Special Poster Session: GIS in Wildlife Ecology


Organizers: Jeff Jenness, Spring Stewardship Institute, Museum of Northern Arizona; Jenness Enterprises; Northern Arizona University

Supported by: TWS Biological Diversity and Drone Working Groups

GIS and spatial analysis is becoming more and more integral to wildlife management and research.  The field is mature enough to have produced powerful methods and tools, but young enough that we are constantly finding new ways to apply those tools.  This is a golden age for spatial ecology and we are right on the cutting edge.  This poster session will illustrate innovative and exciting examples of GIS and spatial analysis applied to wildlife research and management.

Connectivity Metrics for Conservation Planning and Monitoring
Paul Beier, Jeff Jenness, Annika Keeley
Ecological connectivity is key to conserving biodiversity, but currently just 10% of terrestrial protected areas are structurally connected. Metrics of the connectivity of a focal core area, an existing ecological network, or an entire ecoscape can help specify desired outcomes and conservation actions, monitor change over time, and prioritize areas for inclusion in a network. We summarized 35 metrics and sorted them along a spectrum from fully structural to fully functional. We also constructed a decision tree for selecting the most appropriate connectivity metrics for a study or project using three factors: (1) the extent of human modification of the focal landscape, (2) which of four conservation objectives is to be assessed, and (3) the type of connectivity (structural, functional, or both) to be measured. When conservation is focused on a particular species, or if data are available to parameterize models for a suite of species, functional connectivity metrics may be preferred. Under the impending threats of climate change, landscapes need to facilitate movements of all species who need to shift their ranges to adapt. Because an intact ecological network may support many species’ movement, structural metrics that consider the human footprint should be used in approximations of functional connectivity in shared landscapes. Selecting a metric means deciding what aspects of reality to consider or ignore. This review can help practitioners converge on a small suite of useful connectivity metrics that will lead to better efforts to conserve and restore connectivity and measure progress toward connectivity conservation goals.
Spatiotemporal Patterns Of Pronghorn In The Texas Panhandle
Dakota Moberg, Shawn Gray, Humberto Perotto-Baldivieso, Victoria Cavazos, Timothy Fulbright, Randy W. DeYoung, David G. Hewitt, Warren Conway
Pronghorn (Antilocapra americana) are found in 27 counties of the Texas Panhandle. Their behavior is heavily influenced by forage availability and disturbances in the landscape. However, there is very little information on the spatial and temporal distribution of pronghorn in the panhandle. The goal of this project is to compare the spatiotemporal distribution of pronghorn in two contrasting landscapes in the Texas Panhandle: Pampa, an agriculture dominated landscape, and Dalhart, a rangeland dominated landscape. We used GPS data collected from 64 pronghorn (32 males and 32 females) from 2016 to 2019 evenly distributed across Pampa and Dalhart. We created space-time cubes for each individual pronghorn using a monthly step time. The space-time cube is a representation that helps assess spatiotemporal patterns across the landscape. This cube can provide information on whether wildlife movement has distinctive patterns across the landscape through time. Combined with land cover data, the space-time cubes can provide new insights into how species use the landscape throughout the year. We classified the resulting patterns as emerging hot spots, emerging cold spots, or no-patterns areas. We are currently classifying these patterns according to land cover information in relation to agriculture and rangeland landscapes. Once we have identified specific times and areas of patterns within agricultural crops, we will identify the specific crop and growth stage within these areas using remote sensing imagery. The results of this study will help us understand the temporal dynamics of pronghorn spatial distribution.
Using published models to predict coyote abundance and formulate research questions: a cautionary tale with a twist.
