Letter

Transferability of Mechanistic Ecological Models Is About Emergence

Assessing and predicting the persistence of populations is essential for the conservation and control of species. Here, we argue that local mechanisms require a better conceptual synthesis to facilitate a more holistic consideration along with regional mechanisms known from metapopulation theory. We summarise the evidence for local buffer mechanisms along with their capacities and emphasise the need to include multiple buffer mechanisms in studies of population persistence. We propose an accessible framework for local buffer mechanisms that distinguishes between damping (reducing fluctuations in population size) and repelling (reducing population declines) mechanisms. We highlight opportunities for empirical and modelling studies to investigate the interactions and capacities of buffer mechanisms to facilitate better ecological understanding in times of ecological upheaval.

Numerical models of ecological systems are increasingly used to address complex environmental and resource management questions. One challenge for scientists, managers, and stakeholders is to appraise how well suited these models are to answer questions of scientific or societal relevance, that is, to perform, communicate, or access transparent evaluations of ecological models. While there have been substantial developments to support standardised descriptions of ecological models, less has been done to standardise and to report model evaluation practices. We present here a general protocol designed to guide the reporting of model evaluation. The protocol is organised in three major parts: the objective(s) of the modelling application, the ecological patterns of relevance and the evaluation methodology proper, and is termed the OPE (objectives, patterns, evaluation) protocol. We present the 25 questions of the OPE protocol which address the many aspects of the evaluation process and then apply them to six case studies based on a diversity of ecological models. In addition to standardising and increasing the transparency of the model evaluation process, we find that going through the OPE protocol helps modellers to think more deeply about the evaluation of their models. From this last point, we suggest that it would be highly beneficial for modellers to consider the OPE early in the modelling process, in addition to using it as a reporting tool and as a reviewing tool.

Animal movement, spanning all time and space scales in nature, is constrained by the individual's available energy to spend, creating a strong link between physiology and observed movement and distribution patterns. To progress, movement ecology needs an explicit focus on common mechanisms, such as energetics, linking behaviour to fitness consequences across scales, but simplified by process-based approaches, such as individual-based models (IBMs). We review the animal movement literature, from fine-scale patch foraging to large-scale geographic migration, focussing on IBMs incorporating individual energetics (hereafter termed eIBMs).

The literature shows IBMs in movement ecology are mainly defined by the following four categories nested across different space and time scales: (1) fine-scale displacement, i.e. foraging and local habitat selection by animals under different resource availabilities, including (2) cognitive processes, such as risk perception, memory and learning, (3) home range occupancy and dispersal potential, and (4) migration and biogeographic distribution. Amongst eIBMs, a common issue emerges: individual traits like energetics are sometimes species- and problem-specific, leading to divergence in the way energetics are incorporated and aggregated in the models. Further, movement becomes more difficult to predict with increasing time and space scales and behaviour and biological complexity.

Using this individual-level approach, we show 1) it is most effective in explaining fine-scale movement (foraging and competition) where the links between the animal and their habitat are immediate and absolute compared to coarser time and space scales, 2) coarser time and space scales present further challenges for the animal that require more careful interpretation of movement drivers, and 3) common, but more complex behaviours across scales, such as risk perception and memory, are better explained with richer ecological data that are well-integrated into movement models. We propose formulating individual energetics in a modelling framework as a next-gen extension to address the challenges of modelling movement across different scales, species, and constraints.

Rift Valley fever (RVF) is a zoonotic arbovirosis which has been reported across Africa including the northernmost edge, South West Indian Ocean islands, and the Arabian Peninsula. The virus is responsible for high abortion rates and mortality in young ruminants, with economic impacts in affected countries. To date, RVF epidemiological mechanisms are not fully understood, due to the multiplicity of implicated vertebrate hosts, vectors, and ecosystems. In this context, mathematical models are useful tools to develop our understanding of complex systems, and mechanistic models are particularly suited to data-scarce settings. Here, we performed a systematic review of mechanistic models studying RVF, to explore their diversity and their contribution to the understanding of this disease epidemiology. Researching Pubmed and Scopus databases (October 2021), we eventually selected 48 papers, presenting overall 49 different models with numerical application to RVF. We categorized models as theoretical, applied, or grey, depending on whether they represented a specific geographical context or not, and whether they relied on an extensive use of data. We discussed their contributions to the understanding of RVF epidemiology, and highlighted that theoretical and applied models are used differently yet meet common objectives. Through the examination of model features, we identified research questions left unexplored across scales, such as the role of animal mobility, as well as the relative contributions of host and vector species to transmission. Importantly, we noted a substantial lack of justification when choosing a functional form for the force of infection. Overall, we showed a great diversity in RVF models, leading to important progress in our comprehension of epidemiological mechanisms. To go further, data gaps must be filled, and modelers need to improve their code accessibility.