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AppLCC - Work Plan [Tasks] and [Goals] Reporting
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Adjacent LCCs
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Funding Reporting: NAS request (2015)
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AppLCC-NALCC_reporting_coord
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AppLCC - general resource materials
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AppLCC-NALCC_reporting_coord
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NA - general resource materials
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AppLCC-NALCC_reporting_coord
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Rose's Crosswalk - sent to Scott Jan 9th 2015
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Rose's email: sending to you and Scott the crosswalk I worked on.
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NALCC Project Metadata spreadsheet w/ coding
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Scott's email: As a starting point for discussion, Maritza and I have cross-walked our project pages with the fields in the National Project Catalog. We've color-coded them based on the degree to which they match (e.g., green = exact match, red = something in National system that we don't have). For the AppLCC, we also included Rose's cross-walk of your fields with the National Catalog.
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AppLCC-NALCC_reporting_coord
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Spatial and Temporal Dynamics in Brook Trout Density: Implications for Population Monitoring
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T.Wagner et al., Abstract
Many potential stressors to aquatic environments operate over large spatial scales, prompting the need to assess and
monitor both site-specific and regional dynamics of fish populations. We used hierarchical Bayesian models to evaluate
the spatial and temporal variability in density and capture probability of age-1 and older Brook Trout Salvelinus
fontinalis from three-pass removal data collected at 291 sites over a 37-year time period (1975–2011) in Pennsylvania
streams. There was high between-year variability in density, with annual posterior means ranging from 2.1 to 10.2
fish/100 m2
; however, there was no significant long-term linear trend. Brook Trout density was positively correlated
with elevation and negatively correlated with percent developed land use in the network catchment. Probability
of capture did not vary substantially across sites or years but was negatively correlated with mean stream width.
Because of the low spatiotemporal variation in capture probability and a strong correlation between first-pass CPUE
(catch/min) and three-pass removal density estimates, the use of an abundance index based on first-pass CPUE could
represent a cost-effective alternative to conducting multiple-pass removal sampling for some Brook Trout monitoring
and assessment objectives. Single-pass indices may be particularly relevant for monitoring objectives that do not
require precise site-specific estimates, such as regional monitoring programs that are designed to detect long-term
linear trends in density.
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Brook Trout and Stream Temperature Workshop Information
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Resource Materials: Reprints
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Detecting Temporal Trends in Freshwater Fisheries Surveys: Statistical Power and the Important Linkages between Management Questions and Monitoring Objectives
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by T.Wagner et al., ABSTRACT: Monitoring to detect temporal trends in biological
and habitat indices is a critical component of fisheries
management. Thus, it is important that management objectives
are linked to monitoring objectives. This linkage requires a
definition of what constitutes a management-relevant “temporal
trend.” It is also important to develop expectations for the
amount of time required to detect a trend (i.e., statistical power)
and for choosing an appropriate statistical model for analysis.
We provide an overview of temporal trends commonly encountered
in fisheries management, review published studies that
evaluated statistical power of long-term trend detection, and
illustrate dynamic linear models in a Bayesian context, as an
additional analytical approach focused on shorter term change.
We show that monitoring programs generally have low statistical
power for detecting linear temporal trends and argue that
often management should be focused on different definitions of
trends, some of which can be better addressed by alternative
analytical approaches.
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Brook Trout and Stream Temperature Workshop Information
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Resource Materials: Reprints
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Fall and Early Winter Movement and Habitat Use of Wild Brook Trout
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Abstract
Brook Trout Salvelinus fontinalis populations face a myriad of threats throughout the species’ native range in
the eastern United States. Understanding wild Brook Trout movement patterns and habitat requirements is essential
for conserving existing populations and for restoring habitats that no longer support self-sustaining populations.
To address uncertainties related to wild Brook Trout movements and habitat use, we radio-tracked 36 fish in a
headwater stream system in central Pennsylvania during the fall and early winter of 2010–2011. We used generalized
additive mixed models and discrete choice models with random effects to evaluate seasonal movement and habitat
use, respectively. There was variability among fish in movement patterns; however, most of the movement was
associated with the onset of the spawning season and was positively correlated with fish size and stream flow. There
was heterogeneity among fish in selection of intermediate (0.26–0.44 m deep) and deep (0.44–1.06 m deep) residual
pools, while all Brook Trout showed similar selection for shallow (0.10–0.26 m) residual pools. There was selection for
shallow residual pools during the spawning season, followed by selection for deep residual pools as winter approached.
Brook Trout demonstrated a threshold effect for habitat selection with respect to pool length, and selection for pools
increased as average pool length increased up to approximately 30 m, and then use declined rapidly for pool habitats
greater than 30 m in length. The heterogeneity and nonlinear dynamics of movement and habitat use of wild Brook
Trout observed in this study underscores two important points: (1) linear models may not always provide an accurate
description of movement and habitat use, which can have implications for management, and (2) maintaining stream
connectivity and habitat heterogeneity is important when managing self-sustaining Brook Trout populations.
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Resource Materials: Reprints
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Modeling spatially varying landscape change points in species occurrence thresholds
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by T. Wagner and S. Miday, Abstract. Predicting species distributions at scales of regions to continents is often necessary, as largescale
phenomena influence the distributions of spatially structured populations. Land use and land cover
are important large-scale drivers of species distributions, and landscapes are known to create species
occurrence thresholds, where small changes in a landscape characteristic results in abrupt changes in
occurrence. The value of the landscape characteristic at which this change occurs is referred to as a change
point. We present a hierarchical Bayesian threshold model (HBTM) that allows for estimating spatially
varying parameters, including change points. Our model also allows for modeling estimated parameters in
an effort to understand large-scale drivers of variability in land use and land cover on species occurrence
thresholds. We use range-wide detection/nondetection data for the eastern brook trout (Salvelinus
fontinalis), a stream-dwelling salmonid, to illustrate our HBTM for estimating and modeling spatially
varying threshold parameters in species occurrence. We parameterized the model for investigating
thresholds in landscape predictor variables that are measured as proportions, and which are therefore
restricted to values between 0 and 1. Our HBTM estimated spatially varying thresholds in brook trout
occurrence for both the proportion agricultural and urban land uses. There was relatively little spatial
variation in change point estimates, although there was spatial variability in the overall shape of the
threshold response and associated uncertainty. In addition, regional mean stream water temperature was
correlated to the change point parameters for the proportion of urban land use, with the change point value
increasing with increasing mean stream water temperature. We present a framework for quantify
macrosystem variability in spatially varying threshold model parameters in relation to important largescale
drivers such as land use and land cover. Although the model presented is a logistic HBTM, it can
easily be extended to accommodate other statistical distributions for modeling species richness or
abundance.
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Brook Trout and Stream Temperature Workshop Information
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Resource Materials: Reprints
