DOI: 10.5061/dryad.2rbnzs7zw
title: Fish occurrence data with geomorphic and climatic covariates

description: Local species distributions are often geographically restricted to a
 subset of environmental conditions across a species’ full range,
 complicating forecasting climate warming effects. However, Bayesian
 species distribution models (SDM) can leverage geographically restricted
 datasets with broader knowledge of habitat relationships across the
 species’ range, to forecast climate vulnerability in data-limited regions.
 Principles of niche tracking and niche expansion were explored using an
 innovative Bayesian SDM approach to refine a climate vulnerability
 assessment for bull trout (Salvelinus confluentus), a cold-water riverine
 fish. The SDM was fit to a large, spatially dense fish occurrence and
 stream temperature dataset to model how climatic and geomorphic factors
 influence the current and future distribution of bull trout near its
 northern range extent. To assess niche tracking, wherein modelled
 relationships were based on observed occurrence patterns, we fitted the
 SDM with uninformative priors. For niche expansion, which assumes the
 population can adjust to occupy a warmer niche like southerly populations,
 we added an informative prior for summer stream water temperature
 occupancy. Models projected effects of warming on the distribution of
 suitable habitat using Representative Concentration Pathways 4.5 and 8.5
 emissions scenarios for 2061-2080. Bull trout distribution was patchy and
 limited to intermediate thermal and slope conditions in streams with high
 groundwater contributions. The latter is a key determinant of
 biogeographic patterns not seen elsewhere across the species’ range. Under
 niche tracking, suitable habitat extent is projected to decline by 36-46%,
 while under niche expansion, suitable habitat could increase by 25-28%.
 The large dichotomy between projections illustrates the importance of
 considering local features and adaptive capacity when forecasting
 potential responses of cold-water fishes to climate warming. It also
 highlights a need for studies to better understand the mechanisms that may
 prevail as species distributions shift this century.
description: Field surveys were conducted across approximately 400 sites with
 temporal replicates (i.e., sites visited on two seperate occassions within
 a season). Presence-absence data across sampling sites was paired with
 geomorphic and climatic covariates derived through a GIS platform.
 Climatic variables were derived from Spatial Statistical Stream network
 models. 
description: # Fish occurrence data with geomorphic and climatic covariates
 [https://doi.org/10.5061/dryad.2rbnzs7zw](https://doi.org/10.5061/dryad.2rbnzs7zw) ## Description of the data and file structure Stream surveys were conducted across the study area to document the occurrence of bull trout and related species. Each sampling site was visited multiple times to obtain temporal replicates. This dataset includes fish occurrence records along with associated geomorphic and climatic covariates. These data are used in species distribution models to predict the current and future distribution of bull trout in the region. ### Files and variables #### File: prairie_bull_trout_occupancy_Nov2024.csv **Description:**  ##### Variables * site_id: Stream sampling location identity * stream_id: Stream identity * bulltrout_present: 0 = absent, 1 = present  * august_stream_temp: mean August stream temperature  * thermal_sensitivity: thermal sensitivity of streams * contributing_area: stream contributing area in hectares * elevation: stream elevation in meters * gradient: stream gradient  * vegetation_cover: percent of vegetation cover at a site * patch_id: habitat patch identity ## Code/software MS Excel can be used to view th file. ## Access information Other publicly accessible locations of the data: * NA Data was derived from the following sources: * 1:50 000 Canadian Digital Elevation Data, Natural Resources Canada; province polygons: Statistics Canada Catalogue No. 92-160-X; State polygons: US Department of Commerce, US Census Bureau, Geography Division, Cartographic Products and Services Branch.