Parasite occurrence and infection estimates vary through time and space, making understanding the underlying drivers highly complex. Comparative studies based on empirical data must consider the factors of variation involved in estimating infection metrics in natural populations to make appropriate and reliable comparisons. Using a multi-scale approach, we explored the sources of variation in the estimation of infection prevalence, focusing on black spot disease in littoral freshwater fish communities sampled across 15 lakes in Québec, Canada. Our results show that infection prevalence is spatially heterogeneous across the landscape with evidence of infection hotspots and coldspots. Method-related sampling biases led to significant variations in prevalence estimates and spatial patterns of disease occurrence. Our results also indicated that low sampling efforts tend to overestimate the prevalence of infection in the landscape, and that the sampling effort required to estimate an accurate infection prevalence depends on the sampling method employed. Physico-chemical characteristics of the sites and local fish community structure were found to be the best drivers of infection at smaller spatial scales. Furthermore, our results suggest dilution effects due to obstruction and compatibility barriers limit the survival of the free-living cercaria parasite lifestage. Several relationships between infection prevalence and environmental drivers revealed non-linearity, suggesting complex interactions. Examining infection prevalence data at various spatial scales revealed method-induced biases, sampling effort effect and environment driven relationships underscoring the importance of context-dependencies and scale-dependencies in studies on host-parasite interactions.

Approximatively 25 chemical elements are essential for the maintenance, growth and reproduction of all living organisms. Hence, the movement, distribution, and relative proportions of those elements on the landscape should influence the structure and functioning of biological communities. Yet our basic understanding for the spatial distribution of elements across landscapes is limited. Here, we propose to apply tools from community and landscape ecology to study spatial patterns in elements. We illustrate this framework using tree leaves elemental composition and demonstrate how spatial grain and spatial dissimilarity of elements interact leading to predictable patterns in elemental distributions at various spatial scales. Meanwhile, further analysis revealed that potassium and calcium are the most important elemental contributors to spatial dissimilarity in leaf elements, raising new questions about their role in, or response to, distributions of biodiversity and ecosystem functions. Our framework provides a way to integrate abiotic and biotic processes, demonstrating how we can use community metrics to investigate variability of individual elements across landscapes. We conclude by hypothesizing that changes in the evenness or beta-diversity of elements should reflect the structure of biotic communities, providing a long-sought mechanistic link between community and ecosystem processes that can be measured directly in the field.

The integration of ecosystem processes over large spatial extents is critical to predicting whether and how global changes may impact biodiversity and ecosystem functions. Yet, there remains an important gap in meta-ecosystem models to predict multiple functions (e.g., carbon sequestration, elemental cycling, trophic efficiency) across ecosystem types (e.g., terrestrial-aquatic, benthic-pelagic). We derive a flexible meta-ecosystem model to predict ecosystem functions at landscape extents by integrating the spatial dimension of natural systems as spatial networks of different habitat types connected by cross-ecosystem flows of materials and organisms. We partition the physical connectedness of ecosystems from the spatial flow rates of materials and organisms, allowing the representation of all types of connectivity across ecosystem boundaries as well as the interaction(s) between them. Through simulating a forest-lake-stream meta-ecosystem, our model illustrated that even if spatial flows induced significant local losses of nutrients, differences in local ecosystem efficiencies could lead to increased secondary production at regional scale. This emergent result, which we dub the ‘cross-ecosystem efficiency hypothesis’, emphasizes the importance of integrating ecosystem diversity and complementarity in meta-ecosystem models to generate empirically testable hypotheses for ecosystem functions.