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Foulk A, Gouhier T, Choi F, Torossian JL, Matzelle A, Sittenfeld D, Helmuth B. Physiologically informed organismal climatologies reveal unexpected spatiotemporal trends in temperature. CONSERVATION PHYSIOLOGY 2024; 12:coae025. [PMID: 38779431 PMCID: PMC11109819 DOI: 10.1093/conphys/coae025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 03/15/2024] [Accepted: 04/15/2024] [Indexed: 05/25/2024]
Abstract
Body temperature is universally recognized as a dominant driver of biological performance. Although the critical distinction between the temperature of an organism and its surrounding habitat has long been recognized, it remains common practice to assume that trends in air temperature-collected via remote sensing or weather stations-are diagnostic of trends in animal temperature and thus of spatiotemporal patterns of physiological stress and mortality risk. Here, by analysing long-term trends recorded by biomimetic temperature sensors designed to emulate intertidal mussel temperature across the US Pacific Coast, we show that trends in maximal organismal temperature ('organismal climatologies') during aerial exposure can differ substantially from those exhibited by co-located environmental data products. Specifically, using linear regression to compare maximal organismal and environmental (air temperature) climatologies, we show that not only are the magnitudes of body and air temperature markedly different, as expected, but so are their temporal trends at both local and biogeographic scales, with some sites showing significant decadal-scale increases in organismal temperature despite reductions in air temperature, or vice versa. The idiosyncratic relationship between the spatiotemporal patterns of organismal and air temperatures suggests that environmental climatology cannot be statistically corrected to serve as an accurate proxy for organismal climatology. Finally, using quantile regression, we show that spatiotemporal trends vary across the distribution of organismal temperature, with extremes shifting in different directions and at different rates than average metrics. Overall, our results highlight the importance of quantifying changes in the entire distribution of temperature to better predict biological performance and dispel the notion that raw or 'corrected' environmental (and specially air temperature) climatologies can be used to predict organismal temperature trends. Hence, despite their widespread coverage and availability, the severe limitations of environmental climatologies suggest that their role in conservation and management policy should be carefully considered.
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Affiliation(s)
- Aubrey Foulk
- Department of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA 01908, USA
| | - Tarik Gouhier
- Department of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA 01908, USA
| | - Francis Choi
- Department of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA 01908, USA
| | - Jessica L Torossian
- Department of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA 01908, USA
- Volpe Center, U.S. Department of Transportation, Cambridge, MA 02142, USA
| | - Allison Matzelle
- Department of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA 01908, USA
| | - David Sittenfeld
- Center for the Environment, Museum of Science, Boston, MA 02114, USA
- School of Public Policy and Urban Affairs, Northeastern University, Boston, MA 02115, USA
| | - Brian Helmuth
- Department of Marine and Environmental Sciences, Northeastern University Marine Science Center, Nahant, MA 01908, USA
- School of Public Policy and Urban Affairs, Northeastern University, Boston, MA 02115, USA
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2
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Sadoti G, McAfee SA, Nicklen EF, Sousanes PJ, Roland CA. Evaluating multiple historical climate products in ecological models under current and projected temperatures. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2021; 31:e02240. [PMID: 33098323 PMCID: PMC7988543 DOI: 10.1002/eap.2240] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/08/2020] [Revised: 07/15/2020] [Accepted: 08/16/2020] [Indexed: 06/02/2023]
Abstract
Gridded historical climate products (GHCPs) are employed with increasing frequency when modeling ecological phenomena across large scales and predicting ecological responses to projected climate changes. Concurrently, there is an increasing acknowledgement of the need to account for uncertainty when employing climate projections from ensembles of global circulation models (GCMs) and emissions scenarios. Despite the growing usage and documented differences among GHCPs, uncertainty characterization has primarily focused on GCM and emissions scenario choice, while the consequences of using a single GHCP to make predictions over space and time have received less attention. Here we employ average July temperature data from observations and seven GHCPs to model plant canopy cover and tree basal area across central Alaska, USA. We first compare the fit of, and support for, models employing observed temperatures, GHCP temperatures, and GHCP temperatures with an elevation adjustment, finding (1) greater support for, and better fit using, elevation-adjusted vs. raw temperature models and (2) overall similar fits of elevation-adjusted models employing temperatures from observations or GHCPs. Focusing on basal area, we next compare predictions generated by elevation-adjusted models employing GHCP data under current conditions and a warming scenario of current temperatures plus 2°C, finding good agreement among GHCPs though with between-GHCP differences and variation primarily at middle elevations (~1,000 m). These differences were amplified under the warming scenario. Finally, using pooled indices of prediction variation and difference across GHCP models, we identify characteristics of areas most likely to exhibit prediction uncertainty under current and warming conditions. Despite (1) overall good performance of GHCP data relative to observations in models and (2) positive correlation among model predictions, variation in predictions across models, particularly in mid-elevation areas where the position of treeline may be changing, suggests researchers should exercise caution if selecting a single GHCP for use in models. We recommend the use of multiple GHCPs to provide additional uncertainty information beyond standard estimated prediction intervals, particularly when model predictions are employed in conservation planning.
