Consumer-resource interactions are often influenced by other species in the community. At present these 'trophic interaction modifications' are rarely included in ecological models despite demonstrations that they can drive system dynamics. Here, we advocate and extend an approach that has the potential to unite and represent this key group of non-trophic interactions by emphasising the change to trophic interactions induced by modifying species. We highlight the opportunities this approach brings in comparison to frameworks that coerce trophic interaction modifications into pairwise relationships. To establish common frames of reference and explore the value of the approach, we set out a range of metrics for the 'strength' of an interaction modification which incorporate increasing levels of contextual information about the system. Through demonstrations in three-species model systems, we establish that these metrics capture complimentary aspects of interaction modifications. We show how the approach can be used in a range of empirical contexts; we identify as specific gaps in current understanding experiments with multiple levels of modifier species and the distributions of modifications in networks. The trophic interaction modification approach we propose can motivate and unite empirical and theoretical studies of system dynamics, providing a route to confront ecological complexity.

The analysis of interaction networks across spatial environmental gradients is a powerful approach to investigate the responses of communities to global change. Using a combination of DNA metabarcoding and traditional molecular methods we built bipartite Drosophila – parasitoid food webs from six Australian rainforest sites across gradients spanning 850 m in elevation and 5°C in mean temperature. Our cost‐effective hierarchical approach to network reconstruction separated the determination of host frequencies from the detection and quantification of interactions. The food webs comprised 5–9 host and 5–11 parasitoid species at each site, and showed a lower incidence of parasitism at high elevation. Despite considerable turnover in the relative abundance of host Drosophila species, and contrary to some previous results, we did not detect significant changes to fundamental metrics of network structure including nestedness and specialisation with elevation. Advances in community ecology depend on data from a combination of methodological approaches. It is therefore especially valuable to develop model study systems for sets of closely‐interacting species that are diverse enough to be representative, yet still amenable to field and laboratory experiments.

The top‐down and indirect effects of insects on plant communities depend on patterns of host use, which are often poorly documented, particularly in species‐rich tropical forests. At Barro Colorado Island, Panama, we compiled the first food web quantifying trophic interactions between the majority of co‐occurring woody plant species and their internally feeding insect seed predators. Our study is based on more than 200 000 fruits representing 478 plant species, associated with 369 insect species. Insect host‐specificity was remarkably high: only 20% of seed predator species were associated with more than one plant species, while each tree species experienced seed predation from a median of two insect species. Phylogeny, but not plant traits, explained patterns of seed predator attack. These data suggest that seed predators are unlikely to mediate indirect interactions such as apparent competition between plant species, but are consistent with their proposed contribution to maintaining plant diversity via the Janzen–Connell mechanism.

The analysis of interaction networks across spatial environmental gradients is a powerful approach to investigate the responses of communities to global change. Using a combination of DNA metabarcoding and traditional molecular methods we built bipartite Drosophila-parasitoid food webs from six Australian rainforest sites across gradients spanning 850 m in elevation and 5° Celsius in mean temperature. Our cost-effective hierarchical approach to network reconstruction separated the determination of host frequencies from the detection and quantification of interactions. The food webs comprised 5-9 host and 5-11 parasitoid species at each site, and showed a lower incidence of parasitism at high elevation. Despite considerable turnover in the relative abundance of host Drosophila species, and contrary to some previous results, fundamental metrics of network structure including nestedness and specialisation did not change significantly with elevation. Advances in community ecology depend on data from a combination of methodological approaches. It is therefore especially valuable to develop model study systems for sets of closely-interacting species that are diverse enough to be representative, yet still amenable to field and laboratory experiments.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

1. The accurate identification of species in images submitted by citizen scientists is currently a bottleneck for many data uses. Machine learning tools offer the potential to provide rapid, objective and scalable species identification for the benefit of many aspects of ecological science. Currently, most approaches only make use of image pixel data for classification. However, an experienced naturalist would also use a wide variety of contextual information such as the location and date of recording.2. Here, we examine the automated identification of ladybird (Coccinellidae) records from the British Isles submitted to the UK Ladybird Survey, a volunteer-led mass participation recording scheme. Each image is associated with metadata; a date, location and recorder ID, which can be cross-referenced with other data sources to determine local weather at the time of recording, habitat types and the experience of the observer. We built multi-input neural network models that synthesize metadata and images to identify records to species level.3. We show that machine learning models can effectively harness contextual information to improve the interpretation of images. Against an image-only baseline of 48.2%, we observe a 9.1 percentage-point improvement in top-1 accuracy with a multi-input model compared to only a 3.6% increase when using an ensemble of image and metadata models. This suggests that contextual data are being used to interpret an image, beyond just providing a prior expectation. We show that our neural network models appear to be utilizing similar pieces of evidence as human naturalists to make identifications. 4. Metadata is a key tool for human naturalists. We show it can also be harnessed by computer vision systems. Contextualization offers considerable extra information, particularly for challenging species, even within small and relatively homogeneous areas such as the British Isles. Although complex relationships between disparate sources of information can be profitably interpreted by simple neural network architectures, there is likely considerable room for further progress. Contextualizing images has the potential to lead to a step change in the accuracy of automated S U PP O RTI N G I N FO R M ATI O NAdditional supporting information may be found online in the Supporting Information section.

Turnover of species composition through time is frequently observed in ecosystems. It is often interpreted as indicating the impact of changes in the environment. Continuous turnover due solely to ecological dynamics—species interactions and dispersal—is also known to be theoretically possible; however the prevalence of such autonomous turnover in natural communities remains unclear. Here we demonstrate that observed patterns of compositional turnover and other important macroecological phenomena can be reproduced in large spatially explicit model ecosystems, without external forcing such as environmental change or the invasion of new species into the model. We find that autonomous turnover is triggered by the onset of ecological structural instability—the mechanism that also limits local biodiversity. These results imply that the potential role of autonomous turnover as a widespread and important natural process is underappreciated, challenging assumptions implicit in many observation and management tools. Quantifying the baseline level of compositional change would greatly improve ecological status assessments.

Documenting which species interact within ecological communities is challenging and labor intensive. As a result, many interactions remain unrecorded, potentially distorting our understanding of network structure and dynamics. We test the utility of four structural models and a new coverage-deficit model for predicting missing links in both simulated and empirical bipartite networks. We find they can perform well, although the predictive power of structural models varies with the underlying network structure. The accuracy of predictions can be improved by ensembling multiple models. Augmenting observed networks with mostlikely missing links improves estimates of qualitative network metrics. Tools to identify likely missing links can be simple to implement, allowing the prioritization of research effort and more robust assessment of network properties.