Syntheses and Reviews

Complex statistical methods are continuously developed across the fields of ecology, evolution, and systematics (EES). These fields, however, lack standardized principles for evaluating methods, which has led to high variability in the rigor with which methods are tested, a lack of clarity regarding their limitations, and the potential for misapplication. In this review, we illustrate the common pitfalls of method evaluations in EES, the advantages of testing methods with simulated data, and best practices for method evaluations.

A formidable challenge for global change biologists is to predict how natural populations will respond to the emergence of conditions not observed at present, termed novel climates. Popular approaches to predict population vulnerability are based on the expected degree of novelty relative to the amplitude of historical climate fluctuations experienced by a population. Here, we argue that predictions focused on amplitude may be inaccurate because they ignore the predictability of environmental fluctuations in driving patterns of evolution and responses to climate change. To address this disconnect, we review major findings of evolutionary theory demonstrating the conditions under which phenotypic plasticity is likely to evolve in natural populations, and how plasticity decreases population vulnerability to novel environments. We outline key criteria that experimental studies should aim for to effectively test theoretical predictions, while controlling for the degree of climate novelty.

Genetic redundancy has been defined in many different ways at different levels of biological organization. Here, we briefly review the general concept of redundancy and focus on the evolutionary importance of redundancy in terms of the number of genotypes that give rise to the same phenotype. We discuss the challenges in determining redundancy empirically, with published experimental examples, and demonstrate the use of new metrics to quantify redundancy in evolution studies.

Complex life cycles, in which discrete life stages of the same organism differ in form or function and often occupy different ecological niches, are common in nature. Because stages share the same genome, selective effects on one stage may have cascading consequences through the entire life cycle. We review how theoretical and empirical studies have not yet generated clear predictions about how life cycle complexity will influence patterns of adaptation in response to rapidly changing environments, or tested theoretical predictions for fitness trade-offs (or lack thereof) across life stages. 

The literature is awash with “re”-words: reproducibility, repeatability, replicability—even “preproducibility” has found its way into this web of “re”. In this essay, we argue that just because a statistical analysis is reproducible or repeatable or re-whatever does not mean that it is valid. A valid statistical outcome means that the analysis has ended in a true positive or true negative result.

Restriction site-associated DNA sequencing (RADseq), a popular reduced representation method, has ushered in a new era of genome-scale research for assessing population structure, hybridization, demographic history, phylogeography and migration. RADseq has also been widely used to conduct genome scans to detect loci involved in adaptive divergence among natural populations. Here, we examine the capacity of those RADseq-based genome scan studies to detect loci involved in local adaptation

Uncovering the genetic and evolutionary basis of local adaptation is a major focus of evolutionary biology. The recent development of cost-effective methods for obtaining high-quality genome-scale data makes it possible to identify some of the loci responsible for adaptive differences among populations. Two basic approaches for identifying putatively locally adaptive loci have been developed and are broadly used: one that identifies loci with unusually high genetic differentiation among populations (differentiation outlier methods) and one that searches for correlations between local population allele frequencies and local environments (genetic-environment association methods). Here, we review the promises and challenges of these genome scan methods, including correcting for the confounding influence of a species’ demographic history, biases caused by missing aspects of the genome, matching scales of environmental data with population structure, and other statistical considerations.

In multi-species communities, direct interactions among species create the potential for substantial indirect effects. The indirect effect of one species on another emerges from the densities of the interacting species along the path linking those species and the effect of species' traits on the magnitude of the per capita direct effects exerted by each species along that path. The dramatic changes in the size structure of many species that have been subjected to size-selective harvesting raises the question of whether this exchange of phenotypes—smaller fish for larger fish—will have substantial consequences for the ecosystem through changing the net indirect effects of the harvested species.