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Affiliation(s)
- Giancarlo Sadoti
- Department of GeographyUniversity of Nevada, Reno1664 N. Virginia StreetRenoNevada89557‐0154USA
| | - Stephanie A. McAfee
- Department of GeographyUniversity of Nevada, Reno1664 N. Virginia StreetRenoNevada89557‐0154USA
| | - E. Fleur Nicklen
- Central Alaska NetworkNational Park Service4175 Geist RoadFairbanksAlaska99709USA
| | - Pamela J. Sousanes
- Central Alaska NetworkNational Park Service4175 Geist RoadFairbanksAlaska99709USA
| | - Carl A. Roland
- Central Alaska NetworkNational Park Service4175 Geist RoadFairbanksAlaska99709USA
- Denali National Park and PreserveNational Park Service4175 Geist RoadFairbanksAlaska99709USA
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3
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Kearney MR, Gillingham PK, Bramer I, Duffy JP, Maclean IM. A method for computing hourly, historical, terrain‐corrected microclimate anywhere on earth. Methods Ecol Evol 2019. [DOI: 10.1111/2041-210x.13330] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Michael R. Kearney
- School of BioSciences The University of Melbourne Parkville Vic. Australia
| | | | - Isobel Bramer
- Faculty of Science and Technology Bournemouth University Poole UK
| | - James P. Duffy
- Environment and Sustainability Institute University of Exeter Penryn Campus Penryn UK
| | - Ilya M.D. Maclean
- Environment and Sustainability Institute University of Exeter Penryn Campus Penryn UK
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4
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Choi F, Gouhier T, Lima F, Rilov G, Seabra R, Helmuth B. Mapping physiology: biophysical mechanisms define scales of climate change impacts. CONSERVATION PHYSIOLOGY 2019; 7:coz028. [PMID: 31423312 PMCID: PMC6691486 DOI: 10.1093/conphys/coz028] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 04/24/2019] [Accepted: 05/07/2019] [Indexed: 05/11/2023]
Abstract
The rocky intertidal zone is a highly dynamic and thermally variable ecosystem, where the combined influences of solar radiation, air temperature and topography can lead to differences greater than 15°C over the scale of centimetres during aerial exposure at low tide. For most intertidal organisms this small-scale heterogeneity in microclimates can have enormous influences on survival and physiological performance. However, the potential ecological importance of environmental heterogeneity in determining ecological responses to climate change remains poorly understood. We present a novel framework for generating spatially explicit models of microclimate heterogeneity and patterns of thermal physiology among interacting organisms. We used drone photogrammetry to create a topographic map (digital elevation model) at a resolution of 2 × 2 cm from an intertidal site in Massachusetts, which was then fed into to a model of incident solar radiation based on sky view factor and solar position. These data were in turn used to drive a heat budget model that estimated hourly surface temperatures over the course of a year (2017). Body temperature layers were then converted to thermal performance layers for organisms, using thermal performance curves, creating 'physiological landscapes' that display spatially and temporally explicit patterns of 'microrefugia'. Our framework shows how non-linear interactions between these layers lead to predictions about organismal performance and survivorship that are distinct from those made using any individual layer (e.g. topography, temperature) alone. We propose a new metric for quantifying the 'thermal roughness' of a site (RqT, the root mean square of spatial deviations in temperature), which can be used to quantify spatial and temporal variability in temperature and performance at the site level. These methods facilitate an exploration of the role of micro-topographic variability in driving organismal vulnerability to environmental change using both spatially explicit and frequency-based approaches.
