This dataset is one of a suite of products from the Nature’s Network project (naturesnetwork.org). Nature’s Network is a collaborative effort to identify shared priorities for conservation in the Northeast, considering the value of fish and wildlife species and the natural areas they inhabit. Brook Trout probability of occurrence is intended to provide predictions of occupancy (probability of presence) for catchments smaller than 200 km2 in the Northeast and Mid-Atlantic region from Virginia to Maine. The dataset provides predictions under current environmental conditions and for future increases in stream temperature. Brook Trout probability of occurrence (under current climate) is one input used in developing “Lotic Core Areas, Stratified by Watershed, Northeast U.S.” that is also part of Nature’s Network. Lotic core areas represent intact, well-connected rivers and stream reaches in the Northeast and Mid-Atlantic region that, if protected as part of stream networks and watersheds, will continue to support a broad diversity of aquatic species and the ecosystems on which they depend. The combination of lotic core areas, lentic (lake and pond) core areas, and aquatic buffers constitute the “aquatic core networks” of Nature’s Network. These and other datasets that augment or complement aquatic core networks are available in the Nature’s Network gallery: https://nalcc.databasin.org/galleries/8f4dfe780c444634a45ee4acc930a055.
Intended Uses
In the context of Nature’s Network, this dataset is primarily intended to be used in conjunction with the product “Lotic Core Areas, Stratified by Watershed, Northeast U.S.” to better understand the importance of core areas to Brook Trout. It also can be used on its own to identify priority watersheds for Brook Trout.
The dataset was originally developed for and is part of the Interactive Catchment Explorer (ICE). ICE (http://ice.ecosheds.org/) is a dynamic visualization interface for exploring catchment characteristics and environmental model predictions. ICE was created for resource managers and researchers to explore complex, multivariate environmental datasets and model results, to identify spatial patterns related to ecological conditions, and to prioritize locations for restoration or further study. ICE is part of the Spatial Hydro-Ecological Decision System (SHEDS).
Description and Derivation
The dataset provides predictions under current environmental conditions and for future increases in stream temperature of 2, 4, and 6 degrees Celsius. It employs a logistic mixed effects model to include the effects of landscape, land-use, and climate variables on the probability of Brook Trout occupancy in stream reaches (confluence to confluence). It includes random effects of HUC10 (watershed) to allow for the chance that the probability of occupancy and the effect of covariates were likely to be similar within a watershed. The fish data came primarily from state and federal agencies that sample streams for Brook Trout as part of regular monitoring. A stream is considered occupied if any Brook Trout were ever caught during an electrofishing survey between 1991 and 2010. The results are based on more than 15,000 samples from more than 13,000 catchments from all 13 Northeast states.
Factors that had a strong positive effect on Brook Trout occupancy included percent forest cover and summer precipitation. Factors that had a strong negative effect on occupancy included July stream temperature, percent agriculture, drainage area, and percent upstream impounded area.
Estimates of the probability of occupancy for each catchment with increases in stream temperature of either 2,4 or 6 degrees C are also provided. To provide these estimates, the input values for mean July stream temperature were simply increased by 2, 4, or 6 C and estimated occupancies recorded.
More technical details about the Brook Trout probability of occurrence product are available at: http://conte-ecology.github.io/Northeast_Bkt_Occupancy/. Technical details about the regional stream temperature model, which is used in predicting Brook Trout occupancy, are available at: http://conte-ecology.github.io/conteStreamTemperature_northeast/.
Known Issues and Uncertainties
As with any project carried out across such a large area, this dataset is subject to limitations. The results by themselves are not a prescription for on-the-ground action; users are encouraged to verify, with field visits and site-specific knowledge, the value of any areas identified in the project. Known issues and uncertainties include the following:
Users are cautioned against using the data on too small an area (for example, a small segment of stream), as the data may not be sufficiently accurate at that level of resolution.
Uncertainties in predictions of stream temperature also result in uncertainties in Brook Trout occupancy estimates. Local effects of groundwater (which may provide cold-water refugia for Brook Trout) cannot be well accounted for in regional stream temperature models at this time. Catchments near waterbodies with water control structures such as dams may also have unreliable temperature predictions because the temperature model does not include information on release schedules or strategies.
Catchments with any Brook Trout occurrences reported in the past 30 years have been presumed to be occupied for purposes of the model. If local extirpations have occurred, this could lead to overprediction of the probability of Brook Trout occupancy.
Projections of effects of future temperature changes to Brook Trout occupancy are intended to convey a sense of the resilience of the species to changing temperatures. In reality, stream temperatures will not change at the same rate or uniformly, as some streams are more buffered against changing air temperatures than others.
Brook Trout occupancy predictions are not available in certain areas where surficial soil coarseness data were absent. These areas include the White Mountains of NH and mountainous areas in NY such as the Adirondacks.
As with any regional GIS data, errors in mapping and alignment of hydrography, development, agriculture, and a number of other data layers can affect the model results.
Attribute definitions
Source = data source
FEATUREID = unique identifier
NextDownID = unique identifier of catchment immediately downstream (-1 = none)
Shape_Leng = length of catchment in meters
Shape_Area = area of catchment in square meters
AreaSqKm = area of catchment in square kilometers
huc12 = 12 digit Hydrologic Unit Code for the watershed
stusps = state in which the catchment is located
agricultur = the percentage of the catchment that is covered by agricultural land (e.g. cultivated crops, orchards, and pasture) including fallow land.
elevation = mean elevation of catchment (m)
forest = the percentage of the catchment that is forested
summer_prc = mean precipitation per month in summer (mm)
UpAreaSqKM = drainage area upstream of catchment in square kilometers
occ_curren = probability of Brook Trout occupancy (current climate)
plus2 = probability of Brook Trout occupancy if stream temperature were to warm by 2 degrees C, relative to current climate
plus4 = probability of Brook Trout occupancy if stream temperature were to warm by 4 degrees C, relative to current climate
plus6 = probability of Brook Trout occupancy if stream temperature were to warm by 6 degrees C, relative to current climate
max_temp_0 = the maximum additional stream temperature (degrees C), on top of the current mean summer temperature for the catchment, that would be predicted to result in a 30% probability of occupancy for Brook Trout
max_temp_1 = the maximum additional stream temperature (degrees C), on top of the current mean summer temperature for the catchment, that would be predicted to result in a 50% probability of occupancy Brook Trout
max_temp_2 = the maximum additional stream temperature (degrees C), on top of the current mean summer temperature for the catchment, that would be predicted to result in a 70% probability of occupancy Brook Trout
meanSumme = mean summer stream temperature (C)
meanDays_1 = mean days per year that stream temperature exceeds 18 degrees C
meanDays_2 = mean days per year that stream temperature exceeds 22 degrees C