Daniel Bogan
The coyote (Canis latrans) is a wild canine predator that historically inhabited the central plains of Canada and the United States. Following a period of extensive habitat loss and top-predator extirpation, coyotes expanded their species range and have inhabited New York State for approximately 80 years. Published studies have begun to build a scientific body of knowledge pertaining to coyote ecology, yet knowledge gaps regarding their evolving ecological role and community interactions remain. Therefore, I attempted to recreate a published spatial model of coyote abundance developed across northern New York (NY) and apply the model to the Rensselaer Plateau (RP), as both regions are similar in ecology (58 Northern Highlands ecoregion; Bryce et al. 2010). Specifically, my first objective was to cautiously evaluate the ability of the spatial model to predict the relative abundance of coyotes through ArcGIS modelling and field research. My second objective was to advance my research agenda by conducting this modelling exercise, as I expected it would produce more unanswered questions than it would answer. Shortly after initiating the modeling effort, I discovered that insufficient information was reported, key to recreating the exact model output and I fell short of my first objective. However, I used trial and error to successfully produce an approximation of the original spatial model and mapped the results across the Rensselaer Plateau as planned. This process did provide an opportunity to examine the study area and identify additional research opportunities. For example, the proxy model failed to produce abundance estimates for urbanized areas, pointing to modelling flaws or suggesting coyotes may respond to other variables within the urban matrix. Furthermore, this study “failed forward” by underscoring the need to report all information required to replicate study results, and build a quantitative scientific body of knowledge, and not simplified model descriptions.
Using ArcGIS StoryMaps to create a decision support tool for prioritizing coldwater stream habitat restoration and management
Patrick Landisch, Lisa Elliott, William J. Severud, Mark Nelson, Joseph Knight, Jody Vogeler
Spatiotemporal patterns of landscape characteristics interact with localized habitat characteristics to impact fish and wildlife populations and distributions. GIS and spatial analysis are integral tools for analyzing these impacts and account for broadscale and finescale patterns.  Such information can be used to guide resource management decisions. Communicating the relative importance and impacts of these is a critical step that is often overlooked by researchers. ArcGIS StoryMaps offer an integral tool to interactively support decisions. In this case study, we used a watershed framework to assess terrestrial characteristics across US Great Lakes watersheds at multiple spatial scales, both inside and outside riparian areas with the goal of guiding coldwater stream management in ways that account for both in-stream characteristics and spatiotemporal patterns of land use, land cover, and disturbance in the surrounding landscape. We obtained remotely sensed data from the 2016 National Land Cover Database, the Forest Inventory and Analysis database, Landsat time series-based forest canopy disturbance data (1974-2018), the Protected Areas Database of the United
Bats and habitat fragmentation in Nicaragua: A country-wide multi-scale landscape analysis
Carol Chambers, Jose Martinez-Fonseca, Ho Yi Wan, Erin Westeen, Arnulfo Medina-Fitoria, Octavio Saldaña, Samuel Cushman
Bats, the second most diverse order of mammals after rodents, perform a wide range of ecological roles in ecosystems in all continents except Antarctica. With 111 species, Nicaragua is one of the most bat-diverse countries per area in the world. The country also has one of the highest deforestation rates and hosts fragments of some of the most endangered ecosystems on the American continent. Effects of habitat fragmentation on bats have been widely studied in Europe, but few studies focus on landscapes with complex spatial configurations of vegetation types and high species richness. Historically, occurrence and basic ecological data for many rare bat species in the Neotropics are scarce or completely lacking due to difficult access to these sites and lack of funding for long-term data collection. With the collaboration of Nicaraguan and international colleagues, we compiled a dataset of 823 sampling sites and 24,000 bat records that include all species across the country. We also complied 15 new tree cover and land-use classes across the entire extent of Nicaragua at 60-m pixel resolution, and then used FRAGSTATS to calculate 16 landscape composition and configuration metrics across six spatial scales (150-m, 300-m, 600-m, 1,200-m, 2,400-m, and 4,800-m radii). These data are analyzed using random forest algorithms to elucidate non-linear responses, which are common in ecological processes. Our analysis determines thresholds of habitat fragmentation levels that affect the occurrence of different bat species such as forest specialists and habitat generalists. We also explore the influence of landscape variables on ecologically and phylogenetically similar bat groups. Findings of this study will inform management and conservation policies by providing a better understanding of how landscape configuration affects bat species and overall bat species richness not only in Nicaragua but Central America and the Neotropics.