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Affiliation(s)
- Francis Choi
- Marine Science Center, Department of Marine and Environmental Sciences, Northeastern University, 430 Nahant Rd, Nahant, MA, USA
| | - Tarik Gouhier
- Marine Science Center, Department of Marine and Environmental Sciences, Northeastern University, 430 Nahant Rd, Nahant, MA, USA
| | - Fernando Lima
- CIBIO, Research Center in Biodiversity and Genetic Resources, University of Porto, Campus de Vairão, Vairão, Portugal
| | - Gil Rilov
- National Institute of Oceanography, Israel Oceanography and Limnology Research Institute, Haifa, Israel
| | - Rui Seabra
- CIBIO, Research Center in Biodiversity and Genetic Resources, University of Porto, Campus de Vairão, Vairão, Portugal
| | - Brian Helmuth
- Marine Science Center, Department of Marine and Environmental Sciences, Northeastern University, 430 Nahant Rd, Nahant, MA, USA
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5
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Judge R, Choi F, Helmuth B. Recent Advances in Data Logging for Intertidal Ecology. Front Ecol Evol 2018. [DOI: 10.3389/fevo.2018.00213] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
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6
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Helmuth B, Choi F, Matzelle A, Torossian JL, Morello SL, Mislan KAS, Yamane L, Strickland D, Szathmary PL, Gilman SE, Tockstein A, Hilbish TJ, Burrows MT, Power AM, Gosling E, Mieszkowska N, Harley CDG, Nishizaki M, Carrington E, Menge B, Petes L, Foley MM, Johnson A, Poole M, Noble MM, Richmond EL, Robart M, Robinson J, Sapp J, Sones J, Broitman BR, Denny MW, Mach KJ, Miller LP, O'Donnell M, Ross P, Hofmann GE, Zippay M, Blanchette C, Macfarlan JA, Carpizo-Ituarte E, Ruttenberg B, Peña Mejía CE, McQuaid CD, Lathlean J, Monaco CJ, Nicastro KR, Zardi G. Long-term, high frequency in situ measurements of intertidal mussel bed temperatures using biomimetic sensors. Sci Data 2016; 3:160087. [PMID: 27727238 PMCID: PMC5058338 DOI: 10.1038/sdata.2016.87] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2016] [Accepted: 08/30/2016] [Indexed: 11/12/2022] Open
Abstract
At a proximal level, the physiological impacts of global climate change on ectothermic organisms are manifest as changes in body temperatures. Especially for plants and animals exposed to direct solar radiation, body temperatures can be substantially different from air temperatures. We deployed biomimetic sensors that approximate the thermal characteristics of intertidal mussels at 71 sites worldwide, from 1998-present. Loggers recorded temperatures at 10–30 min intervals nearly continuously at multiple intertidal elevations. Comparisons against direct measurements of mussel tissue temperature indicated errors of ~2.0–2.5 °C, during daily fluctuations that often exceeded 15°–20 °C. Geographic patterns in thermal stress based on biomimetic logger measurements were generally far more complex than anticipated based only on ‘habitat-level’ measurements of air or sea surface temperature. This unique data set provides an opportunity to link physiological measurements with spatially- and temporally-explicit field observations of body temperature.
