McCune, Jenny L.

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Now showing 1 - 5 of 8
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    Using stacked SDMs with accuracy and rarity weighting to optimize surveys for rare plant species
    (Springer, 2020) Rosner-Katz, Hanna; McCune, Jenny L.; Bennett, Joseph R.
    Effective conservation of rare species requires reasonable knowledge of population locations. However, surveys for rare species can be time-intensive and therefore expensive. We test a methodology using stacked species distribution models (S-SDMs) to efficiently discover the greatest number of new rare species’ occurrences possible. We used S-SDMs for 22 rare plant species in southern Ontario, Canada to predict the best survey locations among individual 1-ha cells. For each cell, we weighted distribution model outputs by accuracy and species rarity to create an efficiency value. We used these efficiency values as an index to determine the locations of our field surveys. We conducted field surveys in multi-species cells, “MSC” (areas with high predicted efficiency for multiple species) and single species cells, “SSC” (areas with high probability for only one species) to determine the relative efficiency of a multi-species survey approach. MSC were more than twice as likely as SSC to have at least one rare plant species discovered. Efficiency ranks were also useful in directing surveyors toward incidental discoveries of other rare species that were not modeled. Our technique of using S-SDMs can help direct surveys to more efficiently find rare species occurrences.
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    A new record of Stylophorum diphyllum (Michx.) Nutt. in Canada: a case study of the value and limitations of building species distribution models for very rare plants
    (BioOne, 2019) McCune, Jenny L.
    Stylophorum diphyllum (Michx.) Nutt. is an endangered plant of rich floodplain forests in southern Ontario, Canada. Prior to 2015 there were only four known populations in Ontario. I built a species distribution model (SDM) based on the known occurrences, and tested it by surveying 156 forest sites that varied in their predicted suitability. An indicator species analysis showed that sites predicted to be suitable had significantly higher frequency and abundance of common species usually associated with S. diphyllum, demonstrating the ability of the SDM to pinpoint similar habitat, although none of these sites contained S. diphyllum. The most important predictors used by the SDM to determine habitat suitability were growing season precipitation, surficial geology, and soil texture. I discovered a new population of S. diphyllum more than 50 km north of the known populations, at one of the sites not predicted to be suitable. This demonstrates a clear example of SDM overfitting, which may occur when models are built based on few, spatially limited occurrence records. Nonetheless, the key environmental predictors remained the same in an updated SDM including the new record. Stylophorum diphyllum provides a case study of both the value and the limitations of using SDMs to predict suitable habitat for very rare and geographically restricted plants, and the need for more rare plant surveys even in human-dominated landscapes.
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    Species distribution models rarely predict the biology of real populations
    (Wiley, 2022) Lee-Yaw, Julie A.; McCune, Jenny L.; Pironon, Samuel; Sheth, Seema N.
    Species distribution models (SDMs) are widely used in ecology. In theory, SDMs capture (at least part of) species' ecological niches and can be used to make inferences about the distribution of suitable habitat for species of interest. Because habitat suitability is expected to influence population demography, SDMs have been used to estimate a variety of population parameters, from occurrence to genetic diversity. However, a critical look at the ability of SDMs to predict independent data across different aspects of population biology is lacking. Here, we systematically reviewed the literature, retrieving 201 studies that tested predictions from SDMs against independent assessments of occurrence, abundance, population performance, and genetic diversity. Although there is some support for the ability of SDMs to predict occurrence (~53% of studies depending on how support was assessed), the predictive performance of these models declines progressively from occurrence to abundance, to population mean fitness, to genetic diversity. At the same time, we observed higher success among studies that evaluated performance for single versus multiple species, pointing to a possible publication bias. Thus, the limited accuracy of SDMs reported here may reflect the best-case scenario. We discuss the limitations of these models and provide specific recommendations for their use for different applications going forward. However, we emphasize that predictions from SDMs, especially when used to inform conservation decisions, should be treated as hypotheses to be tested with independent data rather than as stand-ins for the population parameters we seek to know.
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    Do traits of plant species predict the efficacy of species distribution models for finding new occurences?
    (Wiley, 2020) McCune, Jenny L.; Rosner-Katz, Hanna; Bennett, Joseph R.; Schuster, Richard; Kharouba, Heather M.
    Species distribution models (SDMs) are used to test ecological theory and to direct targeted surveys for species of conservation concern. Several studies have tested for an influence of species traits on the predictive accuracy of SDMs. However, most used the same set of environmental predictors for all species and/or did not use truly independent data to test SDM accuracy. We built eight SDMs for each of 24 plant species of conservation concern, varying the environmental predictors included in each SDM version. We then measured the accuracy of each SDM using independent presence and absence data to calculate area under the receiver operating characteristic curve (AUC) and true positive rate (TPR). We used generalized linear mixed models to test for a relationship between species traits and SDM accuracy, while accounting for variation in SDM performance that might be introduced by different predictor sets. All traits affected one or both SDM accuracy measures. Species with lighter seeds, animal-dispersed seeds, and a higher density of occurrences had higher AUC and TPR than other species, all else being equal. Long-lived woody species had higher AUC than herbaceous species, but lower TPR. These results support the hypothesis that the strength of species–environment correlations is affected by characteristics of species or their geographic distributions. However, because each species has multiple traits, and because AUC and TPR can be affected differently, there is no straightforward way to determine a priori which species will yield useful SDMs based on their traits. Most species yielded at least one useful SDM. Therefore, it is worthwhile to build and test SDMs for the purpose of finding new populations of plant species of conservation concern, regardless of these species’ traits.
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    Context-dependent interactions and the regulation of species richness in freshwater fish
    (Nature Publishing Group, 2018) MacDougall, Andrew S.; Harvey, Eric; McCune, Jenny L.; Nilsson, Karin A.; Bennett, Joseph; Firn, Jennifer; Bartley, Timothy; Grace, James B.; Kelly, Jocelyn; Tunney, Tyler D.; McMeans, Bailey; Matsuzaki, Shin-Ichiro S.; Kadoya, Taku; Esch, Ellen; Cazelles, Kevin; Lester, Nigel; McCann, Kevin S.
    Species richness is regulated by a complex network of scale-dependent processes. This complexity can obscure the influence of limiting species interactions, making it difficult to determine if abiotic or biotic drivers are more predominant regulators of richness. Using integrative modeling of freshwater fish richness from 721 lakes along an 11o latitudinal gradient, we find negative interactions to be a relatively minor independent predictor of species richness in lakes despite the widespread presence of predators. Instead, interaction effects, when detectable among major functional groups and 231 species pairs, were strong, often positive, but contextually dependent on environment. These results are consistent with the idea that negative interactions internally structure lake communities but do not consistently ‘scale-up’ to regulate richness independently of the environment. The importance of environment for interaction outcomes and its role in the regulation of species richness highlights the potential sensitivity of fish communities to the environmental changes affecting lakes globally.