10.5061/DRYAD.4B8GTHTDM
Hoffman, Andrew
0000-0002-3874-4457
The Ohio State University
Tutterow, Annalee
The Ohio State University
Gade, Meaghan
The Ohio State University
Adams, Bryce
Agricultural Research Service
Peterman, William
The Ohio State University
Variation in behavior drives multiscale responses to habitat conditions in
timber rattlesnakes (Crotalus horridus)
Dryad
dataset
2021
FOS: Biological sciences
2022-01-24T00:00:00Z
2022-01-24T00:00:00Z
en
https://doi.org/10.5281/zenodo.5535174
4976052 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Variations in both the behavior of wildlife and the scale at which the
environment most influences the space use of wild animals (i.e., scale of
effect) are critical, but often overlooked in habitat selection modeling.
Ecologists have proposed that biological responses happening over longer
time frames are influenced by environmental variables at larger spatial
scales, but this has rarely been empirically tested. Here, we hypothesized
that long-term patterns of behavior (i.e., lasting multiple weeks to
months) would be associated with larger scales of effect than more
sporadic behaviors. We predicted site use by 43 radio-telemetered timber
rattlesnakes (Crotalus horridus) exhibiting four distinct, time-varying
behaviors (foraging, digestion, ecdysis, and gestation) using
remotely-sensed environmental variables related to forest structure and
landscape topography. Among sites used by snakes, warmer temperatures and
higher levels of forest disturbance were predictive of behaviors dependent
on thermoregulation including gestation and ecdysis while more moderate
temperatures and drier, more oak-dominated sites were predictive of
foraging. Long-term behaviors were associated with larger spatial scales
across most variables, supporting our hypothesis that the scale at which
habitat selection occurs is linked to the temporal scale of relevant
behaviors. Management recommendations based on single-scale models of
habitat use that do not account for fine-scale variations in behavior may
obscure the importance of potentially limiting habitat features needed for
infrequent behaviors that are important for growth and reproduction of
this and related species.
Telemetry – We captured individual timber rattlesnakes and surgically
implanted intraperitoneal transmitters (Holohil SI-2T) and then tracked
snakes between dawn and dusk, regularly shifting the order in which
individuals were tracked daily to avoid systematic bias. Upon visually
locating a snake, we recorded the location with a Global Positioning
System (Garmin GPSmap 64s) to an estimated < 5m spatial accuracy.
Raw snake location data in the form of X, Y coordinates are not included
in this dataset as they are sensitive and not necessary to run the models
used in our analysis. Instead, each unique point location is given an
alpha numeric code in the "Point" column. Each row represents a
unique snake observation for a given date (indicated by the
"Date" column) and individual snake (indicated by the
"Snake" column). The "dat_use_5m.csv" also contains
paired random points that do not represent snake observations (indicated
by a 0 in the "Used" column). Behavior Classification – We
identified four behavioral and physiological states (hereafter referred to
as “behaviors”): foraging, digestion, ecdysis, and gestation. We
considered snakes to be foraging when they were observed in a stereotyped
foraging posture. Our observations of snakes digesting a recent meal are
limited by the difficulty in detecting a small bolus in a large-bodied
snake, but we noted this whenever possible. These observations are,
therefore, inherently biased toward snakes digesting larger, more obvious
meals and may be prone to a high false negative detection rate. We noted
observations of snakes in ecdysis as indicated by snakes having blue-gray
eyes and dusky skin coloration. We confirmed observations of gestating
females by sonogram shortly after snake emergence in April and May. We
excluded all locations in which a snake was not visible and presumably
underground or actively moving. When not moving and not clearly
participating in the aforementioned behaviors, we designated snake
behavior as resting. We excluded snake observations from analyses when
there was uncertainty about behavioral classification. For each snake
observation, we indicate which of these four behaviors, if any, were
observed in their respective behavioral columns ("Forage",
"Ecdysis", "Digest", and "Gestate"; a 1
indicates the behavior was observed). The dat_use_5m.csv file does not
contain any behavioral columns as it is only to be used when running the
general site use model. Geospatial Habitat Features – We incorporated 15
geospatial habitat features, characterizing vegetation structure, plant
species composition, and environmental variation, at varying resolutions
(5-30 m), in our analysis of multiscale rattlesnake habitat use. Digital
canopy height ("chm") and elevation models were developed from
LiDAR data provided by the Ohio Geospatial Reference Program, collected in
2007 (http://ogrip.oit.ohio.gov/; accessed 13 October 2014). The LiDAR
data featured two returns pulse-1 with an average spacing and density of
1.27 m and 0.27 returns m-2, respectively. The canopy height and elevation
models incorporated conventional methods, using bilinear interpolation, at
5 m resolution. From the elevation models, we developed layers on slope
("slope"), solar radiation ("sol_rad"), and the Beer’s
transformed aspect ("beers"). In addition to canopy height
represented in the "chm" column, we developed three penetration
ratios (number of returns <2 m in height divided by the number of
returns <50 m and <10 m, including returns <2m; and
the number of returns <1 m divided by the number of returns
<5 m, including returns <1 m) to characterize overstory
("ove"), midstory ("mid"), and understory
("und") vegetation density at 30 m resolution. Woody plant
composition was represented by three ordination axes within a gradient
modeling approach developed from a separate study by B. Adams within the
study area. Vegetation plot data, incorporating relative abundance
profiles of trees and shrubs, including 99 woody plant taxa, were
ordinated by non-metric multidimensional scaling (NMDS) and projected onto
the landscape with terrain data and seasonal multispectral imagery
provided by Landsat 8/OLI. Three subsequent floristic gradients
synthesized moisture ("ndms1"), successional
("ndms2"), and elevational ("ndms3") variation among
species responses within the vegetation data, at 30-m resolution. In
addition, the plot data were used to develop geospatial layers on mean
tree basal area ("rf_tba"; m2 ha-1) and density
("rf_tde"; stems ha-1). From April 2017 to December 2017 we
collected temperature data across the property using HOBO Pendant data
loggers (Model # UA-001-08) set to one-hour logging intervals. We placed
150 loggers randomly across our study site by staking each logger in place
at ground level on the north side of a tree to limit the influence of
canopy structure on thermal data. We used these temperature data as a
response variable in a linear mixed effects model with topographic and
LiDAR-derived variables serving as predictive covariates. The final fitted
model was used to predict the mean near-ground mid-summer temperature
across our landscape ("meanT"). Finally, stand age
("stnd_age") was provided from the ODNR and a flowlines layer
was transformed to a spatial grid (5 m) and used to summarize distances to
the nearest stream ("strm_dist"; https://nationalmap.gov/). We
extracted values for each covariate at all snake and random locations
within different spatial scales of effect and centered and scaled all
covariates prior to fitting statistical models.
We modeled site use with generalized linear mixed effects Bernoulli models
in a Bayesian framework using the brms package in R. The
"dat_use_5m.csv" dataset is only used to run the general site
use model (called in the "> Site Use Model" section of
the R code). The four scale-specific datasets ("dat_5m.csv",
"dat_25m.csv","dat_55m.csv","dat_105m.csv")
are used to run the respective behavioral models.