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Affiliation(s)
- Brian Helmuth
- Northeastern University, Marine Science Center, 430 Nahant Rd., Nahant, Massachusetts 01908, USA
| | - Francis Choi
- Northeastern University, Marine Science Center, 430 Nahant Rd., Nahant, Massachusetts 01908, USA
| | - Allison Matzelle
- Northeastern University, Marine Science Center, 430 Nahant Rd., Nahant, Massachusetts 01908, USA
| | - Jessica L Torossian
- Northeastern University, Marine Science Center, 430 Nahant Rd., Nahant, Massachusetts 01908, USA
| | | | - K A S Mislan
- University of Washington, School of Oceanography, Seattle, Washington 98195, USA
| | - Lauren Yamane
- University of California, Davis, Department of Wildlife, Fish, and Conservation Biology, Davis, California 95616, USA
| | - Denise Strickland
- University of South Carolina, Department of Biological Sciences, Columbia, South Carolina 29208, USA
| | - P Lauren Szathmary
- University of South Carolina, Department of Biological Sciences, Columbia, South Carolina 29208, USA
| | - Sarah E Gilman
- W.M. Keck Science Department of Claremont McKenna, Pitzer and Scripps Colleges, Claremont, California 91711, USA
| | - Alyson Tockstein
- University of South Carolina, Department of Biological Sciences, Columbia, South Carolina 29208, USA
| | - Thomas J Hilbish
- University of South Carolina, Department of Biological Sciences, Columbia, South Carolina 29208, USA
| | - Michael T Burrows
- Scottish Association for Marine Science, Oban, Argyll PA37 1QA, Scotland
| | - Anne Marie Power
- Anne Marie Power, School of Natural Sciences, National University of Ireland Galway, Galway H91 TK33, Ireland
| | - Elizabeth Gosling
- School of Life Sciences, Galway-Mayo Institute of Technology, Galway H91 T8NW, Ireland
| | - Nova Mieszkowska
- Marine Biological Association of the United Kingdom, Plymouth, Devon PL1 2PB, UK
| | - Christopher D G Harley
- University of British Columbia, Department of Zoology and Biodiversity Research Centre, Vancouver, British Columbia, Canada V6T1Z4
| | - Michael Nishizaki
- University of Washington, Department of Biology, Seattle, Washington 98195, USA
| | - Emily Carrington
- University of Washington, Department of Biology, Seattle, Washington 98195, USA
| | - Bruce Menge
- Oregon State University, Department of Integrative Biology, Corvallis, Oregon 97331, USA
| | - Laura Petes
- Oregon State University, Department of Integrative Biology, Corvallis, Oregon 97331, USA
| | - Melissa M Foley
- Oregon State University, Department of Integrative Biology, Corvallis, Oregon 97331, USA
| | - Angela Johnson
- Oregon State University, Department of Integrative Biology, Corvallis, Oregon 97331, USA
| | - Megan Poole
- Oregon State University, Department of Integrative Biology, Corvallis, Oregon 97331, USA
| | - Mae M Noble
- Oregon State University, Department of Integrative Biology, Corvallis, Oregon 97331, USA
| | - Erin L Richmond
- Oregon State University, Department of Integrative Biology, Corvallis, Oregon 97331, USA
| | - Matt Robart
- Oregon State University, Department of Integrative Biology, Corvallis, Oregon 97331, USA
| | - Jonathan Robinson
- Oregon State University, Department of Integrative Biology, Corvallis, Oregon 97331, USA
| | - Jerod Sapp
- Oregon State University, Department of Integrative Biology, Corvallis, Oregon 97331, USA
| | - Jackie Sones
- University of California, Davis, Bodega Marine Reserve, Bodega Bay, California 94923, USA
| | | | - Mark W Denny
- Stanford University, Hopkins Marine Station, Pacific Grove, California 93950, USA
| | - Katharine J Mach
- Stanford University, Hopkins Marine Station, Pacific Grove, California 93950, USA
| | - Luke P Miller
- Stanford University, Hopkins Marine Station, Pacific Grove, California 93950, USA
| | - Michael O'Donnell
- Stanford University, Hopkins Marine Station, Pacific Grove, California 93950, USA
| | - Philip Ross
