Recommendations for quantifying and reducing uncertainty in climate projections of species distributions

Brodie, Stephanie; Smith, James A.; Muhling, Barbara A.; Barnett, Lewis A. K.; Carroll, Gemma; Fiedler, Paul C.; Bograd, Steven J.; Hazen, Elliott L.; Jacox, Michael G.; Andrews, Kelly S.; Barnes, Cheryl L.; Crozier, Lisa G.; Fiechter, Jérôme; Fredston, Alexa; Haltuch, Melissa A.; Harvey, Chris J.; Holmes, Elizabeth E.; Karp, Melissa A.; Liu, Owen R.; Malick, Michael J.; Buil, Mercedes Pozo; Richerson, Kate; Rooper, Christopher N.; Samhouri, Jameal F.; Seary, Rachel; Selden, Rebecca L.; Thompson, Andrew R.; Tommasi, Désirée; Ward, Eric J.; Kaplan, Isaac C.

doi:10.1111/gcb.16371

Cited by 32 publications

(35 citation statements)

References 70 publications

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“…In our simulation, we used the HadGEM2-ES ESM, which exhibits some of the fastest warming and productivity declines for the CCS (Pozo Buil et al, 2021) and thus higher novelty (Smith et al, 2022). Thus, while in our study SDM projection performance began to degrade mid-century for many scenarios, other studies may see performance degrade earlier or later depending on which ESM is used (Brodie et al, 2022).…”

Section: Differences Among Sampling Scenariosmentioning

confidence: 86%

“…This suggests that SDMs will likely show degrading performance over time given high climatic novelty in future periods, although random sampling can help mitigate this (Figure 6; Table 3). We note that the amount of extrapolation into the future, and thus the impact on model predictive skill, varies among climate models (see Brodie et al, 2022) and scenarios. In our simulation, we used the HadGEM2-ES ESM, which exhibits some of the fastest warming and productivity declines for the CCS (Pozo Buil et al, 2021) and thus higher novelty (Smith et al, 2022).…”

Section: Differences Among Sampling Scenariosmentioning

confidence: 99%

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Projecting species distributions using fishery‐dependent data

et al. 2022

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Many marine species are shifting their distributions in response to changing ocean conditions, posing significant challenges and risks for fisheries management. Species distribution models (SDMs) are used to project future species distributions in the face of a changing climate. Information to fit SDMs generally comes from two main sources: fishery‐independent (scientific surveys) and fishery‐dependent (commercial catch) data. A concern with fishery‐dependent data is that fishing locations are not independent of the underlying species abundance, potentially biasing predictions of species distributions. However, resources for fishery‐independent surveys are increasingly limited; therefore, it is critical we understand the strengths and limitations of SDMs developed from fishery‐dependent data. We used a simulation approach to evaluate the potential for fishery‐dependent data to inform SDMs and abundance estimates and quantify the bias resulting from different fishery‐dependent sampling scenarios in the California Current System (CCS). We then evaluated the ability of the SDMs to project changes in the spatial distribution of species over time and compare the time scale over which model performance degrades between the different sampling scenarios and as a function of climate bias and novelty. Our results show that data generated from fishery‐dependent sampling can still result in SDMs with high predictive skill several decades into the future, given specific forms of preferential sampling which result in low climate bias and novelty. Therefore, fishery‐dependent data may be able to supplement information from surveys that are reduced or eliminated for budgetary reasons to project species distributions into the future.

