Finn, John

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Professor, Department of Environmental Conservation
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Environmental Sciences
Dr. Finn is the department’s systems ecologist. He received his Ph.D. from the School of Ecology at the University of Georgia, did a post-doc at the Ecosystems Center, Marine Biological Laboratory, Woods Hole, MA, and trained in satellite remote sensing at the Goddard Space Flight Center of NASA. His interests include development of modeling and analysis techniques, statistical methods for ecological data, GIS, remote sensing and application of modeling and theory to environmental assessment and resource management.

Search Results

Now showing 1 - 7 of 7
  • Publication
    Anthropogenic Ignitions
    (2016-01-01) Fusco, Emily J.; Abatzoglou, John T.; Balch, Jennifer K; Finn, John T; Bradley, Bethany
    This dataset contains ignition points derived from the MODIS Burned Area Product (MCD45) from 2000-2012), It also contains a random subset of unburned points. Both ignition and unburned points have associated anthropogenic feature data.
  • Publication
    Ignition Cause
    (2016-01-01) Fusco, Emily J.; Abatzoglou, John T.; Balch, Jennifer K.; Finn, John T; Bradley, Bethany
    This dataset contains ignition points derived from the MODIS Burned Area Product (MCD45) from 2000-2012), It also contains the determined cause for each ignition.
  • Publication
    Northeast Invasive Plants Data
    (2017-01-01) Cross, Tyler; Finn, John T; Bradley, Bethany
    The data are distribution and ranked abundance data for thirteen invasive plants in the Northeast US compiled from various spatial repositories for invasive species. iMAP invasives data are not included in this dataset because they are not publicly available. iMAP data can be requested from individual states. These data form the basis of analyses presented in Cross et al. 2017. "Frequency of invasive plant occurrence is not a suitable proxy for abundance in the Northeast US Ecosphere".
  • Publication
    Frequency of invasive plant occurrence is not a suitable proxy for abundance in the Northeast United States
    (2017-01-01) Cross, Tyler; Finn, John T; Bradley, Bethany A
    Measuring and predicting invasive plant abundance is critical for understanding impacts on ecosystems and economies. Although spatial abundance datasets remain rare, occurrence datasets are increasingly available across broad regional scales. We asked whether the frequency of these point occurrences can be used as a proxy for abundance of invasive plants. We compiled both occurrence and abundance data for 13 regionally important invasive plants in the northeast United States from herbarium records and several contributed distribution datasets. We integrated all available abundance information based on infested area, stem count, percent cover, or qualitative descriptions into abundance rankings ranging from 0 (absent) to 4 (highly abundant). Within equal-area grid cells of 800 m, we counted numbers of occurrence points and used ordinal regression to test whether higher densities of occurrence points increased the odds of a higher abundance ranking. We compiled a total of 86,854 occurrence points in 34,596 grid cells, of which 26,114 points (30%) within 11,976 cells (35%) had some form of abundance information. Eleven of the 13 species had a slight but significantly positive odds ratio; that is, more occurrence points of a species increased the odds that the species was abundant within the grid cell. However, the predictive ability of the models was poor (κ < 0.2) for the majority of species. Additionally, most grid cells contained only one or two occurrence points, making it impossible to infer abundance in all but a few locations. These results suggest that currently available occurrence datasets do not effectively represent abundance, which could explain why many distribution models based on occurrence data are poor predictors of abundance. Increased efforts to consistently collect and report invasive species abundance, ideally estimating both infested area and average cover, are strongly needed for regional-scale assessments of potential abundance and associated impact.
  • Publication
    Movements, connectivity, and space use of immature green turtles within coastal habitats of the Culebra Archipelago, Puerto Rico: implications for conservation
    (2019-01-01) Griffin, Lucas; Finn, John T; Diez, Carlos; Danylchuk, Andy J.
    Juvenile green turtles occupy coastal marine habitats important for their ontogeny; however, the details of their movement, connectivity, and space use in these developmental habitats are still poorly understood. Given that these areas are often threatened by human disturbance, additional information on green turtle spatial ecology is needed to meet conservation end- points for this endangered species. For this study, we used fixed passive acoustic telemetry to (1) describe movement patterns and connectivity of immature green turtles within, outside, and across 2 bays, Manglar and Tortuga bays, on Culebra and Culebrita islands, Puerto Rico; and (2) determine spatio-temporal drivers of the presence and absence of turtles within Manglar Bay. Network analysis used to quantify movement patterns showed that turtles in our study exhibited differential space use with little to no connectivity across the 2 bays. In addition, turtles exhibited high site fidelity, with larger turtles leaving on brief trips. We applied a presence−absence Bayesian binomial model on a subset of 9 turtles at an hourly temporal scale and showed that turtles within Manglar Bay occupied areas of lagoon and seagrass habitats at night and were rarely using areas of macroalgae habitat. The parameter estimates from the model enabled us to predict the space use of turtles across Manglar Bay, and the hourly probability distributions highlighted predictive diel movement patterns across the bay. Considering the importance of juvenile and subadult life stages for population viability, we recommend continued protection of these critical juvenile turtle developmental habitats to ensure recruitment into the adult life stage.
  • Publication
    Biotic resistance to invasion is ubiquitous across ecosystems of the United States
    (2019-01-01) Beaury, Evelyn M.; Finn, John T; Corbin, Jeffrey D.; Barr, Valerie; Bradley, Bethany A
    The biotic resistance hypothesis predicts that diverse native communities are more resistant to invasion. However, past studies vary in their support for this hypothesis due to an apparent contradiction between experimental studies, which support biotic resistance, and observational studies, which find that native and non-native species richness are positively related at broad scales (small scale studies are more variable). Here, we present a novel analysis of the biotic resistance hypothesis using 24,456 observations of plant richness spanning four community types and seven ecoregions of the United States. Non-native plant occurrence was negatively related to native plant richness across all community types and ecoregions, although the strength of biotic resistance varied across different ecological, anthropogenic, and climatic contexts. Our results strongly support the biotic resistance hypothesis, thus reconciling differences between experimental and observational studies and providing evidence for the shared benefits between invasive species management and native biodiversity conservation.
  • Publication
    Lights, Camera...Citizen Science: Assessing the Effectiveness of Smartphone-based Video Training in Invasive Plant Identification dataset
    (2014-01-01) Starr, Jared; Schweik, Charles M; Bush, Nathan; Fletcher, Lena; Finn, John T; Fish, Jennifer; Bargeron, Charles T.
    The rapid growth and increasing popularity of smartphone technology is putting sophisticated data-collection tools in the hands of more and more citizens. This has exciting implications for the expanding field of citizen science. With smartphone-based applications (apps), it is now increasingly practical to remotely acquire high quality citizen-submitted data at a fraction of the cost of a traditional study. Yet, one impediment to citizen science projects is the question of how to train participants. The traditional “in-person” training model, while effective, can be cost prohibitive as the spatial scale of a project increases. To explore possible solutions, we analyze three training models: 1) in-person, 2) app-based video, and 3) app-based text/images in the context of invasive plant identification in Massachusetts. Encouragingly, we find that participants who received video training were as successful at invasive plant identification as those trained in-person, while those receiving just text/images were less successful. This finding has implications for a variety of citizen science projects that need alternative methods to effectively train participants when in-person training is impractical. This file is the raw data that accompanies the PLoS article.