- University of Waikato, Environmental Research Institute, Tauranga 3110, New Zealand
| | - Gretchen E Hofmann
- University of California Santa Barbara, Marine Science Institute, Santa Barbara, California 93106, USA
| | - Mackenzie Zippay
- University of California Santa Barbara, Marine Science Institute, Santa Barbara, California 93106, USA
| | - Carol Blanchette
- University of California Santa Barbara, Marine Science Institute, Santa Barbara, California 93106, USA
| | - J A Macfarlan
- University of California Santa Barbara, Marine Science Institute, Santa Barbara, California 93106, USA
| | - Eugenio Carpizo-Ituarte
- Universidad Autónoma de Baja California, Instituto de Investigaciones Oceanológicas, Ensenada, Baja California 22860, Mexico
| | - Benjamin Ruttenberg
- Universidad Autónoma de Baja California, Instituto de Investigaciones Oceanológicas, Ensenada, Baja California 22860, Mexico
| | - Carlos E Peña Mejía
- Universidad Autónoma de Baja California, Instituto de Investigaciones Oceanológicas, Ensenada, Baja California 22860, Mexico
| | - Christopher D McQuaid
- Rhodes University, Department of Zoology and Entomology, Grahamstown 6140, South Africa
| | - Justin Lathlean
- Rhodes University, Department of Zoology and Entomology, Grahamstown 6140, South Africa
| | - Cristián J Monaco
- Rhodes University, Department of Zoology and Entomology, Grahamstown 6140, South Africa
| | - Katy R Nicastro
- Rhodes University, Department of Zoology and Entomology, Grahamstown 6140, South Africa
| | - Gerardo Zardi
- Rhodes University, Department of Zoology and Entomology, Grahamstown 6140, South Africa
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7
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Kish NE, Helmuth B, Wethey DS. Physiologically grounded metrics of model skill: a case study estimating heat stress in intertidal populations. CONSERVATION PHYSIOLOGY 2016; 4:cow038. [PMID: 27729979 PMCID: PMC5055285 DOI: 10.1093/conphys/cow038] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2016] [Revised: 08/16/2016] [Accepted: 08/18/2016] [Indexed: 05/25/2023]
Abstract
Models of ecological responses to climate change fundamentally assume that predictor variables, which are often measured at large scales, are to some degree diagnostic of the smaller-scale biological processes that ultimately drive patterns of abundance and distribution. Given that organisms respond physiologically to stressors, such as temperature, in highly non-linear ways, small modelling errors in predictor variables can potentially result in failures to predict mortality or severe stress, especially if an organism exists near its physiological limits. As a result, a central challenge facing ecologists, particularly those attempting to forecast future responses to environmental change, is how to develop metrics of forecast model skill (the ability of a model to predict defined events) that are biologically meaningful and reflective of underlying processes. We quantified the skill of four simple models of body temperature (a primary determinant of physiological stress) of an intertidal mussel, Mytilus californianus, using common metrics of model performance, such as root mean square error, as well as forecast verification skill scores developed by the meteorological community. We used a physiologically grounded framework to assess each model's ability to predict optimal, sub-optimal, sub-lethal and lethal physiological responses. Models diverged in their ability to predict different levels of physiological stress when evaluated using skill scores, even though common metrics, such as root mean square error, indicated similar accuracy overall. Results from this study emphasize the importance of grounding assessments of model skill in the context of an organism's physiology and, especially, of considering the implications of false-positive and false-negative errors when forecasting the ecological effects of environmental change.
