Global knowledge gaps in species interaction networks data

Abstract

Ecological networks are increasingly studied at large spatial scales, expanding their focus from a conceptual tool for community ecology into one that also addresses questions in biogeography and macroecology. This effort is supported by increased access to standardized information on ecological networks, in the form of openly accessible databases. Yet, there has been no systematic evaluation of the fitness for purpose of these data to explore synthesis questions at very large spatial scales. In particular, because the sampling of ecological networks is a difficult task, they are likely to not have a good representation of the diversity of Earth's bioclimatic conditions, likely to be spatially aggregated, and therefore unlikely to achieve broad representativeness. In this paper, we analyse over 1300 ecological networks in the http://mangal.io database, and discuss their coverage of climates, and the geographic areas in which there is a deficit of data on ecological networks. Taken together, our results suggest that while some information about the global structure of ecological networks is available, it remains fragmented over space, with further differences by types of ecological interactions. This causes great concerns both for our ability to transfer knowledge from one region to the next, but also to forecast the structural change in networks under climate change.

Global and regional ecological boundaries drive abrupt changes in avian frugivory interactions

“…As with other studies [e.g., (10,31)], our data were not evenly spread across the globe, which likely affected the observed patterns. For instance, around 59% of our networks were located within a single tropical biome (fig.…”

A roadmap towards predicting species interaction networks (across space and time)

“…Moreover, ongoing developments in deep learning are aimed at improvement in low-data regimes and with unbalanced datasets [89,90]. Considering the current biases in network ecology [57] and the scarcity of data of species interactions, the prediction of ecological networks will undoubtedly benefit from these improvements. ML methods are emerging as the new standard in computational ecology in general [88,91], and in network ecology in particular [92], as long as sufficient, relevant data are available.…”

“…This knowledge gap has motivated a variety of approaches to deal with interactions in ecological research based on assumptions that do not always hold, such as the assumption that co-occurrence is equivalent to meaningful interaction strength [56]. Spatial biases in data coverage are prevalent at the global scale (with South America, Africa and Asia being under-represented) and different interaction types show biases towards different biomes [57]. These 'spatial gaps' serve as a limitation to our ability to confidently make predictions when accounting for real-world environmental conditions, especially in environments for which there are no analogous data.…”

Tropical forest dung beetle–mammal dung interaction networks remain similar across an environmental disturbance gradient

“…Moreover, unlike antagonistic or mutualistic interactions, studies on commensalist networks have tended to focus on single study sites or habitats, resulting in a limited understanding of how commensalist networks change across disturbance gradients. This is a critical knowledge gap, given the myriad of interactions in nature that are commensal (Poisot et al., 2021).…”