This dataset shows modelled habitat suitability for the Osprey (Pandion haliaetus)Â under current and projected
future conditions. <br /> <br />We built habitat suitability models for 237 bird, 117 mammal, and 12 amphibian species. Species
were chosen for inclusion in the study based on a simple set of criteria. For a species to be included in the study, it had
to be primarily associated with terrestrial habitats, have a digital map of its current range, and have some portion of its
current distribution intersect with the study area extent. In addition, we restricted the list of species used in the study
to those for which a well-performing continental-scale model could be built. Digital species range maps were converted from
polygons into 50 square kilometer resolution grid cells representing species presences. Although using point-based occurrence
data to represent species presences is preferred when building correlative niche models, comprehensive occurrence data sets
that adequately represent entire species ranges are generally unavailable, particularly for wide-ranging species with distributions
extending into the subarctic and artic regions of North America. We used maps representing species ranges (Ridgley et al.
2003, Patterson et al. 2003, IUCN 2013), that at a coarse, continental scale were deemed to be adequate for representing speciesâ
climatic niches. <br /> <br />Values represent projected habitat suitability changes. Each cell is attributed with one of
four values, which represent: <br /> <br /> <ul> <li>10 - not suitable habitat<br /></li> <li>11 - not suitable habitate historically
(1961-1990), suitable in the future (2070-2099), i.e. "expansion"<br /></li> <li>20 - suitable habitat historically,
not suitable in the future (2070-2099), i.e. "contraction"<br /></li> <li>21 - suitable habitat historically, present
in the suitable in the future (2070-2099), i.e. "stable"<br /></li> </ul> <br />The tens digit represents presence/absence
historically with 1 = not present and 2 = present. The ones digit represents the projected future presence/absence (for the
future time period) with 0 = not present and 1 = present. <br /> <br />Climate suitability models were developed for each
of the 366 species using the 50-km resolution dataset, and we used random forests classifiers to model species distributions
as a function of the bioclimatic variables. Random forest is an ensemble-based machine-learning algorithm used for both classification
and regression analysis producing relatively accurate predictions based on the combined results of multiple classification
trees. The random forest models produce predictions ranging from 0â1. To convert these values to a binary prediction
representing suitable or unsuitable conditions, we selected a threshold value for each species model based on the receiver
operator characteristic (ROC) curve, and an equal weighting of the importance of false positives and false negatives. To project
potential future changes in climatic suitability for the 366 species, we used projected climate from two coupled atmospheric-oceanic
general circulation models (AOGCMs), the Hadley CM3 model, and the Canadian Centre for Climate Modeling and Analysis CGCM3.1
model. The Hadley CM3 model simulates future climatic conditions that are warmer and drier relative to CGCM 3.1 model projections.
We used projections for one greenhouse-gas emissions scenarioâthe A2 scenario, as described by the IPCC Special Report
on Emissions Scenarios. The A2 scenario represents a mid-high emission scenario. Projected future values for the 23 bioclimatic
variables were applied to the same 30 arc-second grid used for the historical data. The future projections were averaged across
a 30-year period, spanning the years 2070 to 2099. <br /> <br />The models were built using 75% of the presences and absences
for each species. We used the remaining 25% to test the models. We calculated the proportion of correctly predicted presences
and absences for each species. The species for which the models correctly predicted at least 80% of the presences and at least
95% of the absences were used in the study. We then applied the 50-km resolution climate suitability models to the downscaled
1-km2 resolution climatic data to produce fine-scaled versions of the baseline and future projected climate suitability maps
for each species To account for the impacts of vegetation on species distributions and the fact that our climate-based models,
built with coarse resolution data, were unable to capture finer scale patterns, we refined our model projections with projected
changes in habitat suitability based on climate-driven changes in biome distributions. To assess potential impacts of biome
shifts on species distributions, we used projected biomes from Rehfeldt et al. (2012). <br /> <br />Terrestrial habitat associations
described in the NatureServe Explorer online database records to develop the species-biome relationships using the biome classifications
developed by Rehfeldt et al. (2012). With these relationships as a guide, we classified each biome type as either suitable
or unsuitable for each species. We then generated maps of biome-suitability for each species based on these classifications
and the projected future biome distributions. For each species, we combined the map of projected biome-suitability with the
map of projected climate suitability to produce a projection of habitat suitability. As a final refinement to these projections,
for all non-synanthropic species, we reclassified areas dominated by urban, suburban, exurban and agricultural land-uses as
being unsuitable. We classified species as being synanthropic or non-synanthropic based on habitat associations recorded in
NatureServe Explorer.
Citation
Title Osprey (Pandion haliaetus) Habitat Suitability Change Models