(C) PLOS One
This story was originally published by PLOS One and is unaltered.
. . . . . . . . . .



Characterizing biological responses to climate variability and extremes to improve biodiversity projections [1]

['Lauren B. Buckley', 'Department Of Biology', 'University Of Washington', 'Seattle', 'Wa', 'United States Of America', 'Emily Carrington', 'Michael E. Dillon', 'Department Of Zoology', 'Physiology']

Date: 2023-06

Projecting ecological and evolutionary responses to variable and changing environments is central to anticipating and managing impacts to biodiversity and ecosystems. Current modeling approaches are largely phenomenological and often fail to accurately project responses due to numerous biological processes at multiple levels of biological organization responding to environmental variation at varied spatial and temporal scales. Limited mechanistic understanding of organismal responses to environmental variability and extremes also restricts predictive capacity. We outline a strategy for identifying and modeling the key organismal mechanisms across levels of biological organization that mediate ecological and evolutionary responses to environmental variation. A central component of this strategy is quantifying timescales and magnitudes of climatic variability and how organisms experience them. We highlight recent empirical research that builds this information and suggest how to design future experiments that can produce more generalizable principles. We discuss how to create biologically informed projections in a feasible way by combining statistical and mechanistic approaches. Predictions will inform both fundamental and practical questions at the interface of ecology, evolution, and Earth science such as how organisms experience, adapt to, and respond to environmental variation at multiple hierarchical spatial and temporal scales.

Funding: This work was supported by the National Science Foundation (DBI-1349865, DEB-1951356 to L.B.B.; EF-1921562, OIS-1826834 to M.E.D.; Dimensions-1737778, ORCC-2222328 to C.G-R; DEB-1555876 to M.C.U.), NASA (80NSSC22K0883 to M.C.U), the Arden Chair in Ecology and Evolutionary Biology (to M.C.U.), and the L. Floyd Clark Chair in Zoology and Physiology (to M.E.D.). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2023 Buckley et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Here we propose an approach to tackle the problem of unpredictability in climate change biology that focuses on characterizing and generalizing the mechanisms by which organisms respond and adapt to environmental variability. We aim to engage and inspire synergies among physical scientists quantifying environmental variability and extremes, molecular and organismal biologists probing the biological mechanisms underlying responses to environmental variability, and computational researchers working to improve biodiversity projections. We first overview existing biodiversity projection approaches and challenges that limited their performance. We then present and illustrate a strategy for characterizing key organismal mechanisms and incorporating them in predictive models. We address questions essential to improving projections of biodiversity responses to climate change: How can we tractably identify the key organismal mechanisms across levels of biological organization that mediate ecological and evolutionary responses to environmental variation? How can we feasibly and generally include these mechanisms in predictive models?

The limited understanding of biological responses to extremes also stems from constraints in quantifying the incidence of climate extremes. For example, approaches to quantifying marine heat waves have only recently been developed and have uncovered increases in frequency and duration over recent decades [ 16 ] with implications for species, communities, and ecosystems [ 17 ]. Coupling the recent advances in threat quantification with information on organismal sensitivity offers a path forward in predicting biological responses to thermal extremes and variability [ 18 ].

Although the responses of well-studied organisms to average conditions are generally known, the role of environmental variability in shaping organismal performance and fitness is still poorly understood [ 8 , 9 ]. Organisms integrate variability in different ways and apply strategies including microhabitat choice or plastic changes in physiology to avoid low-fitness conditions [ 10 ]. Along with variability, organisms must also contend with an increasing incidence of climate extremes, which are when variability crosses a threshold [ 11 ]. However, responses to variability and extremes are not often understood, tested properly, nor incorporated into predictive modeling [ 12 ]. Improving projections of biological responses is imperative for policy and management since the biodiversity and ecosystem impacts of increases in variability and extremes are accelerating [ 13 , 14 ]. Accurately predicting climate change impacts is essential to maximize the effectiveness of limited conservation and management resources [ 15 ].

Biological responses to climate change vary dramatically among populations and species to the degree to which some have argued that they are inherently unpredictable [ 1 , 2 ]. Simple approaches to predicting the responses of individual populations or species exhibit mixed performance. Shifts in species’ distributions are often poorly predicted by statistical models correlating either species occurrences to their environment [ 3 ] or traits to the magnitude of species’ response [ 4 ]. Some species predicted to become extinct from climate change have persisted through adaptation [ 5 ], whereas other species became extinct before their threat was known [ 6 ]. Further, biologists are just beginning to understand how genetic and epigenetic variation alters adaptation and resilience [ 7 ].

