The USGS Conte Laboratory developed an occupancy model for Brook Trout based on presence/absence data from agencies (see below) and landscape data housed in SHEDS: http://ecosheds.org/. The aim of the model was to provide predictions of occupancy (probability of presence) for catchments smaller than 200 km2 in the northeastern US from Virginia to Maine. We provide predictions under current environmental conditions and for future increases in stream temperature.
We used 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). We included random effects of HUC10 (watershed) to allow for the chance that the probability of occupancy and the effect of covariates were similar within a watershed. Our fish data came from state and federal agencies that sample streams for Brook Trout as part of regular monitoring (see below). We considered a stream occupied if any Brook Trout were ever caught during an electrofishing survey between 1991 and 2010.
|State||Number of locations||Number of catchments||Min year||Max year|
Documentation related to the landscape, land-use, streams, catchment delineation, and climate variable data sources and processing can be found at http://conte-ecology.github.io/shedsGisData/. We used an AICc model selection approach to find the best combination of variables balancing model fit with model complexity. The model set was based on the literature and author experience and followed closely with Kanno et al. 2015.
|Total Drainage Area||The total contributing drainage area from the entire upstream network||The SHEDS Data project||The individual polygon areas are summed for all of the catchments in the contributing network||NHDHRDV2|
|Riparian Forest Cover||The percentage of the upstream 200ft riparian buffer area that is covered by trees taller than 5 meters||The National LandCover Database (NLCD)||All of the NLCD forest type classifications are combined and attributed to each riparian buffer polygon using GIS tools. All upstream polygon values are then aggregated.||nlcdLandCover|
|Precipition||The mean of the summer daily precipitation for the individual local catchment||Daymet Daily Surface Weather and Climatological Summaries||Daily precipitation records are spatially assigned to each catchment based on overlapping grid cells using the zonalDaymet R package||daymet|
|Mean July Stream Temperature||Estimated stream temperature from the SHEDS regional model||SHEDS stream temperature model||Daily stream temperature estimates were aggregated to a mean July value for each catchment||conteStreamTemperature_northeast|
|Upstream Impounded Area||The total area in the contributing drainage basin that is covered by wetlands, lakes, or ponds that intersect the stream network||U.S. Fish & Wildlife Service (FWS) National Wetlands Inventory||All freshwater surface water bodies are attributed to each catchment using GIS tools. All upstream polygon values are then aggregated.||fwsWetlands|
|Percent Agriculture||The percentage of the contributing drainage area that is covered by agricultural land (e.g. cultivated crops, orchards, and pasture) including fallow land.||The National LandCover Database||All of the NLCD agricutlural classifications are combined and attributed to each catchment polygon using GIS tools. All upstream polygon values are then aggregated.||nlcdLandCover|
|Percent High Intensity Developed||The percentage of the contributing drainage area covered by places where people work or live in high numbers (typically defined as areas covered by more than 80% impervious surface)||The National LandCover Database||The NLCD high intensity developed classification is attributed to each catchment polygon using GIS tools. All upstream polygon values are then aggregated.||nlcdLandCover|
|Parameter||Estimate||Std. Error||z value||P-value|
Random Effects (HUC10):
These results indicate that mean July stream temperature had the largest (negative) effect on the probability of Brook Trout occupancy. Forest cover within the 200 foot riparian buffer had a strong positive effect on occupancy, whereas agriculture within the entire upstream drainage had a negative effect on occupancy. Mean summer precipitation has a positive effect on occupancy and the effect was larger with increasing levels of riparian forest cover, but was not dependent on stream drainage area. The total impounded area on the stream network had a negative effect on Brook Trout occupancy as did the upstream drainage area. Surficial coarseness was positively correlated with the presence of Brook Trout, which may be a result of better physical habitat structure or as an indication of local groundwater upwelling.
The average occupancy across the range of observed catchments was 0.58.
The effects of these landscape and climate characteristics are similar to what has been observed in other Brook Trout studies.
We examined the false positive and false negative rates and used the Area Under the Receiver Operating Characteristic ROC) curve (AUC) to assess the model fit.
The model output (predictions) are the probability of occupancy but the data are observed presence and absence (1 or 0). Therefore, it is difficult to evaluate how well the model predicts the data. The probabilities of occupancy must be converted to presence-absences for comparison. We do this over a range of thresholds (= cutoffs). The threshold is the probability above which the stream is assumed to be occupied (Brook Trout = present). For example, if the probability of occupancy for a stream is 0.45 and we set a threshold = 0.50, we would assign the stream as unoccupied (absent). However, if we used a threshold of 0.4 then this same stream would be assigned as occupied (present). If the true (observed) state of the stream was occupied, then using a threshold of 0.5 would result in a false absence (predicted absent when really present) but if we used a threshold of 0.4 we would correct assign the stream as occupied (true positive). Assigning a threshold is a balance of trade-offs between false positives and false negatives. The balance is based on the risk tolerance to the consequences of type I and type II errors.
AUC can range from 0-1. An AUC value of 0.5 indicates the model does no better than random chance in discriminating occupancy. Models with AUC >0.7 are considered to have good discrimination in assessing the probability of occupancy.