Mechanistic species distribution modelling as a link between physiology and conservation

Evans, Tyler G.; Diamond, Sarah E.; Kelly, Morgan W.

doi:10.1093/conphys/cov056

Cited by 138 publications

(122 citation statements)

References 119 publications

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“…[15,18,20,21]). These concerns have prompted proposals to use alternative estimates of climatic tolerance in lieu of distribution data when parameterizing spatial models [24,25,38], assuming that expert knowledge or lab-based measurements will provide a broader approximation of climatic tolerance. However, our results show that distribution data in the US describe a consistently broader climatic niche than climatic tolerance estimates available for over 1800 plants (Fig 1).…”

Section: Discussionmentioning

confidence: 99%

Plant Distribution Data Show Broader Climatic Limits than Expert-Based Climatic Tolerance Estimates

Curtis

Bradley

2016

PLoS ONE

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BackgroundAlthough increasingly sophisticated environmental measures are being applied to species distributions models, the focus remains on using climatic data to provide estimates of habitat suitability. Climatic tolerance estimates based on expert knowledge are available for a wide range of plants via the USDA PLANTS database. We aim to test how climatic tolerance inferred from plant distribution records relates to tolerance estimated by experts. Further, we use this information to identify circumstances when species distributions are more likely to approximate climatic tolerance.MethodsWe compiled expert knowledge estimates of minimum and maximum precipitation and minimum temperature tolerance for over 1800 conservation plant species from the ‘plant characteristics’ information in the USDA PLANTS database. We derived climatic tolerance from distribution data downloaded from the Global Biodiversity and Information Facility (GBIF) and corresponding climate from WorldClim. We compared expert-derived climatic tolerance to empirical estimates to find the difference between their inferred climate niches (ΔCN), and tested whether ΔCN was influenced by growth form or range size.ResultsClimate niches calculated from distribution data were significantly broader than expert-based tolerance estimates (Mann-Whitney p values << 0.001). The average plant could tolerate 24 mm lower minimum precipitation, 14 mm higher maximum precipitation, and 7° C lower minimum temperatures based on distribution data relative to expert-based tolerance estimates. Species with larger ranges had greater ΔCN for minimum precipitation and minimum temperature. For maximum precipitation and minimum temperature, forbs and grasses tended to have larger ΔCN while grasses and trees had larger ΔCN for minimum precipitation.ConclusionOur results show that distribution data are consistently broader than USDA PLANTS experts’ knowledge and likely provide more robust estimates of climatic tolerance, especially for widespread forbs and grasses. These findings suggest that widely available expert-based climatic tolerance estimates underrepresent species’ fundamental niche and likely fail to capture the realized niche.

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Section: Discussionmentioning

confidence: 99%

Plant Distribution Data Show Broader Climatic Limits than Expert-Based Climatic Tolerance Estimates

Curtis

Bradley

2016

PLoS ONE

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“…By contrast, mechanistic or process-based models are built around explicit descriptions of biological mechanisms and parameters that have a clear a priori interpretation. If formulated appropriately (e.g., experiment-based parameterizations of species' responses to environmental conditions [62,88]), some mechanistic models can be expected to achieve greater realism, with potential for higher transferability [88]. However, mechanistic models suffer from the same issues of nonstationarity as correlative models, and are thus not immune to potentially inaccurate extrapolation (Box 2).…”

