The evolutionary ecology of interactive synchronism: The illusion of the optimal phenotype

In this article, we discuss some ecological-evolutionary strategies that allow synchronization of organisms, resources, and conditions. Survival and reproduction require synchronization of life cycles of organisms with favourable environmental and ecological features and conditions. This interactive synchronization can occur directly, through pairwise or diffuse co-evolution, or indirectly, for example, as a result of actions of ecosystem engineers and facilitator species. Observations of specific interactions, especially those which have coevolved, may give the false impression that evolution results in optimal genotypes or phenotypes. However, some phenotypes may arise under evolutionary constraints, such as simultaneous evolution of multiple traits, lack of a chain of fit transitional forms leading to an optimal phenotype, or by limits inherent in the process of selection, set by the number of selective deaths and by interference between linked variants. Although there are no optimal phenotypes, optimization models applied to particular species may be useful for a better understanding of the nature of adaptations. The evolution of adaptive strategies results in variable life histories. These strategies can minimize adverse impacts on the fitness of extreme or severe environmental conditions on survival and reproduction, and may include reproductive strategies such as semelparity and iteroparity, or morphological, physiological, or behavioural traits such as diapause, seasonal polyphenism, migration, or bet-hedging. However, natural selection cannot indefinitely maintain intra-population variation, and lack of variation can ultimately extinguish populations

How likely are adaptive responses to mitigate the threats of climate change for amphibians globally?

Whether species are capable of adapting to rapid shifts in climate raises considerable interest. Analyses based on niche models often assume niche conservatism and equilibrium with climate, implying that species will persist only in regions where future climatic conditions match their current conditions and that they will colonize these regions promptly. However, species may adapt to changing climate and persist where future climates differ from their current optimum. Here, we provide a first macroecological generalization to the approach of evolutionary rescue, by comparing the expected shift in mean temperature within the geographic range of 7193 species of amphibians worldwide, under alternative warming scenarios. Expected evolutionary change is expressed in units of standard deviations of mean temperature, per generation (Haldanes) and compared with theoretical models defining the maximum sustainable evolutionary rates (MSER) for each species. For the pessimistic emission scenario RCP8.5, shifts in mean temperature vary between near-zero and 6°C within the geographic ranges for most species, with a median equal to 3.75°C. The probability of evolutionary rescue in temperature peaks is higher than 0.05 for about 55% of the species and higher than 0.95 for only 12% of the species. Therefore, the predicted shift in mean temperature would be too extreme to deal with for almost half of the species. When evolutionary plasticity is incorporated, this scenario becomes more optimistic, with about 44% of the species being likely to shift their thermal peaks tracking future warming. These figures are not random in geographical space: evolutionary rescue would be unlikely in the tropics, especially in South America (Amazonia), parts of Africa, Indonesia, and in the Mediterranean region. Given the uncertainty in demographic and genetic parameters for species’ responses to climate change, we caution that it remains difficult to assess the realism of the macroecological generalization. In any case, it may be precautionary to assume that our results are not liberal, showing low probability of adaptation for most of the species and thus that the persistence of populations by evolutionary rescue may, in general, be unlikely in the long term

Quantitative genetics of extreme insular dwarfing: The case of red deer on Jersey

[Aim]: The Island Rule—that is, the tendency for body size to decrease in large mammals and increase in small mammals on islands has been commonly evaluated through mac-roecological or macroevolutionary, pattern-orientated approaches, which generally fail to model the microevolutionary processes driving either dwarfing or gigantism. Here, we seek to identify which microevolutionary process could have driven extreme insular dwarfism in the extinct dwarf red deer population on the island of Jersey.[Location]: Jersey, UK (Channel Islands).[Taxon]: Red deer (Cervus elaphus).[Methods]: We applied an individual-based quantitative genetics model parameterized with red deer life-history data to study the evolution of dwarfism in Jersey's deer, con-sidering variations in island area and isolation through time due to sea level changes.[Results]: The body size of red deer on Jersey decreased fast early on, due to pheno-typic plasticity, then kept decreasing almost linearly over time down to the actual body size of the Jersey deer (36kg on average). Only 1% of 10,000 replicates failed to reach that size in our simulations. The distribution of time to adaptation in these simulations was right skewed, with a median of 395 generations (equivalent to roughly 4kyr), with complete dwarfism effectively occurring in less than 6kyr 84.6% of times. About 72% of the variation in the time to adaptation between simulations was col-lectively explained by higher mutational variance, the number of immigrants from the continent after isolation, available genetic variance, heritability, and phenotypic plasticity.[Main Conclusions]: The extreme dwarfing of red deer on Jersey is an expected out-come of high mutational variance, high immigration rate, a wide adaptive landscape, low levels of inbreeding, and high phenotypic plasticity (in the early phase of dwarfing), all occurring within a time window of around 6kyr. Our model reveals how extreme dwarfism is a plausible outcome of common, well-known evolutionary processes.This study is a contribution of the INCT in Ecology, Evolution and Biodiversity Conservation founded by MCTIC/CNPq/FAPEG (grant 465610/2014-5), arising from the workshop “Fast Evolution on Islands”, organized by AMCS and JAFD-F. Authors EB, FN, WS, KSS, RSS, and ZASV are supported by CAPES MsC or Doctoral fellowships. JAFD-F, RT, TFR, and RD are supported by CNPq Productivity Fellowships and grants, and LJ and EB received CNPq/DTI-A Fellowships from INCT. JH was supported by the project ‘Predicting diversity variations across scales through process-based models linking community ecology and biogeography’ (CNPq PVE 314523/2014-6), and AMCS by a Spanish MICIU Juan de la Cierva-Incorporación (IJCI-2014-19502) fellowship.Peer reviewe

