Landscape Partnership Resources Library
Document: Summary of CT Pilot Core Team Meeting & Call, 01-05-2015
Summary of meeting to discuss connectivity layer and relationship to connectors with the terrestrial design.
Modeling spatially varying landscape change points in species occurrence thresholds
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.
Fall and Early Winter Movement and Habitat Use of Wild Brook Trout
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.
Detecting Temporal Trends in Freshwater Fisheries Surveys: Statistical Power and the Important Linkages between Management Questions and Monitoring Objectives
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.
Rose's Crosswalk - sent to Scott Jan 9th 2015
Rose's email: sending to you and Scott the crosswalk I worked on.
NALCC Project Metadata spreadsheet w/ coding
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.
North Atlantic LCC Science Strategy
Original North Atlantic LCC Science Strategy primarily organized around Northeast Conservation Framework. Matrix at end updated annually. Recent updates will be provided separately.
AppLCC Site Map
AppLCC Site Map
Annual Planning Cycle v 2014-4-12 jb
Annual Planning Cycle v 2014-4-12 jb
XWalk - (Key File) - Crosswalk on SHC & OTHER frameworks incl. AppLCC
This is a KEY file as it (1) presents a "crosswalk" between all the various (regionally-referenced) conservation frameworks (cycle graphics of various names): (a) SHC (Strategic Habitat Conservation) Elements, (b) [NE-FWS; Ken Elow] Northeast Regional Conservation Framework (NRCF) = "NALCC model" = "Albany Workshop" cycles/elements, (c) SWAP (state wildlife action plan) Elements, (d) [SE-FWS; Bill Uihlein] Southeastern Conservation Adaptation Strategy = "SECAS", .......AND this shows (e) the alignment of the AppLCC's 5-Year Work Plan (i.e., the Goal, Objective, and Task) shown as a three-digit code.
jb - NAS REQUEST FOR BUDGET DATA: AppLCC revised With Colm 3 2014-10-07
jb - NAS REQUEST FOR BUDGET DATA: AppLCC revised With Colm 3 2014-10-07
AppLCC NAS reporting
2014-09-24 reporting #s in response to NAS review -- posted on Sharepoint
Task 4 Responses V3
Elements of success include both tangible (products, funding, actions with direct links etc…) and intangible components (the synergy of the partnership to foster ideas and relationships which may lead to changes in actions by participating collaborators etc…). Our response to this request for information on performance measures, and about project funding patterns, places the emphasis of success more on the measures that are tangible and directly tied to LCC action, but does not appropriately capture the value of a broad partnership. This should be a topic of conversation with the NAS panel.
AppLCC - Goal 4
Goal 4 update reporting on progress as of Aug 2014
AppLCC - Goal 3
Goal 3 update reporting on progress as of Aug 2014
AppLCC - Goal 2
Goal 2 update reporting on progress as of Aug 2014
AppLCC - Goal 1
Goal 1 update reporting on progress as of Aug 2014
SIAS 2.0 - Instructions (word)
SIAS 2.0 - Instructions (word)
SIAS 2.0 - xls Blank
This is the most recent (and proposed for FY14 reporting) xls template that all LCCs will use in FY14 reporting. It will be discussed in the Oct 2014 National Coordinator's training session at NCTC.
AppLCC 2014 P-Track (CC) Reporting
Response to request from Rick Bennett, for P-Track (Climate) Reporting to DOI. NOTE: added the specific research-projects associated with each score to help in future reporting.
