Fish occurrence data with geomorphic and climatic covariates

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 uninformativ..., 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. , , # 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 ...,