Equilibrium of Global Amphibian Species Distributions with Climate

A common assumption in bioclimatic envelope modeling is that species distributions are in equilibrium with contemporary climate. A number of studies have measured departures from equilibrium in species distributions in particular regions, but such investigations were never carried out for a complete lineage across its entire distribution. We measure departures of equilibrium with contemporary climate for the distributions of the world amphibian species. Specifically, we fitted bioclimatic envelopes for 5544 species using three presence-only models. We then measured the proportion of the modeled envelope that is currently occupied by the species, as a metric of equilibrium of species distributions with climate. The assumption was that the greater the difference between modeled bioclimatic envelope and the occupied distribution, the greater the likelihood that species distribution would not be at equilibrium with contemporary climate. On average, amphibians occupied 30% to 57% of their potential distributions. Although patterns differed across regions, there were no significant differences among lineages. Species in the Neotropic, Afrotropics, Indo-Malay, and Palaearctic occupied a smaller proportion of their potential distributions than species in the Nearctic, Madagascar, and Australasia. We acknowledge that our models underestimate non equilibrium, and discuss potential reasons for the observed patterns. From a modeling perspective our results support the view that at global scale bioclimatic envelope models might perform similarly across lineages but differently across regions

Coefficient shifts in geographical ecology: an empirical evaluation of spatial and non-spatial regression

Copyright © 2009 The Authors. Copyright © ECOGRAPHY 2009.A major focus of geographical ecology and macro ecology is to understand the causes of spatially structured ecological patterns. However, achieving this understanding can be complicated when using multiple regressions, because the relative importance of explanatory variables, as measured by regression coefficients, can shift depending on whether spatially explicit or non-spatial modelling is used. However, the extent to which coefficients may shift and why shifts occur are unclear. Here, we analyze the relationship between environmental predictors and the geographical distribution of species richness, body size, range size and abundance in 97 multi-factorial data sets. Our goal was to compare standardized partial regression coefficients of non-spatial ordinary least squares regressions (i.e. models fitted using ordinary least squares without taking autocorrelation into account; “OLS models” hereafter) and eight spatial methods to evaluate the frequency of coefficient shifts and identify characteristics of data that might predict when shifts are likely. We generated three metrics of coefficient shifts and eight characteristics of the data sets as predictors of shifts. Typical of ecological data, spatial autocorrelation in the residuals of OLS models was found in most data sets. The spatial models varied in the extent to which they minimized residual spatial autocorrelation. Patterns of coefficient shifts also varied among methods and datasets, although the magnitudes of shifts tended to be small in all cases. We were unable to identify strong predictors of shifts, including the levels of autocorrelation in either explanatory variables or model residuals. Thus, changes in coefficients between spatial and non-spatial methods depend on the method used and are largely idiosyncratic, making it difficult to predict when or why shifts occur. We conclude that the ecological importance of regression coefficients cannot be evaluated with confidence irrespective of whether spatially explicit modelling is used or not. Researchers may have little choice but to be more explicit about the uncertainty of models and more cautious in their interpretation

ENM2020 : A FREE ONLINE COURSE AND SET OF RESOURCES ON MODELING SPECIES NICHES AND DISTRIBUTIONS

The field of distributional ecology has seen considerable recent attention, particularly surrounding the theory, protocols, and tools for Ecological Niche Modeling (ENM) or Species Distribution Modeling (SDM). Such analyses have grown steadily over the past two decades-including a maturation of relevant theory and key concepts-but methodological consensus has yet to be reached. In response, and following an online course taught in Spanish in 2018, we designed a comprehensive English-language course covering much of the underlying theory and methods currently applied in this broad field. Here, we summarize that course, ENM2020, and provide links by which resources produced for it can be accessed into the future. ENM2020 lasted 43 weeks, with presentations from 52 instructors, who engaged with >2500 participants globally through >14,000 hours of viewing and >90,000 views of instructional video and question-and-answer sessions. Each major topic was introduced by an "Overview" talk, followed by more detailed lectures on subtopics. The hierarchical and modular format of the course permits updates, corrections, or alternative viewpoints, and generally facilitates revision and reuse, including the use of only the Overview lectures for introductory courses. All course materials are free and openly accessible (CC-BY license) to ensure these resources remain available to all interested in distributional ecology.Peer reviewe

