DOI: 10.5061/dryad.4b8gthtpv
title: Data from: Predictive habitat occupancy models for North American river otters along inland streams in New Jersey

description: The North American river otter (Lontra canadensis) is a semi-aquatic
 furbearer species that historically ranged throughout North America.
 Starting in the mid-1800s and continuing through the early 1900s, the
 negative effects associated with anthropogenic disturbances (i.e.
 overharvest, development and ultimately habitat alternation) led to local
 extinctions. Researchers debate whether current land use patterns are
 affecting river otter occupancy. New Jersey is the most densely populated
 state in the United States, thus it provides a perfect study area to test
 potential anthropogenic effects on river otters. Using occupancy modeling
 to examine river otter habitat preferences, we measured presence/absence
 at 244 low order streams from January-April 2011–2012 along with 19
 corresponding site/landscape covariates in both Northern and Southern New
 Jersey. In Southern New Jersey, we detected otters at 83/141 sites (58.9%)
 with a detection probability of 97.7% across repeat visits and a predicted
 occupancy of 59.4 ± 0.04%. In Northern New Jersey we detected otters at
 31/103 sites (30.1%) with a detection probability of 44.5% across repeat
 visits and a predicted occupancy of 58.8 ± 0.04%. We determined the
 influence of habitat covariates on otter occupancy and found that water
 depth, water quality, stream width and mink presence were positively
 correlated with otter occupancy. The % Commercial, Industrial,
 Transportation and Recreational habitat, % low intensity development, bank
 slope, and distance to lake were negatively correlated with otter
 occupancy. Knowing the location of occupied stream and latrine sites will
 assist biologists in their efforts to monitor river otter populations and
 help estimate river otter density for harvest and conservation efforts.
description: <strong>Site Selection</strong>
 Potential sites were restricted to lower order streams that could
 be safely waded and/or had accessible stream banks that could be surveyed
 by foot. To identify a stratified distribution of random stream sites, we
 looked at streams that were close in proximity to sites classified as
 either Ambient Biomonitoring Network (AMNET, NJDEP 2011) or Index of
 Biotic Integrity (IBI)/Fish Index or Biotic Integrity (FIBI) sites (NJDEP
 2012a) because the New Jersey Department of Environmental Protection
 (NJDEP) assessed the overall water quality and stream health which would
 allow us to use corresponding water quality, habitat quality, and fish
 density data as covariates for our occupancy models. Once the sites were
 selected, we selected transects that would include a road/bridge crossing
 of the stream (Roberts and Crimmins 2008, Crimmins et. al 2009) to ensure
 that we would be surveying areas with some level of human traffic. If some
 AMNET, FIBI, or otter harvest/sightings points were not on a road/bridge
 crossing, we moved our stream site to the nearest road/bridge crossing on
 the stream. We had a total of 244 unique sites surveyed throughout the
 two-year study (124 in 2011 and 120 in 2012) that were comprised of 147
 AMNET, 24 IBI, 25 FIBI, 42 otter harvest/sightings, and 6 random stream
 sites with 141 located in Southern New Jersey and 103 located in Northern
 New Jersey. <strong>Field
 Protocol</strong> To sample the presence of
 otters, we conducted 6 repeat walking transect surveys 1 January - 30
 April 2011 and 2012 every 14 ± 4 d at each survey site. The purpose of
 these 4 days of flexibility was twofold:1) to allow for the potential of
 missing survey days due to inclement weather such as snow, ice, and heavy
 rain and 2) to provide the opportunity for surveyors to be proactive and
 survey certain sites in advance in anticipation of extended periods of
 inclement weather. Additionally, depending on the physical characteristics
 of the sites, surveys were not conducted ≤ 2 d after heavy rain or
 snow/ice storms to ensure the safety of surveyors and because scat and
 track signs could be washed away or hidden under snow. We conducted 600 m
 long stream bank walking sign surveys, usually 300 m on each side of a
 bridge crossing (Roberts et al. 2008). If there were any impediments on a
 particular side of the crossing, such as a lake, extremely dense
 vegetation, or deep water, we walked an additional distance on the other
 side to make up the difference. We took GPS coordinates at each site and
 recorded the start/end time for each stream survey. For each replicate
 visit, we recorded current, overnight, and previous day’s temperature, the
 presence or absence of precipitation (snow/ice/rain) during the survey,
 overnight, and the previous day and the presence or absence of snow or ice
 cover at the sites at the time of the survey. Snow cover can both
 negatively and positively impact the ability of the surveyors to locate
 animal signs. We also recorded average site-level habitat characteristics
 including water depth and bankfull height (i.e. the water level, or stage,
 at which a stream, river or lake is at the top of its banks and any
 further rise would result in water moving into the flood plain), which
 were taken randomly within the transect at each replicate visit while
 stream width, bank height, and bank slope were only taken once per
 site. While visiting each site, we made note of live
 otter sightings and the presence of otter tracks, scat, latrines, and
 slides. We also identified and recorded the substrate where that specific
 otter sign was found. GPS coordinates were taken for otter latrines and
 otter slides so that the NJDFW could monitor these locations in the
 future. <strong>Water Quality Assessment of
 Sites</strong> Water quality and habitat quality
 assessments were based on previously gathered and readily available AMNET
 (NJDEP 2011; NJDEP GIS 2013) and FIBI (NJDEP 2012<em>a</em>)
 data. Because not all survey points were AMNET or FIBI points (i.e. random
 points and otter harvest/sighting points mentioned above, N = 48), not all
 points had this background data that we could use. Therefore, we created
 the following guidelines to provide estimated water and habitat quality
 values for all survey points: For those
 points that were AMNET or FIBI, we used the corresponding water and
 habitat quality assessment scores/ratings for those sites.