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Affiliation(s)
- Nicole E. Kish
- Marine Science Program, University of South Carolina, Columbia, SC 29208, USA
| | - Brian Helmuth
- Marine Science Program, University of South Carolina, Columbia, SC 29208, USA
- Marine Science Center, Northeastern University, Nahant, MA 01908, USA
| | - David S. Wethey
- Marine Science Program, University of South Carolina, Columbia, SC 29208, USA
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8
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Abstract
Extreme heat events cause patchy mortality in many habitats. We examine biophysical mechanisms responsible for patchy mortality in beds of the competitively dominant ecosystem engineer, the marine mussel Mytilus californianus, on the west coast of the United States. We used a biophysical model to predict daily fluctuations in body temperature at sites from southern California to Washington and used results of laboratory experiments on thermal tolerance to determine mortality rates from body temperature. In our model, we varied the rate of thermal conduction within mussel beds and found that this factor can account for large differences in body temperature and consequent mortality during heat waves. Mussel beds provide structural habitat for other species and increase local biodiversity, but, as sessile organisms, they are particularly vulnerable to extreme weather conditions. Identifying critical biophysical mechanisms related to mortality and ecological performance will improve our ability to predict the effects of climate change on these vulnerable ecosystems.
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9
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Helmuth B, Russell BD, Connell SD, Dong Y, Harley CDG, Lima FP, Sará G, Williams GA, Mieszkowska N. Beyond long-term averages: making biological sense of a rapidly changing world. ACTA ACUST UNITED AC 2014. [DOI: 10.1186/s40665-014-0006-0] [Citation(s) in RCA: 93] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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10
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Montalto V, Sarà G, Ruti PM, Dell’Aquila A, Helmuth B. Testing the effects of temporal data resolution on predictions of the effects of climate change on bivalves. Ecol Modell 2014. [DOI: 10.1016/j.ecolmodel.2014.01.019] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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11
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Potter KA, Arthur Woods H, Pincebourde S. Microclimatic challenges in global change biology. GLOBAL CHANGE BIOLOGY 2013; 19:2932-9. [PMID: 23681970 DOI: 10.1111/gcb.12257] [Citation(s) in RCA: 271] [Impact Index Per Article: 24.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2013] [Revised: 05/08/2013] [Accepted: 05/10/2013] [Indexed: 05/05/2023]
Abstract
Despite decades of work on climate change biology, the scientific community remains uncertain about where and when most species distributions will respond to altered climates. A major barrier is the spatial mismatch between the size of organisms and the scale at which climate data are collected and modeled. Using a meta-analysis of published literature, we show that grid lengths in species distribution models are, on average, ca. 10 000-fold larger than the animals they study, and ca. 1000-fold larger than the plants they study. And the gap is even worse than these ratios indicate, as most work has focused on organisms that are significantly biased toward large size. This mismatch is problematic because organisms do not experience climate on coarse scales. Rather, they live in microclimates, which can be highly heterogeneous and strongly divergent from surrounding macroclimates. Bridging the spatial gap should be a high priority for research and will require gathering climate data at finer scales, developing better methods for downscaling environmental data to microclimates, and improving our statistical understanding of variation at finer scales. Interdisciplinary collaborations (including ecologists, engineers, climatologists, meteorologists, statisticians, and geographers) will be key to bridging the gap, and ultimately to providing scientifically grounded data and recommendations to conservation biologists and policy makers.
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Affiliation(s)
- Kristen A Potter
- Division of Biological Sciences, University of Montana, Missoula, MT, 59812, USA; School of Forestry, Northern Arizona University, Flagstaff, AZ, 86011, USA
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12
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Kearney MR, Matzelle A, Helmuth B. Biomechanics meets the ecological niche: the importance of temporal data resolution. ACTA ACUST UNITED AC 2012; 215:922-33. [PMID: 22357586 DOI: 10.1242/jeb.059634] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
The emerging field of mechanistic niche modelling aims to link the functional traits of organisms to their environments to predict survival, reproduction, distribution and abundance. This approach has great potential to increase our understanding of the impacts of environmental change on individuals, populations and communities by providing functional connections between physiological and ecological response to increasingly available spatial environmental data. By their nature, such mechanistic models are more data intensive in comparison with the more widely applied correlative approaches but can potentially provide more spatially and temporally explicit predictions, which are often needed by decision makers. A poorly explored issue in this context is the appropriate level of temporal resolution of input data required for these models, and specifically the error in predictions that can be incurred through the use of temporally averaged data. Here, we review how biomechanical principles from heat-transfer and metabolic theory are currently being used as foundations for mechanistic niche models and consider the consequences of different temporal resolutions of environmental data for modelling the niche of a behaviourally thermoregulating terrestrial lizard. We show that fine-scale temporal resolution (daily) data can be crucial for unbiased inference of climatic impacts on survival, growth and reproduction. This is especially so for species with little capacity for behavioural buffering, because of behavioural or habitat constraints, and for detecting temporal trends. However, coarser-resolution data (long-term monthly averages) can be appropriate for mechanistic studies of climatic constraints on distribution and abundance limits in thermoregulating species at broad spatial scales.