Section: Box 3 Correlative Versus Mechanistic Modelsmentioning

confidence: 99%

Outstanding Challenges in the Transferability of Ecological Models

Yates

Bouchet

Caley

et al. 2018

Trends in Ecology & Evolution

478

456

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Predictive models are central to many scientific disciplines and vital for informing management in a rapidly changing world. However, limited understanding of the accuracy and precision of models transferred to novel conditions (their 'transferability') undermines confidence in their predictions. Here, 50 experts identified priority knowledge gaps which, if filled, will most improve model transfers. These are summarized into six technical and six fundamental challenges, which underlie the combined need to intensify research on the determinants of ecological predictability, including species traits and data quality, and develop best practices for transferring models. Of high importance is the identification of a widely applicable set of transferability metrics, with appropriate tools to quantify the sources and impacts of prediction uncertainty under novel conditions. Predicting the UnknownPredictions facilitate the formulation of quantitative, testable hypotheses that can be refined and validated empirically [1]. Predictive models have thus become ubiquitous in numerous scientific disciplines, including ecology [2], where they provide means for mapping species distributions, explaining population trends, or quantifying the risks of biological invasions and disease outbreaks (e.g., [3,4]). The practical value of predictive models in supporting policy and decision making has therefore grown rapidly (Box 1) [5]. With that has come an increasing desire to predict (see Glossary) the state of ecological features (e.g., species, habitats) and our likely impacts upon them [5], prompting a shift from explanatory models to anticipatory predictions [2]. However, in many situations, severe data deficiencies preclude the development of specific models, and the collection of new data can be prohibitively costly or simply impossible [6]. It is in this context that interest in transferable models (i.e., those that can be legitimately projected beyond the spatial and temporal bounds of their underlying data [7]) has grown.Transferred models must balance the tradeoff between estimation and prediction bias and variance (homogenization versus nontransferability, sensu [8]). Ultimately, models that can Highlights Models transferred to novel conditions could provide predictions in data-poor scenarios, contributing to more informed management decisions.The determinants of ecological predictability are, however, still insufficiently understood.Predictions from transferred ecological models are affected by species' traits, sampling biases, biotic interactions, nonstationarity, and the degree of environmental dissimilarity between reference and target systems.We synthesize six technical and six fundamental challenges that, if resolved, will catalyze practical and conceptual advances in model transfers.We propose that the most immediate obstacle to improving understanding lies in the absence of a widely applicable set of metrics for assessing transferability, and that encouraging the development of models grounded in well-established mech...

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“…Coastal alteration projects such as beach nourishment and restoration activities, as well as dredge and fill operations, pose threats to fish and wildlife due to general habitat disturbances, increased suspended sediments in the water column, lower dissolved oxygen, and direct mortality to some species. Time of year (TOY) restrictions on in‐water work are routinely used to protect critical life stages by establishing periods during which species use spatially constrained habitats or are particularly vulnerable to stress (Evans, Diamond, & Kelly, ). TOY restrictions are typically established based on average conditions, with some room for annual variation.…”

Section: Implications Of Changing Phenologymentioning

confidence: 99%

“…However, during recent years, alewife were observed migrating upriver as early as late February–early March (Annual Summaries from Massachusetts Division of Marine Fisheries, Association to Preserve Cape Cod (APCC), and Massachusetts Bays National Estuary Program (MassBays)). If shifts are occurring in these systems, regulatory agencies may need to reconsider the duration of TOY windows, increase monitoring to support in‐season modification during extreme years, and/or use precautionary temporal buffers that allow for greater variation in phenology (Evans et al, ). Similar considerations will also apply to hydropower and water drawdown operations that affect seasonal flows and aquatic‐marine habitat connectivity.…”

Section: Implications Of Changing Phenologymentioning

confidence: 99%

It’s about time: A synthesis of changing phenology in the Gulf of Maine ecosystem

Staudinger

Mills

Stamieszkin

et al. 2019

Fisheries Oceanography

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The timing of recurring biological and seasonal environmental events is changing on a global scale relative to temperature and other climate drivers. This study considers the Gulf of Maine ecosystem, a region of high social and ecological importance in the Northwest Atlantic Ocean and synthesizes current knowledge of (a) key seasonal processes, patterns, and events; (b) direct evidence for shifts in timing; (c) implications of phenological responses for linked ecological‐human systems; and (d) potential phenology‐focused adaptation strategies and actions. Twenty studies demonstrated shifts in timing of regional marine organisms and seasonal environmental events. The most common response was earlier timing, observed in spring onset, spring and winter hydrology, zooplankton abundance, occurrence of several larval fishes, and diadromous fish migrations. Later timing was documented for fall onset, reproduction and fledging in Atlantic puffins, spring and fall phytoplankton blooms, and occurrence of additional larval fishes. Changes in event duration generally increased and were detected in zooplankton peak abundance, early life history periods of macro‐invertebrates, and lobster fishery landings. Reduced duration was observed in winter–spring ice‐affected stream flows. Two studies projected phenological changes, both finding diapause duration would decrease in zooplankton under future climate scenarios. Phenological responses were species‐specific and varied depending on the environmental driver, spatial, and temporal scales evaluated. Overall, a wide range of baseline phenology and relevant modeling studies exist, yet surprisingly few document long‐term shifts. Results reveal a need for increased emphasis on phenological shifts in the Gulf of Maine and identify opportunities for future research and consideration of phenological changes in adaptation efforts.

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Mechanistic species distribution modelling as a link between physiology and conservation

Cited by 138 publications

References 119 publications

Plant Distribution Data Show Broader Climatic Limits than Expert-Based Climatic Tolerance Estimates

Plant Distribution Data Show Broader Climatic Limits than Expert-Based Climatic Tolerance Estimates

Outstanding Challenges in the Transferability of Ecological Models

It’s about time: A synthesis of changing phenology in the Gulf of Maine ecosystem

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