        2. For those points that were characterized as
 either random or otter harvest/sighting, we chose the closest AMNET or
 FIBI point to that survey point, with preference given to AMNET or FIBI
 points that were on the same stream as the survey point.
 We also had to consider the year in which the AMNET and FIBI
 points were surveyed because these assessments are not done annually at
 every site making some variation regarding the year in which the water
 quality or habitat quality assessments are made. While AMNET/FIBI samples
 are taken every year, it takes 5 years to reach all sites. While assigning
 water and habitat quality values to a survey site, we chose the most
 recent available values within the five year intervals. Each survey site,
 was given a water quality rating of excellent, good, fair, or poor (via
 AMNET or FIBI rating). Further, survey sites were given habitat quality
 ratings (and corresponding scores) of optimal (160-200), suboptimal
 (110-159), marginal (60-109), and poor (&lt;60) via the AMNET and FIBI
 scoring based on individual condition of 10 habitat parameters (NJDEP
 2007). <strong>Data Analysis: Occupancy and
 Predictive Habitat Modeling</strong> We
 arbitrarily defined the “survey site” as the area within 600 m of the 600
 m stream transect (i.e. 600 m upstream and 600 meters downstream totaling
 1800 m in length). We quantified the AMNET/FIBI 10 site level habitat
 configuration and quality variables including: stream width, water depth,
 bank height, bankfull height, bank slope, distance to the closest
 lake/pond, AMNET/FIBI water quality, AMNET/FIBI habitat quality, as well
 as beaver and mink presence we observed in our transects.
 For landscape level covariates, we reclassified the 84 land
 use/land cover types of the New Jersey Land Use Land Cover (NJLULC)
 dataset to into 8 summary habitat types defined by NJLUC including: 1)
 High Intensity Development; 2) Low Intensity Development; 3) Commercial,
 Industrial, Transportation and Recreational (CITR); 4) Agriculture; 5)
 Upland Natural; 6) Fresh Water; 7) Non-coastal Wetlands; and 8) Other. To
 quantify habitat composition, we measured the proportion of each habitat
 type (excluding Other) within the buffered transect area. For each
 covariate measured at the landscape scale, we determined the buffer radius
 around the site at which each covariate was most strongly correlated to
 otter presence among a set of buffer radii ranging from 0.6 km to 16.2 km
 (being within the range of home range estimates of the river otter) at 600
 m increments for all the sites in each sampling region (Holland et al.
 2004, Duren et al. 2011). We used bootstrapping to obtain Spearman’s Rank
 Correlation Coefficients on 10,000 random samples of 10 sites that were
 25.2 km apart in Southern New Jersey and 9 sites that were 22.8 km apart
 in Northern New Jersey.  We estimated site occupancy
 and detection probability using the modeling approach of Mackenzie et al.
 (2002), which accounts for the probability of an individual occupying the
 site and being detected during a survey. We used Akaike’s Information
 Criterion (AIC) to evaluate and select models (Burnham and Anderson 2002)
 and performed analyses using the program PRESENCE (Hines 2006). First, we
 modeled detection probability among survey points considering four
 explanatory covariates: month, observer, snow cover, presence of
 precipitation, and additionally constant detection probability to overcome
 or avoid variation in detection probability, which was considered to be a
 nuisance parameter. We selected a best model from that analysis to control
 for detection probability for subsequent modeling of otter occupancy. We
 modeled otter occupancy for Northern and Southern New Jersey separately
 using logistic regression with the covariates of site and landscape scale
 metrics. Candidate models were constrained by the
 following rules: 1)    Each model, except for the
 initial individual covariate models, included at least one site-scale and
 one landscape-scale covariate. Models were not required to have the same
 number of site and landscape covariates. Finally, to avoid the
 construction of overly complex models, no model was allowed to have more
 than 6 total covariates. 2)    Covariates that are
 correlated (|r| ≥ 0.5) were not used in the same model and we randomly
 chose one of the two correlated variables based on potential predictive
 relationship discussed in the literature. 3)    All
 covariates were used at least once. Some covariates were used more than
 others because they repeatedly appeared in higher-ranked models.