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Affiliation(s)
- Michael R Kearney
- Department of Zoology, The University of Melbourne, Victoria 3010, Australia.
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13
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Ibáñez I, Gornish ES, Buckley L, Debinski DM, Hellmann J, Helmuth B, HilleRisLambers J, Latimer AM, Miller-Rushing AJ, Uriarte M. Moving forward in global-change ecology: capitalizing on natural variability. Ecol Evol 2012; 3:170-81. [PMID: 23404535 PMCID: PMC3568852 DOI: 10.1002/ece3.433] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2012] [Revised: 10/22/2012] [Accepted: 10/29/2012] [Indexed: 11/13/2022] Open
Abstract
Natural resources managers are being asked to follow practices that accommodate for the impact of climate change on the ecosystems they manage, while global-ecosystems modelers aim to forecast future responses under different climate scenarios. However, the lack of scientific knowledge about short-term ecosystem responses to climate change has made it difficult to define set conservation practices or to realistically inform ecosystem models. Until recently, the main goal for ecologists was to study the composition and structure of communities and their implications for ecosystem function, but due to the probable magnitude and irreversibility of climate-change effects (species extinctions and loss of ecosystem function), a shorter term focus on responses of ecosystems to climate change is needed. We highlight several underutilized approaches for studying the ecological consequences of climate change that capitalize on the natural variability of the climate system at different temporal and spatial scales. For example, studying organismal responses to extreme climatic events can inform about the resilience of populations to global warming and contribute to the assessment of local extinctions. Translocation experiments and gene expression are particular useful to quantitate a species' acclimation potential to global warming. And studies along environmental gradients can guide habitat restoration and protection programs by identifying vulnerable species and sites. These approaches identify the processes and mechanisms underlying species acclimation to changing conditions, combine different analytical approaches, and can be used to improve forecasts of the short-term impacts of climate change and thus inform conservation practices and ecosystem models in a meaningful way.
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Affiliation(s)
- Inés Ibáñez
- School of Natural Resources and Environment, University of MichiganAnn Arbor, Michigan
| | - Elise S Gornish
- Department of Biological Science, Florida State UniversityTallahassee, Florida
| | - Lauren Buckley
- Biology Department, University of North CarolinaChapel Hill, North Carolina
| | - Diane M Debinski
- Department of Ecology, Evolution and Organismal Biology, Iowa State UniversityAmes, Iowa
| | - Jessica Hellmann
- Department of Biological Sciences, University of Notre DameNotre Dame, Indiana
| | - Brian Helmuth
- Environment and Sustainability Program and Department of Biological Sciences, University of South CarolinaColumbia, South Carolina
| | | | - Andrew M Latimer
- Department of Plant Sciences, University of California, DavisDavis, California
| | - Abraham J Miller-Rushing
- National Park Service, Schoodic Education and Research Center and Acadia National ParkBar Harbor, Maine
| | - Maria Uriarte
- Department of Ecology, Evolution and Environmental Biology, Columbia UniversityNew York, New York
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14
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Wethey DS, Brin LD, Helmuth B, Mislan K. Predicting intertidal organism temperatures with modified land surface models. Ecol Modell 2011. [DOI: 10.1016/j.ecolmodel.2011.08.019] [Citation(s) in RCA: 38] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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