McCune, Jenny L.

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https://opus.uleth.ca/handle/10133/6392

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Browsing McCune, Jenny L. by Author "Bennett, Joseph R."

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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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    The influence of landscape context on short- and long-term forest change following a severe ice storm
    (Wiley, 2019) Lloren, Jed I.; Fahrig, Lenore; Bennett, Joseph R.; Contreras, Thomas A.; McCune, Jenny L.
    1. When deforestation results in small forest fragments surrounded by a non‐forest matrix, forest stands within these fragments experience changes in structure and community composition. They also continue to experience natural disturbances like hurricanes and ice storms. It remains unclear whether the landscape context of forest stands influences plant community response to natural disturbances.2. Using data from surveys of forested plots in the years immediately following and 19 years after a severe ice storm, we measured changes in woody stem density, species richness and beta diversity.3. Plots with greater storm damage had greater gains in stems and species, and greater shifts in community composition. In addition, there were interactions between the degree of storm damage and landscape context. The short‐term ef‐fects of storm damage were magnified in plots with less forest on the surrounding landscape and farther from the forest edge. In plots with high damage, a return towards pre‐storm conditions over the long‐term occurred more often in plots farther from the forest edge compared to those close to the edge.4. Synthesis. Future climate scenarios predict increases in severe weather and accompanying ecosystem disturbance. Our results show that it is important to consider landscape context when assessing the response of forest communities to such disturbances.
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    The state of plant conservation in Canada: a survey of practitioners
    (Canadian Science Publishing, 2024) McCune, Jenny L.; Baldwin, Sarah J.; Bennett, Joseph R.; Husband, Brian C.; Joly, Simon; Kraus, Daniel; Lamb, Eric G.; Vamosi, Jana C.; Van Natto, Alyson C.; Whitton, Jeannette
    Plants make up more than one quarter of all species listed under Canada’s Species at Risk Act, but very few have improved in status over time. Ineffective legal protections, lack of public awareness, difficulties in prioritizing species, and a scarcity of research relevant to the recovery of plant species at risk are some of the many challenges facing effective plant conservation in Canada. We used an online survey to ask 243 people who work in plant conservation or who do research in plant ecology or evolution to assess the state of plant conservation in Canada and to identify the actions needed to improve it. Most respondents agreed that Canada is underperforming or merely average when it comes to conserving plants. Based on their responses, we outline a set of recommendations that could form the basis of a national strategy for plant conservation in Canada. These include greater advocacy for habitat protection, connecting researchers with funding opportunities, supporting graduate students working on research related to plant conservation, increasing public awareness of plants, collaborating with and respecting Indigenous knowledge holders, promoting collaboration between researchers and local conservation groups, and increasing capacity to assess the status of species that are potentially at risk.
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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.