A practical guide to selecting models for exploration, inference, and prediction in ecology

Tredennick, Andrew T.; Hooker, Giles; Ellner, Stephen P.; Adler, Peter B.

doi:10.1002/ecy.3336

Cited by 274 publications

(212 citation statements)

References 70 publications

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“…We calculate credible intervals (CIs) for each species-specific term in the preliminary model and include in the final model only those terms whose intervals do not overlap 0. By using this approach, we can directly adjust how conservative we wish to be in including model parameters, balancing model prediction, the proportion of variance explained, and simplicity depending on modeling goals (Tredennick et al, 2021) (i.e. using a 50% CI will lead to models including more parameters than if we use a 95% CI).…”

Section: Incorporating Sparsity-inducing Priorsmentioning

confidence: 99%

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

Weiss‐Lehman

Werner

Bowler

et al. 2021

Preprint

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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.

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Section: Incorporating Sparsity-inducing Priorsmentioning

confidence: 99%

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

Weiss‐Lehman

Werner

Bowler

et al. 2021

Preprint

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“…Following Tredennick et al (2021), we separated our analysis into two sections: hypothesis testing, and exploration. First, we carried out strict hypothesis testing on previously proposed synergies between A. syriaca defenses (latex-by-trichome, latex-by-cardenolide) using regression models, including main effects of the two traits of interest as well as their interaction.…”

Section: Discussionmentioning

confidence: 99%

Plant defense synergies and antagonisms affect performance of specialist herbivores of common milkweed

Edwards

Ellner

Agrawal

2021

Preprint

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As a general rule, plants defend against herbivores with multiple traits. The defense synergy hypothesis posits that some traits are more potent when co-expressed with others, compared to their independent potency. However, this hypothesis has rarely been tested outside of synergies within a class of particular phytochemicals, and seldom under field conditions. We tested for synergies between multiple defense traits of common milkweed (Asclepias syriaca) by assaying the performance of two specialist herbivores on plants in natural populations. We employed both standard regression and exploratory analysis using a novel application of Random Forests that allowed us to detect synergies between defense traits. In hypothesis testing, we found the first empirical evidence for a previously hypothesized synergy between one pair of co-expressed defense traits (latex and cardenolides), but not another (latex and trichomes). When exploring all potential interactions between pairs of traits we found eight synergies and five antagonisms in predicting herbivore performance. Half of the identified synergies involved carbon, which is the basis of several defenses including chemical and physical barriers to feeding, and also essential nutrients. Our findings suggest that defense synergies could explain co-expression of latex and cardenolides in milkweeds. This synergy may be common among the diverse plant species that employ latex as a defense. Future studies should test carbon-based synergies, which our work suggests are prevalent, as well as the other synergies identified in our exploratory analysis. Our analytic approach provides a general, flexible framework for more broadly discovering and predicting the coexpression of traits through their synergistic function.

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“…It had been also demonstrated using likelihood-ratio tests approximations ( Aho, Derryberry & Peterson, 2017 , see also Supplemental Section S1 ). The lack of consistency of AIC when alternative models with uninformative parameters are considered has been highlighted recently ( Aho, Derryberry & Peterson, 2014 , 2017 ; Dennis et al, 2019 ; Leroux, 2019 ; Tredennick et al, 2021 ). This characteristic is due to AIC being created mostly with prediction in mind ( Dennis et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

Towards a pragmatic use of statistics in ecology

Castilho

Prado

2021

PeerJ

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Although null hypothesis testing (NHT) is the primary method for analyzing data in many natural sciences, it has been increasingly criticized. Recently, approaches based on information theory (IT) have become popular and were held by many to be superior because it enables researchers to properly assess the strength of the evidence that data provide for competing hypotheses. Many studies have compared IT and NHT in the context of model selection and stepwise regression, but a systematic comparison of the most basic uses of statistics by ecologists is still lacking. We used computer simulations to compare how both approaches perform in four basic test designs (t-test, ANOVA, correlation tests, and multiple linear regression). Performance was measured by the proportion of simulated samples for which each method provided the correct conclusion (power), the proportion of detected effects with a wrong sign (S-error), and the mean ratio of the estimated effect to the true effect (M-error). We also checked if the p-value from significance tests correlated to a measure of strength of evidence, the Akaike weight. In general both methods performed equally well. The concordance is explained by the monotonic relationship between p-values and evidence weights in simple designs, which agree with analytic results. Our results show that researchers can agree on the conclusions drawn from a data set even when they are using different statistical approaches. By focusing on the practical consequences of inferences, such a pragmatic view of statistics can promote insightful dialogue among researchers on how to find a common ground from different pieces of evidence. A less dogmatic view of statistical inference can also help to broaden the debate about the role of statistics in science to the entire path that leads from a research hypothesis to a statistical hypothesis.

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A practical guide to selecting models for exploration, inference, and prediction in ecology

Cited by 274 publications

References 70 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

Plant defense synergies and antagonisms affect performance of specialist herbivores of common milkweed

Towards a pragmatic use of statistics in ecology

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