Biodiversity projection approaches and challenges

The ongoing and looming biodiversity impacts of climate change are well established. However, the definitive proportions of species estimated to face extinction in the IPCC (Intergovernmental Panel on Climate Change) WGII and the IPBES (Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services) reports are based on coarse modeling approaches with high uncertainty. For example, one study that informed the IPCC report projects that with emissions pledges corresponding to ~3.2°C warming, ~49% of insects, 44% of plants, and 26% of vertebrates will lose >50% of their ranges and thus face a high risk of extinction [19]. In contrast, overall rates of extinction are estimated at 9% for a similar rise in temperature when aggregated over 131 studies implementing a spectrum of modeling approaches and a more conservative ~95% range loss threshold for extinction risk [13].

These and other biodiversity projections which inform policy recommendations are mostly generated by correlative niche models [14]. In this approach, species’ locality data are correlated with underlying environmental data to estimate an environmental response surface, often termed a “climate envelope” [20]. Correlative niche models can be readily implemented using limited data and perform well on some tasks such as describing existing species distributions [21]. However, they often perform poorly in extrapolation, due to issues such as novel climates, changing species interactions, complex relationships between environmental variables, or interactions between environment and genotype/epigenotype [3]. Yet, alternative modeling approaches are not sufficiently general or parameterized well enough to make biodiversity projections at the scales desired to inform policy. Thus, we are ill-prepared to understand which species are under the greatest threats from climate change and design mitigation strategies to prevent their loss.

One way to improve our ability to predict climate change responses is to create models that incorporate the links between environmental variation, evolutionary history, genetic and epigenetic variation, functional traits, and subsequent demographic responses [14,22,23]. Functional traits are organismal properties that affect individual performance, including survival, development, growth, and reproduction [24]. This functional approach builds from a growing body of research that suggests that linking physiological traits to realistic environmental variation is often central to understanding ecological and evolutionary dynamics [11,25]. Moreover, an understanding of realistic genetic and epigenetic contributions to the phenotype is needed to predict changes to functional traits [7,26–28]. Lastly, because traits link multiple biological levels, functional trait models can reveal how responses to environmental variability integrate across multiple levels of biological organization, including ecosystem properties.

Two classes of models leverage functional traits to better predict responses to novel and variable environments. Mechanistic niche models scale up from functional traits and their environmental interactions to performance and ultimately fitness and are often discussed as a means of improving predictions of climate change responses (Fig 1). Researchers have developed mechanistic niche models that provide proof-of-principle for a variety of species [22]. However, these models are seldom applied widely because we usually lack the high-quality data to parameterize them for most species on Earth [14]. Moreover, the lack of a flexible, general modeling framework creates a roadblock for those without the time or resources to develop a model crafted for an individual species or ecosystem [15].

PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 1. Correlative, hybrid, and mechanistic niche models for predicting distributions differ in their input, modeling approach, and output. Climate inputs are usually several temporally-aggregated (e.g., quarterly, annually) gridded datasets for correlative models versus climate time series for mechanistic models. Occurrence coordinates are input into correlative models whereas mechanistic models are parameterized with genotypes or phenotypes and other biological information. Correlative models predict the probability of occurrence based on statistically relating the climate data to occurrences and then spatially projecting the relationship. Mechanistic models explicitly model the processes by which organisms respond to the climate conditions. Often, empirically measured performance curves are used to estimate survival and fecundity (bottom row). Fitness estimates as a function of genotype or phenotype are estimated for each grid cell. Hybrid models meld correlative and mechanistic approaches. The most common strategy is to input biologically-informed layers into correlative models (middle row), but other strategies include using biological information to inform statistical relationships or statistically estimating parameters in process models. https://doi.org/10.1371/journal.pclm.0000226.g001

Hybrid niche models offer a practical alternative to purely mechanistic niche models. They incorporate key biological mechanisms, but use computational pattern-based approaches to inform uncertain or unknown parameters or relationships. Despite the benefits of this more flexible, mechanistic approach, these models have seldom been implemented [14,29–32]. Consequently, a potentially important process-based tool for predicting biological responses to climate change remains underdeveloped and under-used.

Here, we seek to define a middle ground by developing models that are feasible to parameterize and implement computationally but capture the key biological mechanisms needed to predict responses to climate variability and change [14,30,32–35]. We follow the recommendations for creating interoperable biodiversity projection models that are developed with open, reproducible, flexible, and integrative design principles [15]. Resultant models should account for uncertainties in data sources, model structure, and outputs [36].

