The Impact of Management on the Movement and Home Range Size of Indiana’s Eastern Hellbender Salamanders Emily B. McCallen, Bart T. Kraus, Nicholas G.

Slides:



Advertisements
Similar presentations
Action Effectiveness Monitoring in the Upper Columbia (Chapter 4) Karl M. Polivka, Pacific Northwest Research Station, USDA Forest Service.
Advertisements

Assessment of Bull Trout Populations in the Yakima River Watershed.
Apparent over-winter survival of juvenile coho in three tributaries to the lower Columbia River Trevor Johnson, Mara Zimmerman, Matthew Sturza, Patrick.
Tagging  Fish are captured via angling & implanted with a VEMCO acoustic transmitter (V13, V9, or V7) – (Figure 4).  Specific age classes are targeted.
The introduction of zebra mussels (ZM) into the Great Lakes and subsequently waters of the Ohio and Mississippi River Valleys, and numerous other waters.
Estimation from Samples Find a likely range of values for a population parameter (e.g. average, %) Find a likely range of values for a population parameter.
FACTORS AFFECTING NESTING SUCCESS OF COEXISTING SHOREBIRDS AT GREAT SALT LAKE, UTAH John F. Cavitt, Department of Zoology, Weber State University The Great.
Copyright © 2014, 2013, 2010 and 2007 Pearson Education, Inc. Chapter Hypothesis Tests Regarding a Parameter 10.
Autocorrelation Lecture 18 Lecture 18.
Random Variables and Probability Distributions
The Lognormal Distribution
Populations and Home Range Relationships of the Box Turtle Emily Marquardt February 15, 2007 Emily Marquardt February 15, 2007.
Environmental Science
Embedding population dynamics models in inference S.T. Buckland, K.B. Newman, L. Thomas and J Harwood (University of St Andrews) Carmen Fernández (Oceanographic.
A Statistical Analysis of Seedlings Planted in the Encampment Forest Association By: Tony Nixon.
Describing Populations Population Ecology. POPULATION Individuals of the same species living in a particular area Population size, density, distribution.
Esri Southeast User Conference Lara Hall May 5, 2014.
Why Model? Make predictions or forecasts where we don’t have data.
+ Chapter 12: More About Regression Section 12.1 Inference for Linear Regression.
Chinook Salmon Supplementation in the Imnaha River Basin- A Comparative Look at Changes in Abundance and Productivity Chinook Salmon Supplementation in.
Chapter 7. Control Charts for Attributes
6.1 Inference for a Single Proportion  Statistical confidence  Confidence intervals  How confidence intervals behave.
BIOL 4240 Field Ecology. How many? “Estimates of abundance themselves are not valuable, and a large book filled with estimates of the abundance of every.
Objective Determine how using 9mm tags may affect our current research project in the John Day Basin.
Social structure and habitat preferences of moose population in Biebrza National Park Mammal Research Institute, Polish Academy of Sciences, Białowieża.
Fecal DNA typing to determine the fine scale population structure and sex-biased dispersal pattern of Eurasian otter (Lutra lutra) in Kinmen CHUAN-CHIN.
A Comparison of Northern Bobwhite Demographic Sensitivity between a Mid- Atlantic and a National Population Model Chris Williams 1, Brett Sandercock 2,
Population Structure High population divergence at the state level Populations from western Indiana genetically differed from the BONWR population Genetic.
Chapter 9 Estimation and Confidence Intervals. Our Objectives Define a point estimate. Define level of confidence. Construct a confidence interval for.
Essential Statistics Chapter 191 Comparing Two Proportions.
Use & Availability of Habitats & Foods Resource selection Measurements of use & availability –Food –Habitat Design & analysis Modeling Sampling.
Some Wildlife Census Techniques
The Role of Telecommunications in Our Society
Why Model? Make predictions or forecasts where we don’t have data.
Density Estimation Converts points to a raster
BAE 6520 Applied Environmental Statistics
Christopher Nagy, Mianus River Gorge; Bedford, NY
Demographic vital rates and population growth:
BAE 5333 Applied Water Resources Statistics
AP Biology Intro to Statistics
Brown Trout Growth: Growing, Growing, or Gone
Inference and Tests of Hypotheses
Identify the abiotic and biotic factors in this picture
Scientific Method Section 1.1.
Multiple Regression Analysis and Model Building
How demographics and the economic downturn are affecting the way we live LSE Seminar: 1 July 2013 Neil McDonald: Visiting Fellow CCHPR.
Chapter 5 STATISTICS (PART 4).
Comparison of Selected Behavioural Patterns of German Shepherd Puppies in Open-Field Test by Practical Assessment Report   Igor Miňo, Lenka Lešková University.
Population Dynamics Chapter 6 pp
Volume 25, Issue 6, Pages (March 2015)
Forecasting Population Size
Populations have many Characteristics
The Abundance and Distribution of Populations
Team 1 A subject who is exposed to the special treatment is in the…
Statistical Analysis Error Bars
ECOSYSTEMS & ENERGY FLOW
Basic Practice of Statistics - 3rd Edition Inference for Regression
Psych 231: Research Methods in Psychology
Nicholas A. Procopio, Ph.D, GISP
Psych 231: Research Methods in Psychology
Bell Ringer You need: Both handouts Calculator.
Inferential Statistics
Psych 231: Research Methods in Psychology
Sampling Distribution of a Sample Proportion
Nicholas A. Procopio, Ph.D, GISP
Populations Chapter 5.
LEARNING AND PERFORMING WILDLIFE RESEARCH THROUGH THE MUSKRAT
Volume 67, Issue 6, Pages (September 2010)
Volume 25, Issue 6, Pages (March 2015)
Presentation transcript:

The Impact of Management on the Movement and Home Range Size of Indiana’s Eastern Hellbender Salamanders Emily B. McCallen, Bart T. Kraus, Nicholas G. Burgmeier, and Rod N. Williams Department of Forestry and Natural Resources GIS Processing Results Hellbenders in Indiana Large, fully aquatic salamanders requiring cool, fast-flowing lotic systems (Fig. 1) Once found in tributaries throughout the Ohio and Wabash River watersheds Now restricted to a single river in southern Indiana (Fig. 2) 20 years of monitoring has revealed significant, ongoing declines in the abundance of hellbenders in the Blue River No evidence of successful recruitment during this time leading to an increasingly geriatric population All points recorded on Garmin Rino 86 and imported into ESRI ArcGIS 10.2 (Fig. 3) Distance between points calculated with R package “geosphere” Linear Home Range (LHR) Calculation Points snapped to line representing river midpoint Points connected and distance (m) of line measured (Fig. 3) Mean Convex Polygon (MCP) Home Range Calculation Minimum bounding geometry tool with convex hull geometry type Polygon area (m2) measured (Fig. 3) Movement Probability Variable Estimate SE P-value Intercept -1.840 0.197 <2e-16 Time Since Last Observation -0.184 0.048 0.0001 Time Since Release 0.338 0.034 Site Density -0.207 0.082 0.0119 Fall 0.218 0.090 0.0148 Predicted movement probability over time Distance Moved Variable Estimate SE P-value Intercept 2.94 0.106 <2e-16 Time Since Last Observation 0.115 0.038 0.0027 Site Density -0.154 0.070 0.0286 Fall 0.688 0.083 Status 0.698 0.187 0.0002 Predicted movement probability as a function of site density Home Range-LHR Movement and Management Variable Estimate SE P-value Intercept 5.1927 0.1359 <2e-16 Number of Observations 0.2055 0.1137 0.0707 Status 0.599 0.2412 0.0129 Movement tracked 3x weekly during field season (May – November) via radio telemetry Pre-management 2008-2009 21 resident adult hellbenders tracked at 8 sites Post-management 2011-2012 42 individuals tracked at 2 sites where translocations were undertaken to increase local densities Site 1 contained resident adult hellbenders (11) and translocated adult hellbenders (11) from isolated sites across the river Site 2 contained resident adult hellbenders (10) and translocated captive-reared juvenile hellbenders (10) Predicted movement distance for resident and translocated hellbenders Home Range-MCP Variable Estimate SE P-value Intercept 9.562 0.3901 <2e-16 Age -1.0266 0.4260 0.016 Predicted LHR and MCP home range sizes for statistically independent groups Figure 3. Location points, LHR and MCP for an individual hellbender in the study Figure 1. An eastern hellbender on the banks of the Blue River Figure 2. The location of the Blue River in southern Indiana Predicted movement distance as a function of site density Statistical Models Model Type Generalized Linear Mixed Model Generalized Linear Model Discussion Management Less movement (probability and distance) at higher site densities Temporal Initial period of higher movement probability decreases over time More movement (probability and distance) in the fall which is when breeding occurs Individual Translocated hellbenders move more resulting in larger home ranges Effect more pronounced in juvenile age classes Random Effects Animal ID Site ID Negative Binomial Binomial Research Question Error Distribution Are hellbender movement patterns or home range sizes in the Blue River impacted by management actions, temporal patterns, or individual hellbender characteristics? Response Movement Probability Movement Distance LHR Size MCP Size Covariates: Management Site Density (hellbenders/kilometer) Management (pre or post) Acknowledgements Covariates: Temporal Time Since Release Season (summer or fall) Financial support was provided by the Indiana Department of Natural Resources, Division of Fish and Wildlife, Wildlife Diversity Section, State Wildlife Improvement Grant T07R11 and the Department of Forestry and Natural Resources at Purdue University. Eastern Hellbenders were collected, handled, and processed following standard procedures approved by the Purdue Animal Care and Use Committee (PACUC; 08-025-11) and in accordance with Indiana Scientific Permit # 13-0087. Covariates: Individual Age (juvenile or adult) Status (translocated or resident) Sex (female or male) Covariates: Control Time Since Last Observation Number of Observations