Aim Species distribution models (SDMs) are common tools in biogeography and conservation ecology. It has been repeatedly claimed that aggregated (stacked) SDMs (S‐SDMs) will overestimate species richness. One recently suggested solution to this problem is to use macroecological models of species richness to constrain S‐SDMs. Here, we examine current practice in the development of S‐SDMs to identify methodological problems, provide tools to overcome these issues, and quantify the performance of correctly stacked S‐SDMs alongside macroecological models. Locations Barents Sea, Europe and Dutch Wadden Sea. Methods We present formal mathematical arguments demonstrating how S‐SDMs should and should not be stacked. We then compare the performance of macroecological models and correctly stacked S‐SDMs on the same data to determine if the former can be used to constrain the latter. Next, we develop a maximum‐likelihood approach to adjusting S‐SDMs and discuss how it could potentially be used in combination with macroecological models. Finally, we use this tool to quantify how S‐SDMs deviate from observed richness in four very different case studies. Results We demonstrate that stacking methods based on thresholding site‐level occurrence probabilities will almost always be biased, and that these biases will tend toward systematic overprediction of richness. Next, we show that correctly stacked S‐SDMs perform very similarly to macroecological models in that they both have a tendency to overpredict richness in species‐poor sites and underpredict it in species‐rich sites. Main conclusions Our results suggest that the perception that S‐SDMs consistently overpredict richness is driven largely by incorrect stacking methods. With these biases removed, S‐SDMs perform similarly to macroecological models, suggesting that combining the two model classes will not offer much improvement. However, if situations where coupling S‐SDMs and macroecological models would be beneficial are subsequently identified, the tools we develop would facilitate such a synthesis.

Aim: Recent studies increasingly use statistical methods to infer biotic interactions from cooccurrence information at a large spatial scale. However, disentangling biotic interactions from other factors that can affect co-occurrence patterns at the macroscale is a major challenge. Approach:We present a set of questions that analysts and reviewers should ask to avoid erroneously attributing species association patterns to biotic interactions. Our questions relate to the appropriateness of data and models, the causality behind a correlative signal, and the problems associated with static data from dynamic systems. We summarize caveats reported by macroecological studies of biotic interactions and examine whether conclusions on the presence of biotic interactions are supported by the modelling approaches used.Findings: Irrespective of the method used, studies that set out to test for biotic interactions find statistical associations in species' co-occurrences. Yet, when compared with our list of questions, few purported interpretations of such associations as biotic interactions hold up to scrutiny. This does not dismiss the presence or importance of biotic interactions, but it highlights the risk of too lenient interpretation of the data. Combining model results with information from experiments and functional traits that are relevant for the biotic interaction of interest might strengthen conclusions.Main conclusions: Moving from species-to community-level models, including biotic interactions among species, is of great importance for process-based understanding and forecasting ecological responses. We hope that our questions will help to improve these models and facilitate the interpretation of their results. In essence, we conclude that ecologists have to recognize that a species association pattern in joint species distribution models will be driven not only by real biotic interactions, but also by shared habitat preferences, common migration history, phylogenetic history and shared response to missing environmental drivers, which specifically need to be discussed and, if possible, integrated into models. K E Y W O R D Sbiotic interactions, communities, co-occurrence, environment, residual structure, species distribution models

There is a widespread concern about the direct and indirect effects of industrial fisheries; this concern is particularly pertinent for so-called “marine protected areas” (MPAs), which should be safeguarded by national and international law. The intertidal flats of the Dutch Wadden Sea are a State Nature Monument and are protected under the Ramsar convention and the European Union's Habitat and Birds Directives. Until 2004, the Dutch government granted permission for ~75% of the intertidal flats to be exploited by mechanical dredgers for edible cockles (Cerastoderma edule). Here we show that dredged areas belonged to the limited area of intertidal flats that were of sufficient quality for red knots (Calidris canutus islandica), a long-distance migrant molluscivore specialist, to feed. Dredging led to relatively lower settlement rates of cockles and also reduced their quality (ratio of flesh to shell). From 1998 to 2002, red knots increased gizzard mass to compensate for a gradual loss in shellfish quality, but this compensation was not sufficient and led to decreases in local survival. Therefore, the gradual destruction of the necessary intertidal resources explains both the loss of red knots from the Dutch Wadden Sea and the decline of the European wintering population. This study shows that MPAs that do not provide adequate protection from fishing may fail in their conservation objectives.

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