Environmental variability Organismal and ecosystem processes respond to multiple climatic conditions at numerous spatial and temporal scales ranging from minutes to millennia and meters to miles [8,37–39]. Yet, most ecological predictions rely on environmental variables, such as air temperatures, measured at unrealistically large spatial and temporal resolutions relative to organismal processes as well as body sizes, movement distances, and generation lengths [38–41]. This mismatch in scales reduces predictive accuracy by omitting variation at the scale of an organism’s exposure [42,43] (Fig 2). Moreover, the non-linear responses of many biological processes to environmental variation cause performance at average conditions to depart dramatically from average performance over time [44,45]. Hence, we have yet to resolve the basic links between organismal fitness and environmental heterogeneity and their scaling and incorporate these insights into predictive models. PPT PowerPoint slide

PNG larger image

TIFF original image Download: Fig 2. A thermal image of an intertidal mussel bed reveals substantial thermal variation over fine spatial scales (~1m, see visual image inset) as well as temperatures that exceed cool air temperatures due to solar heating. https://doi.org/10.1371/journal.pclm.0000226.g002 Examples are rapidly accumulating showing that variability and extremes can shape organismal and ecological responses to climate, with implications for distributions and diversity [46]. An assessment of responses to climate and weather extremes across taxa found that the majority of responses (including changes in body condition, fitness components, abundance, and distribution) were negative, followed by many ambiguous responses [47]. Most cases of neutral or positive responses were for species that benefit from disturbance. Short-term weather was found to better predict bird distributions than long term climate averages [48]. Tolerance to thermal extremes better predicts Drosophila distributions than does the thermal sensitivity of population growth [49]. The interaction of gradual climate warming and increasing extreme events is likely to exacerbate biodiversity impacts. Gradual warming can cumulatively stress organisms or elevate environmental variability and extremes into a thermal range that severely impacts organisms, which has been termed the press and pulse, respectively, of climate change impacts [50]. Thermal extremes, rather than gradual warming, appear to be driving insect responses to climate change, with implications for agriculture and biodiversity [51,52]. Heat extremes of the early 21st century are projected to become routine during the late 21st century and will interact with other weather extremes including drought and intense precipitation [53]. Organisms that can capitalize on these increasingly common extremes will be the winners of climate change, while those that cannot will be the losers. Yet, we still cannot predict which species are winners or losers or when current winners will become losers [12, but see 54]. Organisms in many regions will experience combinations of environmental conditions that are novel across their evolutionary history due to climate change [55,56]. However, linkages between organisms and their environments that are estimated statistically without attention to biological mechanisms are likely to extrapolate poorly across spatial and temporal scales and to novel environments [3]. Extrapolation requires a more holistic understanding of the numerous physiological processes responding to multiple environmental cues at disparate spatial and temporal scales. Additionally, the reshuffling of communities and other human-induced changes is also exposing organisms to novel species interactions [57], but phenomenological approaches tend to implicitly assume species interactions remain fixed [14]. Empirical and theoretical work has largely focused on thermal and moisture sensitivity, but research is increasingly highlighting the importance of considering how multiple environmental factors interact to influence physiology and performance. Thermal variability tends to increase overall performance, via the increase in biological rates with temperature, until variability results in stressful temperatures [58]. Carryover effects including plasticity and damage can influence what temperatures are stressful [59]. Timescales of environmental variability relative to generation times and the duration of sensitive life stages are known to influence whether organisms can respond to the variability via plasticity or genetic adaptation [60]. In particular, high levels of unpredictable environmental variation often will be associated with constitutive molecular stress responses and potential tradeoffs with the strength of induced responses. The frequency and intensity of short term environmental variation relative to seasonal variation can determine the extent of temporal fluctuations in selection, the role of plasticity in altering selection, and resultant rates of evolution [61,62]. Spatial and temporal behavioral shifts can substantially buffer organisms from exposure to climate variability and change, which can slow thermal adaptation [41,43,59]. Comparisons of tropical and temperate elevation gradients have highlighted how organismal sensitivity shifts in response to environmental variability. Tropical organisms are thought to be particularly sensitive to climate change due to the evolution of thermal specialization to relatively constant, warm climates [63]. However, finer-scaled analyses indicate that high temporal environmental variability also can produce impacts of a similar or greater magnitude in temperate areas [64,65]. The survival of tropical organisms may be particularly affected by increasing variability. Increasing temperatures increase ectotherm energetic costs and decrease activity times at tropical or low-elevation sites [66]. Conversely, increasing temperatures may increase energy balance (and thus fecundity) at high-elevation sites [67].

[END]
---
[1] Url: https://journals.plos.org/climate/article?id=10.1371/journal.pclm.0000226

Published and (C) by PLOS One
Content appears here under this condition or license: Creative Commons - Attribution BY 4.0.

via Magical.Fish Gopher News Feeds:
gopher://magical.fish/1/feeds/news/plosone/

You are viewing proxied material from magical.fish. The copyright of proxied material belongs to its original authors. Any comments or complaints in relation to proxied material should be directed to the original authors of the content concerned. Please see the disclaimer for more details.