Bray-Curtis (AFD) differentiation in molecular ecology: Forecasting, an adjustment (^AA), and comparative performance in selection detection

Geographic genetic differentiation measures are used for purposes such as assessing genetic diversity and connectivity, and searching for signals of selection. Confirmation by unrelated measures can minimize false positives. A popular differentiation measure, Bray-Curtis, has been used increasingly in molecular ecology, renamed AFD (hereafter called BCAFD). Critically, BCAFD is expected to be partially independent of the commonly used Hill “Q-profile” measures. BCAFD needs scrutiny for potential biases, by examining limits on its value, and comparing simulations against expectations. BCAFD has two dependencies on within-population (alpha) variation, undesirable for a between-population (beta) measure. The first dependency is derived from similarity to (Formula presented.) and (Formula presented.). The second dependency is that BCAFD cannot be larger than the highest allele proportion in either location (alpha variation), which can be overcome by data-filtering or by a modified statistic AA or “Adjusted AFD”. The first dependency does not forestall applications such as assessing connectivity or selection, if we know the measure's null behavior under selective neutrality with specified conditions—which is shown in this article for AA, for equilibrium, and nonequilibrium, for the commonly used data type of single-nucleotide-polymorphisms (SNPs) in two locations. Thus, AA can be used in tandem with mathematically contrasting differentiation measures, with the aim of reducing false inferences. For detecting adaptive loci, the relative performance of AA and other measures was evaluated, showing that it is best to use two mathematically different measures simultaneously, and that AA is in one of the best such pairwise criteria. For any application, using AA, rather than BCAFD, avoids the counterintuitive limitation by maximum allele proportion within localities

The introduction of entropy and information methods to ecology by ramon margalef

In ecology and evolution, entropic methods are now used widely and increasingly frequently. Their use can be traced back to Ramon Margalef’s first attempt 70 years ago to use log-series to quantify ecological diversity, including searching for ecologically meaningful groupings within a large assemblage, which we now call the gamma level. The same year, Shannon and Weaver published a generally accessible form of Shannon’s work on information theory, including the measure that we now call Shannon–Wiener entropy. Margalef seized on that measure and soon proposed that ecologists should use the Shannon–Weiner index to evaluate diversity, including assessing local (alpha) diversity and differentiation between localities (beta). He also discussed relating this measure to environmental variables and ecosystem processes such as succession. Over the subsequent decades, he enthusiastically expanded upon his initial suggestions. Finally, 2019 also would have been Margalef’s 100th birthday

Applicability and limitations of sensitivity analyses for wildlife management

Sensitivity analyses that assess the impact of changing vital rates on population growth have been widely used to guide conservation. If implemented with caution, they can provide guidance as to which management actions will optimize conservation outcomes. In this review, we first focus on the commonly used proportional sensitivity and elasticity analyses that change each vital rate by equal proportions, to assess their importance for wildlife management. These types of analyses also feature potential pitfalls and limitations, including (1) Each vital rate is usually on a different scale. Without appropriate scaling this can result in a flawed evaluation of the importance of vital rates. (2) Vital rates rarely change at equal proportions in nature. This can bring about misguided management recommendations on the basis of vital rate changes that are unrealistic. (3) Proportional sensitivity analyses often do not reflect the feasibility and effectiveness of altering particular demographic parameters. Consequently, relying solely on proportional sensitivities or elasticities can lead to flawed evaluation of the importance of vital rates and thus prioritization of management options that are unrealistic or ineffective. We outline alternative approaches, which involve assessing the impact of threats, the relative demography of stable and declining populations, the effect of observable variation of vital rates on population viability, and the potential effects of feasible management scenarios. Synthesis and applications. Sensitivity analyses are useful tools to guide wildlife management. If implemented and interpreted with care, sensitivity analyses can identify key demographic parameters and threats to population viability. However, their usefulness is limited, when applied without careful evaluation as to whether the perturbations evaluated are realistic, feasible and meet the need of wildlife managers. We caution against the over-reliance on proportional sensitivity and elasticity analyses and point to alternative approaches, including life-stage simulation analysis, vital rate sensitivity analysis or manual perturbations

Why does the complexity of functionally equivalent signals vary across closely related species?

Animal signals are observed to vary widely in complexity among species, but why this should be the case - especially among closely related taxa - is unclear. Identifying the selective forces that drive these differences is important for understanding signal evolution, as well as the origins of communication more generally. We used a measure derived from information theory to quantify the complexity of visual territorial advertisement displays performed by males of closely related taxa of Puerto Rican Anolis lizard. In general, the information potential of visual displays appeared to be high compared with signals of other taxonomic groups (e.g., other lizards, birds). Nevertheless, there was still considerable variation in signal complexity among the Anolis taxa studied. We found a strong relationship between signal complexity and phylogeny for some aspects of the advertisement display traditionally thought to be important in species recognition. Other aspects of the display tended to vary independently of phylogeny, with differences in signal complexity among taxa reflecting the distance over which displays were typically viewed by territorial neighbors, and to some extent the number of sympatric congeners present in the environment. More generally, we highlight a little used, but tractable means of quantifying complexity in different species - and in different aspects of the same signal (the number, timing, and type of components) - that can reveal the evolutionary processes generating increases (or decreases) in communicative complexity

Predicting Shannon’s information for genes in finite populations: new uses for old equations

