Mechanistic models of seed dispersal by animals

Abstract

Modelling seed dispersal by animals seems straightforward; we need a way to keep track of the position on the animal through time and a clock for how long seeds travel with it. Mathematical models show how changing seed retention parameters can result in very different seed dispersal kernels, including fat-tailed ones. When movement is more realistic, in the sense that it is tied to the spatial distribution of resources, agent-based models result in both seed consumption and dispersal kernels varying according to the neighborhoods of focal plants. In general, agent-based models are built in ways that lead to overparameterization and poor predictive capacity. Considering several emergent properties that one wishes to capture and building a hierarchy of models varying in complexity should improve our understanding of the important mechanisms behind particular patterns. Progress in hierarchical data analysis tools allows fitting joint-species models in which species-level movement and foraging parameters are modelled as a function of species traits and their phylogenetic relationships. Overall, there has been great progress in data collection and modelling of seed dispersal by animals but we still need a better understanding of seed retention times, and of how bird physiology influences fruit choice. Further improvements in our ability to understand and predict seed dispersal by animals would probably also require considering individual personalities, as well as within and among species interactions. As our capacity to collect data bring us into the realm of big data and big models, important progress in mechanistic modelling of seed dispersal by animals should be achieved by close collaborations merging ecology, behavior, physiology, mathematics, computation and statistics.

The establishment of plants following long-distance dispersal

“…Movement ecology has contributed greatly, through both theoretical and empirical studies, to our understanding of LDD mechanisms [71][72][73], and emperical studies of within-range LDD can be used to test the accuracy of dispersal kernels as descriptors of the dispersal phase. One such kernel, WALD, incorporates the effects of key factors affecting LDD [74], e.g.…”

“…LDD through the ocean can be modeled using ocean current simulations [14,77], without enforcing a particular dispersal kernel. Such mechanistic approaches can also incorporate knowledge about animal foraging [73], yielding new insights into how landscape structure and resource predictability affect seed dispersal [78]. Furthermore, incorporating mechanistic links between the functional traits of organisms and their environments into species distribution models permits more robust predictions of range shifts in novel or non-equilibrium contexts, such as invasions, translocations, climate change, and evolutionary shifts [79].…”

Multiple intrinsic and extrinsic drivers influence the quantity and quality components of seed dispersal effectiveness in the rare shrub Lindera subcoriacea

“…Although our findings suggest L. subcoriacea seeds are being dispersed by at least several avian species, we do not know with certainty the absolute or relative dispersal effectiveness of the different species in terms of quantity and quality. Given the challenges of observing and tracking birds within the vegetation communities occupied by L. subcoriacea, it might be useful to instead explore SDE by modeling the fruit consumption, gut retention times, movements, and habitat occupancy of the various species to identify the relative numbers of seeds potentially dispersed and the suitability of dispersal locations for L. subcoriacea recruitment [e.g., [125][126][127]. The dispersal information generated by this sort of modeling effort would also be useful for informing models of L. subcoriacea regional population dynamics that could guide conservation actions such as small population augmentation and population introduction to enhance connectivity [128,129].…”

A global systematic review of frugivorous animal tracking studies and the estimation of seed dispersal distances

“…Seed dispersal is defined by 1) frugivore diet, 2) seed retention time and 3) movement behaviour Morales & Lopez, 2022). Frugivore diets can be described by targeted observations or faecal analysis.…”

“…Many seed shadow models use a single lognormal distribution to calculate dispersal kernels, which may not be sufficient to correctly identify spatially aggregated seed deposition patterns that are common for vertebrate seed dispersers (Russo et al, 2006). However, these models are improved by considering an animal's behavioural response to different environmental stimuli and their ability to handle potential biases within the movement data, such as autocorrelation (Morales & Lopez, 2022).…”