McCune, Jenny L.

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

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    Comparing the effect of landscape context on vascular plant and bryophyte communities in a human-dominated landscape
    (Wiley, 2020) McCune, Jenny L.; Frendo, Christina J.; Ramadan, Mohammed; Baldwin, Lyn K.
    Aims: It is important to understand the effect of landscape context on biological communities to predict how biodiversity will be affected on human-dominated land-scapes. While many studies have tested the effects of landscape context on the spe-cies richness and composition of vascular plants, few have compared the responses of vascular plants and bryophytes on the same landscape. We sampled non-epiphytic bryophytes and vascular plants in 184 plots to test whether three landscape context factors measured four years or four decades previously could predict bryophyte or vascular plant species richness and composition after accounting for local factors.Location: Temperate forests and oak savannahs, Vancouver Island, British Columbia, Canada.Methods: We used model selection and comparisons to test the effects of surround-ing road density, total amount of forest, and distance to the nearest forest edge on species richness, species richness of non-disturbance-associated species, and com-munity composition after controlling for important local predictors including sub-strate availability and topography.Results: The species richness of non-disturbance-associated vascular plants was lower in plots with greater surrounding historical road density, and perennial stayer bryophyte richness declined with increasing historical road density and lower histori-cal forest amount, suggesting a potential extinction debt. Landscape context signifi-cantly affected total species richness and community composition of vascular plants, but not bryophytes.Conclusion: While bryophytes appear to be less sensitive overall to landscape con-text than vascular plants, disturbance-intolerant perennial stayer bryophytes may decline in the future in response to the increased road density and loss of forest cover that has occurred over the past four decades.
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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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    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.