This study provides predictive equations for Shannon’s information in a finite population, which are intuitive and simple enough to see wide scale use in molecular ecology and population genetics. A comprehensive profile of genetic diversity contains three complementary components: numbers of allelic types, Shannon’s information and heterozygosity. Currently heterozygosity has greater resources than Shannon’s information, such as more predictive models and integration into more mainstream genetics software. However, Shannon’s information has several advantages over heterozygosity as a measure of genetic diversity, so it is important to develop Shannon’s information as a new tool for molecular ecology. Past efforts at making forecasts for Shannon’s information in specific molecular ecology scenarios mostly dealt with expectations for Shannon’s information at genetic equilibrium, but dynamic forecasts are also vital. In particular, we must be able to predict loss of genetic diversity when dealing with finite populations, because they risk losing genetic variability, which can have an adverse effect on their survival. We present equations for predicting loss of genetic diversity measured by Shannon’s information. We also provide statistical justification for these models by assessing their fit to data derived from simulations and managed, replicated laboratory populations. The predictive models will enhance the usefulness of Shannon’s information as a measure of genetic diversity; they will also be useful in pest control and conservation

Erratum: Information theory broadens the spectrum of molecular ecology and evolution: (Trends in Ecology and Evolution 32:12, p:948–963, 2017) (Trends in Ecology & Evolution (2017) 32(12) (948–963), (S0169534717302550), (10.1016/j.tree.2017.09.012))

In Sherwin et al. [1], several corrections are required, having been noticed when assisting other researchers to use the methods. In the main text, in Figure III in Box 2, nine subscripts were incorrect (reversing localities 1 and 2). The correct figure is shown below. [Figure presented] On pages 5 and 6 of the supplement, it should be explained that [Formula presented], that is, the averaging happens before conversion to the D scale (see Equations 10 and 11 of Jost [2]). Similarly, on page 8 of the supplement, [Formula presented]. On Page 6 of the supplement, the equation from Dewar et al. [3] is incorrectly transcribed; the correct equation is: [Formula presented] The authors and publisher apologise for any confusion

Rapid evolution of leaf physiology in an introduced beach daisy

Photosynthesis is a key biological process. However, we know little about whether plants change their photosynthetic strategy when introduced to a new range. We located the most likely source population for the South African beach daisy Arctotheca populifolia introduced to Australia in the 1930s, and ran a common-garden experiment measuring 10 physiological and morphological leaf traits associated with photosynthesis. Based on predictions from theory, and higher rainfall in the introduced range, we hypothesized that introduced plants would have a (i) higher photosynthetic rate, (ii) lower water-use efficiency (WUE) and (iii) higher nitrogen-use efficiency. However, we found that introduced A. populifolia had a lower photosynthetic rate, higher WUE and lower nitrogen-use efficiency than did plants from Arniston, South Africa. Subsequent site visits suggested that plants in Arniston may be able to access moisture on a rocky shelf, while introduced plants grow on sandy beaches where water can quickly dissipate. Our unexpected findings highlight that: (1) it is important to compare introduced species to their source population for an accurate assessment of evolutionary change; (2) rainfall is not always a suitable proxy for water availability and (3) introduced species often undergo evolutionary changes, but without detailed ecological information we may not be able to accurately predict the direction of these changes

Detecting steps in spatial genetic data: Which diversity measures are best?

Accurately detecting sudden changes, or steps, in genetic diversity across landscapes is important for locating barriers to gene flow, identifying selectively important loci, and defining management units. However, there are many metrics that researchers could use to detect steps and little information on which might be the most robust. Our study aimed to determine the best measure/s for genetic step detection along linear gradients using biallelic single nucleotide polymorphism (SNP) data. We tested the ability to differentiate between linear and step-like gradients in genetic diversity, using a range of diversity measures derived from the q-profile, including allelic richness, Shannon Information, GST, and Jost-D, as well as Bray-Curtis dissimilarity. To determine the properties of each measure, we repeated simulations of different intensities of step and allele proportion ranges, with varying genome sample size, number of loci, and number of localities. We found that alpha diversity (withinlocality) based measures were ineffective at detecting steps. Further, allelic richness-based beta (between-locality) measures (e.g., Jaccard and Sørensen dissimilarity) were not reliable for detecting steps, but instead detected departures from fixation. The beta diversity measures best able to detect steps were: Shannon Information based measures, GST based measures, a Jost-D related measure, and Bray-Curtis dissimilarity. No one measure was best overall, with a trade-off between those measures with high step detection sensitivity (GST and Bray-Curtis) and those that minimised false positives (a variant of Shannon Information). Therefore, when detecting steps, we recommend understanding the differences between measures and using a combination of approaches

Tropical plants do not have narrower temperature tolerances, but are more at risk from warming because they are close to their upper thermal limits

Aim: Tropical species are thought to be more susceptible to climate warming than are higher latitude species. This prediction is largely based on the assumption that tropical species can tolerate a narrower range of temperatures. While this prediction holds for some animal taxa, we do not yet know the latitudinal trends in temperature tolerance for plants. We aim to address this knowledge gap and establish if there is a global trend in plant warming risk. Location: Global. Time period: Present–2070. Major taxa studied: Plants. Methods: We used 9,737 records for 1,312 species from the Kew Gardens’ global germination database to quantify global patterns in germination temperature. Results: We found no evidence for a latitudinal gradient in the breadth of temperatures at which plant species can germinate. However, tropical plants are predicted to face the greatest risk from climate warming, because they experience temperatures closer to their upper germination limits. By 2070, over half (79/142) of tropical plant species are predicted to experience temperatures exceeding their optimum germination temperatures, with some even exceeding their maximum germination temperature (41/190). Conversely, 95% of species at latitudes above 45° are predicted to benefit from warming, with environmental temperatures shifting closer to the species’ optimal germination temperatures. Main conclusions: The prediction that tropical plant species would be most at risk under future climate warming was supported by our data, but through a different mechanism to that generally assumed