Use of sUAS imagery for surveying waterfowl in a managed wetland in Colusa County
SHARON KAHARA, Kaitlyn Breana Hernandez, Buddhika Madurapperuma, Luke Scaroni, Judson Fisher, Katherine Marlin, James Lamping, Alex Pickering, Ariel Weisgrau
Recent advances in small unmanned aerial systems (sUAS) have facilitated monitoring and counting waterfowl using object based image analysis (OBIA) in remote sensing. The objective of the study is to use a semi-automated workflow to extract waterfowls from a managed wetland in Colusa County, California. Over 560 sUAS imagery was obtained using a DJI Mavic 2 PRO at an average Ground Sample Distance of 3 cm/px. Nine ground control points were placed across the study area and the coordinates were recorded using a Real-Time Kinematic GPS. An orthomosaic image was created using the Agisoft Photoscan software and the image was smoothed using a low pass filter to prevent over segmentation. Training points of waterfowl were manually created and then ENVI Segmentation only workflow was used to extract waterfowl objects, using the Edge algorithm at a scale of 75% and merge algorithm at a level of 95%. Two subsets of waterfowl present (6.8 ha) and waterfowl absent (1.4 ha) were used for OBIA. Rule-based feature extraction workflow in ENVI was used to classify two data subsets. The total automated waterfowl count was 2,259. The overall classification accuracy for identifying birds was 57.3%. The user’s accuracy for birds and non-birds was 93.9% and 51.5% and producer’s accuracy for birds and non-birds was 23.6% and 98.1% respectively. The greatest misclassification had visually similar grass patches in shallow water, or areas without birds. Conducting automated and manual counts in defined habitat may overcome the challenge.
Modeling 19 years of herbaceous cover, demography, and spatial patterns in northern Arizona’s ponderosa pine-bunchgrass ecosystems
H. Dowling, Judith Springer, David Huffman, Rachel Mitchell, Jeff Jenness, Margaret Moore, Daniel Laughlin, R. Strahan, J. Bakker, Sade Partridge
Understanding plant population responses to climate and land-use change is fundamental to conservation and habitat management. Mapping individual plants over time provides a precise method of determining demographic parameters, while modeling these data over time is the most realistic way to predict how species respond to changes in climate and land-use. While demographic parameters are known for many tree species, they are unknown for most herbaceous plants. Here we discuss a unique dataset of 98 permanent 1-m2 quadrats, located on ponderosa pine–bunchgrass ecosystems near Flagstaff, Arizona, USA.  The original chart quadrats were established between 1912 and 1927 to determine the effects of livestock grazing on herbaceous plants and pine seedlings, and today these data provide opportunities to examine the effects of climate and land-use variables on plant demography, population, and community processes.  Individual herbaceous plants in these quadrats were identified and mapped annually for 19 years from 2002-2020, and demographic data derived from this effort were used to examine the species-specific effects of precipitation and temperature on survival and growth. We also use these data to construct life tables to examine the individual species’ vital rates (survival probabilities, growth, and life expectancies) and then make population projections using the species’ state (size) and weather variables within an Integral Projection Model (IPM) framework. These data could also be used to examine the direct effect of plant competition on the quadrat.  We illustrate how we are modeling herbaceous plant cover, composition, and demography changes over time using GIS and Integral Projection Models, and discuss some of the insights and implications provided by these long-term studies for ponderosa pine-bunchgrass restoration and habitat management.
