The authors, led by C. Musters of Leiden University, begin from an uncomfortable observation: nature conservation still works largely by trial and error. Measures are taken based on ecological reasoning and experience, then evaluated some years later, often without clear objectives or timeframes. Nobody, they note, could have predicted that reintroducing grey wolves to Yellowstone National Park would ultimately alter the course of rivers, an indirect effect that rippled through elk behaviour, vegetation and erosion. Accurate predictions of community change would allow policy-makers to compare alternative scenarios, such as enlarging reserves, cutting fertiliser emissions or reintroducing predators, before committing resources, and would give the public a transparent basis for management decisions.

Why has prediction proved so hard? Ecological communities at the scale relevant to conservation, roughly 10 to 1,000,000 square kilometres over 10 to 100 years, are staggeringly complex, with many biotic actors, abiotic conditions, feedback loops and a heavy dependence on local circumstances. Some populations even show chaotic dynamics, where vanishingly small differences in starting conditions lead to divergent long-term outcomes, eroding predictive accuracy over time. Decades of effort have produced well-studied process models for individual mechanisms, but assembling them into a General Ecological Model has only partly succeeded, largely because simplifications discard the overwhelming contingency of real communities.

The paper’s response is Organism-based Ecology, a framework that defines communities not as collections of species but as networks of individual organisms whose autonomous actions generate the patterns we observe. Crucially, those actions are guided by information. Organisms constantly read cues from their genes, metabolic state and past experiences, and from their surroundings through eyes, ears, chemical receptors and even electromagnetic sensitivity. Traditional ecology, with its focus on flows of energy and materials through food webs and carrying capacities, has largely ignored this parallel processing of information, yet it is what determines when and where organisms interact.

Building models that capture millions of information-processing individuals seems hopeless with conventional approaches, which is where deep learning enters. The authors propose training recurrent neural networks, algorithms designed for time-series data, on sequences describing the spatial distribution of organisms, materials, energy and biotic signals within a community. At each time step the model predicts the community’s state and corrects itself against observations, much as meteorological models iteratively refine forecasts of temperature and rainfall. Large language models, they suggest, could eventually knit together heterogeneous sub-models, from abiotic process modules to agent-based simulations, into a complete prediction workflow.

Deep learning brings its own well-documented hazards. Neural networks can wander into physically or ecologically impossible territory, predicting systems that violate conservation of mass or predators thriving on vanishing prey, so constraints from established process models must be built in. They are also black boxes: even when predictions are accurate, the hidden-layer architecture obscures how they were reached, undermining both stakeholder trust and the scientific cycle of hypothesis testing. The authors argue that explainable AI techniques and rigorous causal checks should turn deep learning into a hypothesis-generating tool, with each prediction tested and translated into testable theory that could feed better General Ecosystem Models and digital twins of nature.

There are ethical pitfalls too. Models extrapolated beyond their training data fail badly: a movement model of black-tailed godwits trained in western Europe, where the birds are beloved soil-probe predators, would misdescribe the same species in west African rice fields, where they are omnivorous and often seen as a farmer’s burden. Because opacity makes such misapplication hard to detect, the authors call for routine testing against data from other regions and periods, with the limits of applicability explicitly published. They also warn of unintentional colonialism, where models trained on data reflecting one culture’s use of nature impose conservation decisions misaligned with local needs, and urge the field to develop ethically responsible guidelines for applying AI in ecology.

The most radical proposal concerns data. Existing ecological datasets, they argue, are fundamentally insufficient: taxonomically, spatially and temporally biased toward large species, western Europe, the United States and fair-weather daylight monitoring, and rarely focused on individuals, which organism-based models require. More importantly, no one systematically collects the information organisms actually use to steer their behaviour. The authors propose a new generation of sensory instruments that simulate organisms’ sensory organs, recording light, imagery, sound, volatile organic compounds, nutrients, pH, pressure, humidity and electromagnetic fields simultaneously, with broad frequency ranges and minimal disturbance, deployed at fixed sites and read out remotely like instruments in space research.

Prototype pipelines are already imaginable with current technology. The authors sketch a thought experiment: one square kilometre of forest, a quarter cleared of vegetation, monitored monthly on a 100-square-metre grid for plant cover, tree growth, birdsong, camera-trap records of vertebrates and insects, microclimate and soil samples. After five years, a recurrent neural network could be trained to forecast the monthly distribution of individual trees and birds, then improved year by year as predictions extend further into the future and older data enrich the training sets. Camera trapping, biologging, environmental DNA and e-nose technology, including eco-acoustic monitoring of belowground life, could supply much of the raw material at far lower cost than traditional surveying.

The path forward is formidable, demanding interdisciplinary collaboration, substantial funding and careful choices about response variables and model architecture, and the authors caution that techniques designed to compensate for scarce training data may simply mask and amplify the biases in what ecologists have recorded. Yet if the deep learning and organism-based ecology pipeline can be built and rigorously tested, the payoff would be transformative: empirically grounded forecasts of how communities will change, delivered at the spatial and temporal scales that conservation policy-makers actually need. Ecology, after thirty years of being told it should predict, would finally have the tools to prove it can.

Subject of Research: Artificial intelligence and deep learning for predicting changes in ecological communities

Article Title: Challenges and opportunities for Artificial Intelligence for predicting changes in ecological communities

Article References: Musters, C. J. M., & de Snoo, G. R. (2026). Challenges and opportunities for Artificial Intelligence for predicting changes in ecological communities. Individual-based Ecology, 2, Article e199762. https://doi.org/10.3897/ibe.2.199762

Image Credits: AI Generated

DOI: 10.3897/ibe.2.199762

Keywords: deep learning, artificial intelligence, ecology, organism-based ecology, biodiversity, conservation, recurrent neural networks, predictive ecology, sensory ecology, ecological modelling, individual-based ecology, training data