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Abstract Many management and conservation contexts can benefit from understanding relationships between species abundances, which can be used to improve predictions of species occurrence and abundance.We present conditional prediction as a tool to capture information about species abundances via residual covariance between species. From a fitted joint species distribution model, this framework produces a species coefficient matrix that contains relationships between species abundances. The species coefficients allow co‐observed species to be treated as a second set of predictors supplementing covariates in the model to improve prediction. We use simulations to demonstrate the potential benefits and limitations of conditional prediction across data types and species covariance before applying conditional prediction to two management contexts with real data.Simulations demonstrate that conditional prediction provides the largest benefits to continuous data and when there is residual covariance between many species.In our first application, we show that conditioning on other species improves in‐sample and out‐of‐sample predictions of fish and invertebrate species, including Atlantic cod. In our second application, we show that the species coefficient matrix can be used to identify bird species at risk of nest parasitism by Brown‐headed Cowbirds.Synthesis and applications. We present guidelines for using conditional prediction, which can help understand relationships between species abundances, improve predictions and inform conservation in a variety of contexts. 

Abstract AimUnderstanding the distribution of marine organisms is essential for effective management of highly mobile marine predators that face a variety of anthropogenic threats. Recent work has largely focused on modelling the distribution and abundance of marine mammals in relation to a suite of environmental variables. However, biotic interactions can largely drive distributions of these predators. We aim to identify how biotic and abiotic variables influence the distribution and abundance of a particular marine predator, the bottlenose dolphin (Tursiops truncatus), using multiple modelling approaches and conducting an extensive literature review. LocationWestern North Atlantic continental shelf. MethodsWe combined widespread marine mammal and fish and invertebrate surveys in an ensemble modelling approach to assess the relative importance and capacity of the environment and other marine species to predict the distribution of both coastal and offshore bottlenose dolphin ecotypes. We corroborate the modelled results with a systematic literature review on the prey of dolphins throughout the region to help explain patterns driven by prey availability, as well as reveal new ones that may not necessarily be a predator–prey relationship. ResultsWe find that coastal bottlenose dolphin distributions are associated with one family of fishes, the Sciaenidae, or drum family, and predictions slightly improve when using only fish versus only environmental variables. The literature review suggests that this tight coupling is likely a predator–prey relationship. Comparatively, offshore dolphin distributions are more strongly related to environmental variables, and predictions are better for environmental‐only models. As revealed by the literature review, this may be due to a mismatch between the animals caught in the fish and invertebrate surveys and the predominant prey of offshore dolphins, notably squid. Main ConclusionsIncorporating prey species into distribution models, especially for coastal bottlenose dolphins, can help inform ecological relationships and predict marine predator distributions.