Stratifying Lidar Point Clouds to Map Vegetation Vertical Structure in Minnesota and Michigan Forests
Patrick Landisch, Lisa Elliott, William J. Severud, Joseph Knight, Mark Nelson, Jody Vogeler
Forests, with their diverse composition, structure, and canopy cover, offer many ecosystem benefits for both terrestrial and aquatic species. They provide habitat for wildlife, improve water quality and quantity, reduce greenhouse gas emissions through carbon sequestration, and shade thermal habitats for aquatic species. Aerial lidar, an active remote sensing technology, provides opportunities to accurately map forested landscapes at high spatial resolutions, and permits users to derive vertical and horizontal information on forest structural characteristics such as canopy cover and tree height. This 3-dimensional vegetation structure details accurate information that can be used to define different habitat categories such as shrubs and trees that may be important predictors of species distributions and habitat use. Minnesota and Michigan have comparable state-wide lidar data with similar pulse densities of 1.5 and 0.7 points per square meter, respectively. We derived raster and vector data products from lidar point clouds for the Arrowhead region of Minnesota and the Huron-Manistee National Forests in Michigan. We used LAStools to process the lidar point clouds and produce raster images at 10 meter spatial resolutions for canopy density and cover for height intervals defined to closely match FIA field data collection protocols. We created vector layers that represent forest stand polygons delineated based on total canopy height by height intervals, and attributed each polygon with relevant canopy metrics. Canopy metrics, calculated by filtering points by each height threshold, include the average point height, the 95th percentile, percent coverage, and standard deviation. We found that the majority of canopy cover falls into height interval 10-15m, while height intervals above 25m are relatively uncommon. This poster details our semi-automated approach to transforming lidar point clouds into raster and vector products applicable to forestry and wildlife habitat management and presents our canopy cover raster layers for MN and MI study areas.
Estimating observational home ranges (OHR) for female grizzly bears with cubs in Yellowstone National Park using citizen science data
Tyler Brasington, James Halfpenny
Monitoring and surveillance are superb applications for citizen science, requiring little technical or specialized equipment. Yellowstone National Park provides a unique opportunity to utilize citizen science, given its dedicated local community, wildlife-watching community, and continual accessibility. Integrating citizen science with federal agency data allows for better spatial and temporal continuity of information. The compilation of citizen science data with agency observations and information will extend data collection capabilities and assist budget-strapped federal agencies with limited finances and staff resources. The Interagency Grizzly Bear Study Team (IGBST) records unduplicated counts of females with cubs of the year (FCOY) to gather quasi-quantitative data on the Greater Yellowstone Ecosystem (GYE) grizzly population. FCOY are typically easier to count than other demographics because of their visibility, combined with their timing surrounding maternal care. We analyzed visual observations of females with cubs obtained via citizen scientists between 2016-2018. We created ‘Observational Home Ranges’ (OHR) for ten female grizzly bears with cubs using three home range estimators: minimum convex polygons (MCP), local convex hull polygons (LoCoH.k), fixed kernel density estimation (FKDE; hLSCV & hREF). Due to the nature of the data gathered (visual observation) instead of telemetry or GPS technology, we elected to differentiate our terminology from the home range to ‘observational range.’ We provide a direct comparison of home range sizes previously calculated and reported using VHF and GPS collar technology to evaluate accuracy vs. cost-benefit of the various techniques employed. This study intends to provide supplementary data and analysis to federal agencies to aid bear management decisions within Yellowstone National Park while providing insight into home range estimators’ effectiveness on visual observation data for grizzly bears in Yellowstone National Park.
On the use of drones to enhance spring habitat surveys
Jeri Ledbetter, Carol Chambers, Jeff Jenness, Jose Martinez-Fonseca, Larry Stevens, Andrea Hazelton
Spring ecosystems are among the most biologically diverse and ecologically vital ecosystems on the landscape, and even more so in dry habitats such as the Southwestern US.  Comprehensive surveys of these spring habitats give us a unique perspective into larger ecosystem dynamics.  Unfortunately, some aspects of springs can be difficult to measure and describe.  It is easy enough to record elevation, slope and aspect at a location, for example, but it is considerably more difficult to precisely describe the complex topographic shape of the surrounding area.  We can map the outflow path, but it is harder to describe the shape of the land that produces that path.  Furthermore, some springs can be difficult or dangerous to reach, such as hanging gardens high on a cliff.  Here we describe lessons learned from some attempts to use relatively inexpensive drone imagery, along with the free open-source software package OpenDroneMap, to describe habitat characteristics of hard-to-reach spring locations and to perform photogrammetric analyses of the images to generate a 3D map of local topography.

Location: Virtual Date: November 5, 2021 Time: 3:00 pm - 4:00 pm