Adaptation and the physiology of ocean acidification

Ocean acidification, caused by the uptake of atmospheric CO2, is a threat to marine biodiversity, potentially rivalling the threat imposed by rising temperatures in some marine ecosystems. Although a growing body of literature documents negative effects of acidification on marine organisms, the majority of this work has focused on the effects of future conditions on modern populations, ignoring the potential effects of adaptation and physiological acclimatization. We review current literature on the potential for adaptation to elevated pCO2 in marine organisms. Although this body of work is currently quite small, we argue that data on the physiological effects of acidification, natural variation in pH and lessons learned from previous work on thermal adaptation can all inform predictions and priorities for future research. Spatially varying selection is one of the most important forces maintaining intraspecific genetic variation. Unlike temperature, pH lacks a strong and persistent global gradient, and so selection may maintain less adaptive variation for pH than for temperature. On the other hand, we are only beginning to amass long-term data sets for pH variation in natural habitats, and thus, pH gradients may be more common than previously observed. Two of the most important effects of elevated pCO2 are reduced calcification and changes in metabolism. We discuss the ways that a detailed understanding of the physiological mechanisms underlying these effects is key to predicting the capacity for acclimatization and adaptation. Important priorities for future research will be to assess local adaptation to pH conditions and to measure the capacity for adaptation to future acidified conditions in natural populations. Tools for this work include traditional quantitative genetics, transcriptomics and the adaptation of ion-sensitive field-effect transistor (ISFET) technology for use in continuous seawater pH monitoring in the field. © 2012 British Ecological Society

Plastic and evolved responses to global change: What can we learn from comparative transcriptomics?

© The American Genetic Association. 2015. All rights reserved. Physiological plasticity and adaptive evolution may facilitate persistence in a changing environment. As a result, there is an interest in understanding species\u27 capacities for plastic and evolved responses, and the mechanisms by which these responses occur. Transcriptome sequencing has become a powerful tool for addressing these questions, providing insight into otherwise unobserved effects of changing conditions on organismal physiology and variation in these effects among individuals and populations. Here, we review recent studies using comparative transcriptomics to understand plastic and evolutionary responses to changing environments. We focus on 2 areas where transcriptomics has played an important role: first, in understanding the genetic basis for local adaptation to current gradients as a proxy for future adaptation, and second, in understanding organismal responses to multiple stressors. We find most studies examining multiple stressors have tested the effects of each stressor individually; the few studies testing multiple stressors simultaneously have found synergistic effects on gene expression that would not have been predicted from single stressor studies. We discuss the importance of robust experimental design to allow for a more sophisticated characterization of transcriptomic responses and conclude by offering recommendations for future research, including integrating genomics with transcriptomics, testing gene regulatory networks, and comparing the equivalence of transcription to translation and the effects of environmental stress on the proteome

Camera trap arrays improve detection probability of wildlife: Investigating study design considerations using an empirical dataset.

Camera trapping is a standard tool in ecological research and wildlife conservation. Study designs, particularly for small-bodied or cryptic wildlife species often attempt to boost low detection probabilities by using non-random camera placement or baited cameras, which may bias data, or incorrectly estimate detection and occupancy. We investigated the ability of non-baited, multi-camera arrays to increase detection probabilities of wildlife. Study design components were evaluated for their influence on wildlife detectability by iteratively parsing an empirical dataset (1) by different sizes of camera arrays deployed (1-10 cameras), and (2) by total season length (1-365 days). Four species from our dataset that represented a range of body sizes and differing degrees of presumed detectability based on life history traits were investigated: white-tailed deer (Odocoileus virginianus), bobcat (Lynx rufus), raccoon (Procyon lotor), and Virginia opossum (Didelphis virginiana). For all species, increasing from a single camera to a multi-camera array significantly improved detection probability across the range of season lengths and number of study sites evaluated. The use of a two camera array increased survey detection an average of 80% (range 40-128%) from the detection probability of a single camera across the four species. Species that were detected infrequently benefited most from a multiple-camera array, where the addition of up to eight cameras produced significant increases in detectability. However, for species detected at high frequencies, single cameras produced a season-long (i.e, the length of time over which cameras are deployed and actively monitored) detectability greater than 0.75. These results highlight the need for researchers to be critical about camera trap study designs based on their intended target species, as detectability for each focal species responded differently to array size and season length. We suggest that researchers a priori identify target species for which inference will be made, and then design camera trapping studies around the most difficult to detect of those species

Trait Correlations in the Genomics Era

© 2017 Elsevier Ltd Thinking about the evolutionary causes and consequences of trait correlations has been dominated by quantitative genetics theory that is focused on hypothetical loci. Since this theory was initially developed, technology has enabled the identification of specific genetic variants that contribute to trait correlations. Here, we review studies of the genetic basis of trait correlations to ask: What has this new information taught us? We find that causal variants can be pleiotropic and/or linked in different ways, indicating that pleiotropy and linkage are not alternative genetic mechanisms. Further, many trait correlations have a polygenic basis, suggesting that both pleiotropy and linkage likely contribute. We discuss implications of these findings for the evolutionary causes and consequences of trait correlations

Population-specific responses in eastern oysters exposed to low salinity in the northern Gulf of Mexico

Eastern oysters, Crassostrea virginica, are facing rapid environmental changes in the northern Gulf of Mexico and can respond to these changes via plasticity or evolution. Plastic responses can immediately buffer against environmental changes, although this buffering may impact the organism’s ability to evolve in subsequent generations. While plasticity and evolution are not mutually exclusive, the relative contribution and interaction between them remains unclear. In this study, we investigated the roles of plastic and evolved responses of C. virginica acclimated to low salinity using a common garden experiment with four populations exposed to two salinities. We used three transcriptomic analyses (edgeR, PERMANOVA and WGCNA) combined with physiology data to identify the effect of genotype (population), environment (salinity) and the genotype–environment interaction on both whole-organism and molecular phenotypes. We demonstrate that variation in gene expression is mainly driven by population, with relatively small changes in response to salinity. In contrast, the morphology and physiology data reveal that salinity has a larger influence on oyster performance than the population of origin. All analyses lacked signatures of the genotype×environment interaction and, in contrast to previous studies, we found no evidence for population-specific responses to low salinity. However, individuals from the highest salinity estuary displayed highly divergent gene expression from that of other populations, which could potentially drive population-specific responses to other stressors. Our findings suggest that C. virginica largely rely on plasticity in physiology to buffer the effects of low salinity, but that these changes in physiology do not rely on large persistent changes in gene expression