Identifying “Useful” Fitness Models: Balancing the Benefits of Added Complexity with Realistic Data Requirements in Models of Individual Plant Fitness

Martyn, Trace E.; Stouffer, Daniel B.; Godoy, Óscar; Bartomeus, Ignasi; Pastore, Abigail I.; Mayfield, Margaret M.

doi:10.1086/713082

Cited by 25 publications

(38 citation statements)

References 107 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Heterospecifics may also be grouped more finely, for example, according to their taxonomic relationship (Uriarte et al, 2004) or their origin status and life form (e.g. native versus exotic and grasses versus forbs) (Martyn et al, 2020). Alternatively, functional groups can be created by grouping species according to their traits (e.g.…”

Section: Introductionmentioning

confidence: 99%

Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modeling approach

Weiss‐Lehman

Werner

Bowler

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Modeling species interactions in diverse communities traditionally requires a prohibitively large number of species-interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity-inducing priors on non-linear species abundance models to determine which species-interactions should be retained and which can be represented as an average heterospecific interaction term, reducing the number of model parameters. We evaluated model performance using simulated communities, computing out-of-sample predictive accuracy and parameter recovery across different input sample sizes. We applied our method to a diverse empirical community, allowing us to disentangle the direct role of environmental gradients on species' intrinsic growth rates from indirect effects via competitive interactions. We also identified a few neighboring species from the diverse community that had non-generic interactions with our focal species. This sparse modeling approach facilitates exploration of species-interactions in diverse communities while maintaining a manageable number of parameters.

show abstract

Section: Introductionmentioning

confidence: 99%

Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modeling approach

Weiss‐Lehman

Werner

Bowler

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…While studies have attempted to tackle this complexity by using statistical methods such as Akaike Information Criterion [46], the number of solutions of a polynomial system does not necessarily depend on the number of parameters but on the polynomial degree [45]. Hence, it is not just the lack of data that limits the use of complex models, as it can be perceived [47], it is their intractability, especially in high-dimensional systems [40].…”

Section: Synthetic Data: Unknown Factorsmentioning

confidence: 99%

Towards a system-level causative knowledge of pollinator communities

Saavedra

Bartomeus

Godoy

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Pollination plays a central role both in the maintenance of biodiversity and in crop production. However, habitat loss, pesticides, invasive species, and larger environmental fluctuations are contributing to a dramatic decline of numerous pollinators world-wide. This has increased the need for interventions to protect the composition, functioning, and dynamics of pollinator communities. Yet, how to make these interventions successful at the system level remains extremely challenging due to the complex nature of species interactions and the various unknown or unmeasured confounding ecological factors. Here, we propose that this knowledge can be derived by following a probabilistic causal analysis of pollinator communities. This analysis implies the inference of interventional expectations from the integration of observational and synthetic data. We propose that such synthetic data can be generated using theoretical models that can enable the tractability and scalability of unseen confounding ecological factors affecting the behavior of pollinator communities. We discuss a road map for how this probabilistic causal analysis can be accomplished to increase our system-level causative knowledge of natural communities.

show abstract

“…To minimise edge effects, trees in the 12 edge subplots were not considered as focal individuals (but were considered as neighbours). Neighbour species j included the six focal species, as well as all remaining non-focal species aggregated as a seventh neighbour group (Martyn et al 2021). Conspecific neighbour basal areas, N ipq , were calculated without the basal area of conspecific focal individual m. The pairwise interaction coefficients, α ij , quantify the per-basal-area main effects of species j on the growth of focal individual m (of species i).…”

Section: Statistical Modelmentioning

confidence: 99%

“…We opted to include or exclude entire sets of variables that share biological meaning, rather than constructing numerous candidate models with all possible variable combinations to avoid data dredging. However, this forces us to treat the importance of abiotic and biotic factors, as well as their interactions, as an 'all or none' question and assume that their effects are either equally important or equally unimportant (Lai et al 2021;Martyn et al 2021). Because there is no obvious biological basis for that assumption, we used Bayesian shrinkage simultaneously to achieve parsimony.…”

Section: Statistical Modelmentioning

confidence: 99%

“…Regardless of whether abiotic or bbiotic factors indirectly or directly modify how focal i and neighbour j interact, these non-additivities can be detected and tested as statistical interaction terms in regressions (Mayfield & Stouffer 2017;Martyn et al 2021). Many phenomenological models estimate the impact of biotic interactions as an interaction coefficient α ij multiplied by the density N j of neighbour species j (i.e., α ij N j ).…”

Section: Text Boxmentioning

confidence: 99%

See 1 more Smart Citation

Environmental gradients modify biotic interactions among tree species in temperate rain forest

Lai

Bellingham²,

McCarthy

et al. 2021

Preprint

View full text Add to dashboard Cite

The realised niche is jointly shaped by both abiotic and biotic processes. Moreover, the strength and direction of biotic interactions may vary across abiotic conditions and generate non-additivities that, if ignored, could lead to inaccurate predictions of species responses to changes in environment and composition. We tested this idea by analysing nationwide forest inventory data that span broad environmental gradients throughout New Zealand. The use of Bayesian shrinkage priors enabled our conclusion that the most complex model—featuring biotic interactions that changed with vapour pressure deficit and higher-order interactions with intermediary species—had the highest predictive accuracy of tree diameter growth. That is, pairwise competition became pairwise facilitation, or vice versa, depending on atmospheric moisture and/or the density of a third species. Our study highlights the importance of the interplay between abiotic and biotic processes when predicting how biotic interactions may structure communities under global change.

show abstract

Identifying “Useful” Fitness Models: Balancing the Benefits of Added Complexity with Realistic Data Requirements in Models of Individual Plant Fitness

Cited by 25 publications

References 107 publications

Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modeling approach

Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modeling approach

Towards a system-level causative knowledge of pollinator communities

Environmental gradients modify biotic interactions among tree species in temperate rain forest

Contact Info

Product

Resources

About