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Author(s): Baiser, B; Gravel, D; Cirtwill, AR; Dunne, JA; Fahimipour, AK; Gilarranz, LJ; Grochow, JA; Li, D; Martinez, ND; McGrew, A; Poisot, T; Romanuk, TN; Stouffer, DB; Trotta, LB; Valdovinos, FS; Williams, RJ; Wood, SA; Yeakel, JD | Abstract: Aim: How do factors such as space, time, climate and other ecological drivers influence food web structure and dynamics? Collections of well-studied food webs and replicate food webs from the same system that span biogeographical and ecological gradients now enable detailed, quantitative investigation of such questions and help integrate food web ecology and macroecology. Here, we integrate macroecology and food web ecology by focusing on how ecogeographical rules [the latitudinal diversity gradient (LDG), Bergmann's rule, the island rule and Rapoport's rule] are associated with the architecture of food webs. Location: Global. Time period: Current. Major taxa studied: All taxa. Methods: We discuss the implications of each ecogeographical rule for food webs, present predictions for how food web structure will vary with each rule, assess empirical support where available, and discuss how food webs may influence ecogeographical rules. Finally, we recommend systems and approaches for further advancing this research agenda. Results: We derived testable predictions for some ecogeographical rules (e.g. LDG, Rapoport's rule), while for others (e.g., Bergmann's and island rules) it is less clear how we would expect food webs to change over macroecological scales. Based on the LDG, we found weak support for both positive and negative relationships between food chain length and latitude and for increased generality and linkage density at higher latitudes. Based on Rapoport's rule, we found support for the prediction that species turnover in food webs is inversely related to latitude. Main conclusions: The macroecology of food webs goes beyond traditional approaches to biodiversity at macroecological scales by focusing on trophic interactions among species. The collection of food web data for different types of ecosystems across biogeographical gradients is key to advance this research agenda. Further, considering food web interactions as a selection pressure that drives or disrupts ecogeographical rules has the potential to address both mechanisms of and deviations from these macroecological relationships. For these reasons, further integration of macroecology and food webs will help ecologists better understand the assembly, maintenance and change of ecosystems across space and time.

Aim Although there is a vast body of literature on the causes of variation in species composition in ecological communities, less effort has been invested in understanding how interactions between these species vary. Given that interactions are crucial to the structure and functioning of ecological communities, we need to develop a better understanding of their spatial distribution. Here, we investigate whether species interactions vary more in response to different climate variables than do individual species. Location Eurasia. Time period 2000s. Major taxa Animalia. Methods We used a measure of local contribution to β-diversity to evaluate the compositional uniqueness of 51 host–parasite communities of rodents and their ectoparasitic fleas across Eurasia, using publicly available data. We measured uniqueness based on the species composition and based on potential and realized biotic interactions (here, host–parasite interactions). Results We show that species interactions vary more, across space, than do species. In particular, we show that species interactions respond to some climatic variables that have no effect on species distributions or dissimilarity. Main conclusions Species interactions capture some degree of variation that is not apparent when looking at species occurrences only. In this system, this appeared as hosts and parasites interacting in different ways as a response to different environmental factors, especially the temperature and dryness. We discuss the implications of this finding for the amount of information that should be considered when measuring community dissimilarity.

Aim Current interest in forecasting changes to species ranges has resulted in a multitude of approaches to species distribution models (SDMs). However, most approaches include only a small subset of the available information, and many ignore smaller-scale processes such as growth, fecundity and dispersal. Furthermore, different approaches often produce divergent predictions with no simple method to reconcile them. Here, we present a flexible framework for integrating models at multiple scales using hierarchical Bayesian methods. Location Eastern North America (as an example). Methods Our framework builds a metamodel that is constrained by the results of multiple sub-models and provides probabilistic estimates of species presence. We applied our approach to a simulated dataset to demonstrate the integration of a correlative SDM with a theoretical model. In a second example, we built an integrated model combining the results of a physiological model with presence–absence data for sugar maple (Acer saccharum), an abundant tree native to eastern North America. Results For both examples, the integrated models successfully included information from all data sources and substantially improved the characterization of uncertainty. For the second example, the integrated model outperformed the source models with respect to uncertainty when modelling the present range of the species. When projecting into the future, the model provided a consensus view of two models that differed substantially in their predictions. Uncertainty was reduced where the models agreed and was greater where they diverged, providing a more realistic view of the state of knowledge than either source model. Main conclusions We conclude by discussing the potential applications of our method and its accessibility to applied ecologists. In ideal cases, our framework can be easily implemented using off-the-shelf software. The framework has wide potential for use in species distribution modelling and can drive better integration of multi-source and multi-scale data into ecological decision-making.

Aim To define biome-scale hotspots of phylogenetic and functional mammalian biodiversity (PD and FD, respectively) and compare them to ‘classical’ hotspots based on species richness (SR) only.