Variance partitioning and spatial eigenvector analyses with large macroecological datasets

Macroecological data are usually structured in space, so taking into account spatial autocorrelation in regression and correlation analyses is essential for a better understanding of patterns and processes. Many methods are available to deal with spatial autocorrelation, but there are some difficulties when one is dealing with huge geographical extents and fine-scale data. So, we propose a relatively simple and fast computer-intensive approach to deal with Principal Coordinate of Neighbor Matrices (PNCM)/Moran’s Eigenvector Mapping (MEM) analyses for large datasets, using global richness pattern of sharks as a model. We performed a variance partitioning approach by regressing species richness against environmental variables and spatial eigenvectors derived from PCNM. Due to the large number of ocean grid cells (> 9000), we ran the analyses 1000 timesby randomly subsampling each time 50 to 4500 cells and compared the distribution of the variance partitioning components, as well as the slopes of the environmental variables. We also estimated Moran’s I coefficients for regression residuals to check if spatial eigenvectors took into account spatial autocorrelation. Comparing statistics of analyses with different sample sizes, we note that although the environmental component increases linearly, other components (unique space and shared) of the most important variables stabilize with about 1000 cells, whereas all other smaller effects tend to stabilize between 2500 and 3000 cells. Besides that, PCNM eigenvectors were able to control spatial autocorrelation very well. We showed that shark richness patterns are strongly and positively correlated with temperature range, according to the well-known pattern of distribution for the taxon, and strong negatively correlated with oxygen supplies, which are higher in polar zones where  ice acts as a barrier to sharks. Our approach clearly shows that it is possible to perform a robust evaluation of global patterns of diversity using eigenvector approaches based on a resampling strategy and allows effective computation of the variance partitioning even when dealing with large datasets

Variance partitioning and spatial eigenvector analyses with large macroecological datasets

Macroecological data are usually structured in space, so taking into account spatial autocorrelation in regression and correlation analyses is essential for a better understanding of patterns and processes. Many methods are available to deal with spatial autocorrelation, but there are some difficulties when one is dealing with huge geographical extents and fine-scale data. So, we propose a relatively simple and fast computer-intensive approach to deal with Principal Coordinate of Neighbor Matrices (PNCM)/Moran’s Eigenvector Mapping (MEM) analyses for large datasets, using global richness pattern of sharks as a model. We performed a variance partitioning approach by regressing species richness against environmental variables and spatial eigenvectors derived from PCNM. Due to the large number of ocean grid cells (> 9000), we ran the analyses 1000 timesby randomly subsampling each time 50 to 4500 cells and compared the distribution of the variance partitioning components, as well as the slopes of the environmental variables. We also estimated Moran’s I coefficients for regression residuals to check if spatial eigenvectors took into account spatial autocorrelation. Comparing statistics of analyses with different sample sizes, we note that although the environmental component increases linearly, other components (unique space and shared) of the most important variables stabilize with about 1000 cells, whereas all other smaller effects tend to stabilize between 2500 and 3000 cells. Besides that, PCNM eigenvectors were able to control spatial autocorrelation very well. We showed that shark richness patterns are strongly and positively correlated with temperature range, according to the well-known pattern of distribution for the taxon, and strong negatively correlated with oxygen supplies, which are higher in polar zones where  ice acts as a barrier to sharks. Our approach clearly shows that it is possible to perform a robust evaluation of global patterns of diversity using eigenvector approaches based on a resampling strategy and allows effective computation of the variance partitioning even when dealing with large datasets

Too simple models may predict the island rule for the wrong reasons

Biddick & Burns (2021) proposed a null/neutral model that reproduces the island rule as a product of random drift. We agree that it is unnecessary to assume adaptive processes driving island dwarfing or gigantism, but several flaws make their approach unrealistic and thus unsuitable as a stochastic model for evolutionary size changes.This paper is a contribution of the INCT in Ecology, Evolution and Biodiversity Conservation founded by CNPq/FAPEG, in the context of the workshop ‘Fast Evolution on Islands’

Too simple models may predict the island rule for the wrong reasons

- Biddick & Burns (2021) proposed a null/neutral model that reproduces the island rule as a product of random drift. We agree that it is unnecessary to assume adaptive processes driving island dwarfing or gigantism, but several flaws make their approach unrealistic and thus unsuitable as a stochastic model for evolutionary size changes