10.5061/DRYAD.3C5R8
Bean, William T.
Humboldt State University
Stafford, R.
University of California, Berkeley
Butterfield, H. Scott
Nature Conservancy
Prugh, Laura R.
University of Alaska Fairbanks
Westphal, Michael
Bureau of Land Management
Brashares, Justin S.
University of California, Berkeley
Data from: Species distribution models of an endangered rodent offer
conflicting measures of habitat quality at multiple scales
Dryad
dataset
2014
MaxEnt
giant kangaroo rat
Dipodomys ingens
habitat suitability
2014-05-08T17:24:01Z
2014-05-08T17:24:01Z
en
https://doi.org/10.1111/1365-2664.12281
8670861 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
1. The high cost of directly measuring habitat quality has led ecologists
to test alternate methods for estimating and predicting this critically
important ecological variable. In particular, it is frequently assumed but
rarely tested that models of habitat suitability (“species distribution
models”, SDMs) may provide useful indices of habitat quality, either from
an individual animal or manager’s perspective. Critically, SDMs are
increasingly used to estimate species’ ranges, with an implicit assumption
that areas of high suitability will result in higher probability of
persistence. This assumption underlies efforts to use SDMs to design
protected areas, assess the status of cryptic species, or manage responses
to climate change. Recent tests of this relationship have provided mixed
results, suggesting SDMs may predict abundance but not other measures of
high quality habitat (e.g., survival, persistence). 2. In this study, we
created a suite of SDMs for the endangered giant kangaroo rat Dipodomys
ingens at three distinct scales using the machine-learning method Maxent.
We compared these models with three measures of habitat quality: survival,
abundance, and body condition. 3. SDMs were not correlated with survival,
while models at all scales were positively correlated with abundance.
Finer-scale models were more closely correlated with abundance than the
largest scale. Body condition was not correlated with habitat suitability
at any scale. The inability of models to predict survival may be due to a
lack of information in environmental covariates; unmeasured community
processes or stochastic events; or the inadequacy of using models that
predict species presence to also predict demography. Synthesis and
applications: SDMs, especially fine scale ones, may be useful for
longer-term management goals, such as identifying high quality habitat for
protection. However, short-term management decisions should be based only
on models that use covariates appropriate for the necessary temporal and
spatial scales. Assumptions about the relationship between habitat
suitability and habitat quality must be made explicit. Even then, care
should be taken in inferring multiple types of habitat quality from SDMs.
GKR Capture HistoriesCapture histories at each trapping grid, formatted
for analysis in Program MARK or rMarkcaphists_combined.csvEnvironmental
Predictors for Giant Kangaroo Rats in Carrizo PlainASCII rasters of
environmental predictors used to produce species distribution model for
giant kangaroo rats (Dipodomys ingens) in the Carrizo Plain National
Monument.SDM_ASCII.zipEstimate of giant kangaroo rat burrow densityCount
of active and inactive giant kangaroo rat burrows in 10m x 50m transect at
sites across GKR range.burrow.mean.rangewide.csv
Ciervo-Panoche
Carrizo Plain
California