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Section: Differences Among Sampling Scenariosmentioning

confidence: 86%

Section: Differences Among Sampling Scenariosmentioning

confidence: 99%

Projecting species distributions using fishery‐dependent data

et al. 2022

Self Cite

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“…For SDM projections to be used appropriately in science-based decision-making, it is imperative that the results and associated uncertainty are communicated effectively to both technical and non-technical audiences (Baron 2010, Corner et al 2018, Raimi et al 2017). In the context of the changing ocean, where ideal marine management decisions achieve objectives both now and in the future, the clear communication of results aids in reducing misinterpretation or dismissal of important findings (Brodie et al 2022). Involving end users throughout the development of SDM projections, from developing the study objectives to communication of SDM outputs, will enhance mutual understanding (Dietz 2013, Guillera-Arroita et al 2015, Villero et al 2017.…”

Section: Communicate the Results And Uncertaintiesmentioning

confidence: 99%

“…In addition, while marine species are better at tracking climate shifts poleward than terrestrial species (Lenoir et al 2020), human extractive activities (i.e., fishing) are also shifting poleward, making it difficult to disentangle the different pressures (Pinsky and Fogarty 2012). In light of these challenges, SDM predictions have been successfully used to support various marine resource management initiatives including conservation planning, fisheries management, risk assessments, marine spatial planning, and emergency response initiatives (Baker et al 2021, Sofaer et al 2019, Young and Carr 2015, and are a valuable tool to project the distributions of marine species (Brodie et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Addressing uncertainty when projecting marine species distributions under climate change

Davies

Thompson

Gómez

et al. 2022

Preprint

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Species distribution models (SDMs) have been widely used to project terrestrial species’ responses to climate change and are increasingly being used for similar objectives in the marine realm. These projections are critically needed to develop strategies for resource management and the conservation of marine ecosystems. SDMs are a powerful and necessary tool; however, they are subject to many sources of uncertainty. To ensure that SDM projections are informative for management and conservation decisions, sources of uncertainty must be considered and properly addressed. Here we provide ten overarching guidelines that will aid researchers to identify, minimize, and account for uncertainty through the entire model development process, from the formation of a study question to the presentation of results. These guidelines were developed at an international workshop attended by over 50 researchers and practitioners. Although our guidelines are broadly applicable across biological realms, we provide particular focus to the challenges and uncertainties associated with projecting the impacts of climate change on marine species and ecosystems.

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“…Thus, an exhaustive evaluation of several resampling methods [ 129 , 130 , 131 ] could provide new best practices for a correct evaluation of future potential distributions. Finally, major improvements for enhancing temporal projection could be achieved by developing proper methods to quantify and evaluate the uncertainty associated with prediction [ 12 , 132 , 133 ], a topic not addressed in this study.…”

Section: Discussionmentioning

confidence: 99%

Assessing the Effectiveness of Correlative Ecological Niche Model Temporal Projection through Floristic Data

Dolci

Peruzzi

2022

Biology

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Correlative ecological niche modelling (ENM) is a method widely used to study the geographic distribution of species. In recent decades, it has become a leading approach for evaluating the most likely impacts of changing climate. When used to predict future distributions, ENM applications involve transferring models calibrated with modern environmental data to future conditions, usually derived from Global Climate Models (GCMs). The number of algorithms and software packages available to estimate distributions is quite high. To experimentally assess the effectiveness of correlative ENM temporal projection, we evaluated the transferability of models produced using 12 different algorithms on historical and modern data. In particular, we compared predictions generated using historical data and projected to the modern climate (simulating a “future” condition) with predictions generated using modern distribution and climate data. The models produced with the 12 ENM algorithms were evaluated in geographic (range size and coherence of predictions) and environmental space (Schoener’s D index). None of the algorithms shows an overall superior capability to correctly predict future distributions. On the contrary, a few algorithms revealed an inadequate predictive ability. Finally, we provide hints that can be used as guideline to plan further studies based on the adopted general workflow, useful for all studies involving future projections.

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Recommendations for quantifying and reducing uncertainty in climate projections of species distributions

Cited by 32 publications

References 70 publications

Projecting species distributions using fishery‐dependent data

Projecting species distributions using fishery‐dependent data

Addressing uncertainty when projecting marine species distributions under climate change

Assessing the Effectiveness of Correlative Ecological Niche Model Temporal Projection through Floristic Data

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