 Covariates that consistently appeared in low-ranked models were used less
 often. Additional covariates were individually added to high-ranked models
 to determine their effect on the model. Covariates that did not improve
 the log-likelihood estimate of the model by a value of &gt;2 were
 removed. 4)    Within the set of candidate models, we
 also included a global model of all covariates and a null model in which
 occupancy was held constant. We calculated the
 small-sample-corrected information criterion AICc for all models (Hurvich
 and Tsai 1989) because sample size was small with respect to the number of
 parameters (K) in the analyses. We performed multiple-model averaging to
 predict occupancy using those models that had substantial support for
 fitting the data given the candidate set of models to address model
 selection uncertainty (Burnham and Anderson 2002). To determine the
 direction and magnitude of effect sizes for covariates, we calculated the
 mean standardized partial regression coefficients across all the models
 containing the variables of interest, and estimated precision using an
 unconditional variance estimator that incorporates model selection
 uncertainty (Burnham and Anderson 2002, p. 162). We
 ultimately validated the accuracy of our best-fit occupancy models for
 Southern and Northern New Jersey by constructing an error matrix as part
 of a classification analysis. This analysis required the use of observed
 and conditional occupancy values for every site in both sampling regions.
 We averaged conditional occupancy values for each site in both sampling
 regions across all models that had the best support for fitting the data
 (i.e. those models that were used in the model averaging process). Thus,
 each site in both regions had representative observed and conditional
 occupancy values. Using the constructed matrix, we determined the overall
 accuracy and our omission and commission errors for the best-fit occupancy
 models in both regions (Congalton 1991).
description: # New Jersey River Otter Occupancy
 [https://doi.org/10.5061/dryad.4b8gthtpv](https://doi.org/10.5061/dryad.4b8gthtpv) ## Description of the data and file structure We chose fewer sites and smaller distances per bootstrapped replicate in Northern New Jersey to better accommodate the smaller area of the Northern study area. We used Student’s t-test (α = 0.05) to identify the habitat-specific landscape distances that exhibited the strongest correlation to river otter presence at the site. We used the smallest radius within that range as the scale for measuring that landscape variable (see Figure 2 as an example). Using the shortest distance reduced the overlap of landscape buffers between points. In addition to the 8 spatially explicit landscape covariates (% Agriculture; % Commercial, Industry, Transportation, Recreation; % High-Intensity Development; % Low-Intensity Development; % Upland Natural; % Water, % Wetland, % Other) we also measured 8 site level covariates including: 1\)    Average water depth at a site during the field season: (1) 0-15 cm, (2) 16-30 cm, (3) 31-45 cm, (4) 46-60 cm, (5) 61-75 cm, (6) 76-90 cm, (7) 91-105 cm, (8) &gt;106 cm. 2\)    Average bank height, measured from the stream bed to the top of the stream bank, for the surveyed portion of the stream: (1) 0-31 cm, (2) 31-62 cm, (3) 62-93 cm, (4) 93-124 cm, (5) 124-155 cm, (6) 155-186 cm, (7) &gt;186 cm. 3\)    Average bankfull height, measured from the top of the water line to the top of the bank, at a site during the field season: (1) 0-31 cm, (2) 31-62 cm, (3) 62-93 cm, (4) 93- 124 cm, (5) 124-155 cm, (6) 155-186 cm, (7) &gt;186 cm. 4\)    Average bank slope for the surveyed portion of the stream: (1) &lt;30 degrees, (2) 30-60 degrees, (3) &gt;60 degrees. 5\)    Average stream width for the surveyed portion of the stream: (1) 0-2 m, (2) 2- 4 m, (3) 4-6 m, (4) 6-8 m, (5) 8- 10, (6) &gt;10 m 6\)    Distance to closest lake or pond from the survey site: (0) 0-.5 km, (1) .5-1 km, (2) 1-1.5 km, (3) 1.5-2 km, (4) 2-2.5 km, (5) 2.5-3 km, (6) &gt;3 km. 7\)    Overall water quality for a site, determined by AMNET/FIBI rating: (1) Poor, (2) Fair, (3) Good, (4) Excellent. 8\)    Overall habitat quality for a site, determined by AMNET/FIBI rating: (1) Poor, (2) Marginal, (3) Sub-optimal, (4) Optimal.This is data collected in 2010-2011 regarding otter occupancy in rivers in New Jersey. The data is organized by the following columns: * Site Number - A unique identifier for the stream point measured * Visit - a value over 6 repeat visits * Date * Current Weather * Previous Weather * Start Time - of survey * End Time - of survey * % Ice Cover * Ice Cover (Binary) - yes (1) or no (0) * Snow Cover * Snow Cover (Binary) - yes (1) or no (0) * Precipitation * Precipitation Binary - yes (1) or no (0) * Approx water depth * Average Water Depth * Soil Type * Bank Slope * Average Bank Slope * Bank Height * Average Bank Height * Stream Width * Average Stream Width * Otter sign * Otter Sign Type/Location Substrate * Otter Binary - yes (1) or no (0) * Mink sign * Mink Binary - yes (1) or no (0) * Beaver sign * Beaver Binary - yes (1) or no (0) * Muskrat sign * Muskrat Binary - yes (1) or no (0) * Notes Missing data: n/a