally tailored vector control intervention. This work aimed at exploring the environmental tenets of human-biting activity in the main malaria vectors (Anopheles gambiae s.s., Anopheles coluzzii and Anopheles funestus) in the health district of Diébougou, rural Burkina Faso.MethodsAnopheles human-biting activity was monitored in 27 villages during 15 months (in 2017-2018), and environmental variables (meteorological and landscape) were extracted from high resolution satellite imagery. A two-step data-driven modeling study was then carried-out. Correlation coefficients between the biting rates of each vector species and the environmental variables taken at various temporal lags and spatial distances from the biting events were first calculated. Then, multivariate machine-learning models were generated and interpreted to i) pinpoint primary and secondary environmental drivers of variation in the biting rates of each species and ii) identify complex associations between the environmental conditions and the biting rates.ResultsMeteorological and landscape variables were often significantly correlated with the vectors’ biting rates. Many nonlinear associations and thresholds were unveiled by the multivariate models, both for meteorological and landscape variables. From these results, several aspects of the bio-ecology of the main malaria vectors were precised or hypothesized for the Diébougou area, including breeding sites typologies, development and survival rates in relation to weather, flight ranges from breeding sites, dispersal related to landscape openness.ConclusionsUsing high resolution data in an interpretable machine-learning modeling framework proved to be an efficient way to enhance the knowledge of the complex links between the environment and the malaria vectors at a local scale. More broadly, the emerging field of interpretable machine-learning has significant potential to help improving our understanding of the complex processes leading to malaria transmission.

The scale effect of landscape variables on landscape experiences: A multi-scale spatial analysis of social media data in an urban nature park context

Context The roles of landscape variables with regard to the recreational services provided by nature parks have been widely studied. However, the potential scale effects of the relationships of landscape features and attributes to categorized nature experiences have not been adequately studied from an experimental perspective. Objectives This article demonstrates multiscale geographically weighted regression (MGWR) as a new method to quantify the relationship between experiences and landscape variables and aims to answer the following questions: 1) Which dimensions of landscape experiences can be interpreted from geocoded social media data, and what landscape variables are associated with specific dimensions of experience? 2) At what spatial scale and relative magnitude can landscape variables mediate landscape experiences? Methods Social media data (Flickr photos) from Amager Nature Park were categorized into different dimensions of landscape experience. Estimated parameter surfaces resulting from the MGWR were generated to show the patterns of the relationship between the landscape variables and the categorized experiences. Results All considered landscape variables were identified as relating to certain landscape experiences (nature, animals, scenery, engagement, and culture). Scale effects were observed in all relationships. This highlights the realities of context- and place-specific relationships and the limited applicability of simple approaches that assume relationships to be spatially stationary. Conclusions The spatial effect of landscape variables on landscape experiences was clarified and demonstrated to be important for understanding the spatial patterns of landscape experiences. The demonstrated modelling method may be used to further the study of the value of natural landscapes to human wellbeing.

Host relatedness and landscape connectivity shape pathogen spread in the puma, a large secretive carnivore

Urban expansion can fundamentally alter wildlife movement and gene flow, but how urbanization alters pathogen spread is poorly understood. Here, we combine high resolution host and viral genomic data with landscape variables to examine the context of viral spread in puma (Puma concolor) from two contrasting regions: one bounded by the wildland urban interface (WUI) and one unbounded with minimal anthropogenic development (UB). We found landscape variables and host gene flow explained significant amounts of variation of feline immunodeficiency virus (FIV) spread in the WUI, but not in the unbounded region. The most important predictors of viral spread also differed; host spatial proximity, host relatedness, and mountain ranges played a role in FIV spread in the WUI, whereas roads might have facilitated viral spread in the unbounded region. Our research demonstrates how anthropogenic landscapes can alter pathogen spread, providing a more nuanced understanding of host-pathogen relationships to inform disease ecology in free-ranging species.