scholarly journals Optimising Reactive Disease Management Using Spatially Explicit Models at the Landscape Scale

2021 ◽  
pp. 47-72
Author(s):  
Frédéric Fabre ◽  
Jérôme Coville ◽  
Nik J. Cunniffe
Keyword(s):  
Disease Management ◽  
Landscape Scale ◽  
Spatially Explicit ◽  
Spatially Explicit Models

scholarly journals Lessons learned from comparing spatially explicit models and the Partners in Flight approach to estimate population sizes of boreal birds in Alberta, Canada

The Condor ◽  
2020 ◽  
Vol 122 (2) ◽  
Author(s):  
Péter Sólymos ◽  
Judith D Toms ◽  
Steven M Matsuoka ◽  
Steven G Cumming ◽  
Nicole K S Barker ◽  
...  
Keyword(s):  
North America ◽  
Time Of Day ◽  
Lessons Learned ◽  
Population Estimates ◽  
Spatially Explicit ◽  
Species Variation ◽  
Detection Distance ◽  
Spatially Explicit Models ◽  
Population Sizes ◽  
Partners In Flight

Abstract Estimating the population abundance of landbirds is a challenging task complicated by the amount, type, and quality of available data. Avian conservationists have relied on population estimates from Partners in Flight (PIF), which primarily uses roadside data from the North American Breeding Bird Survey (BBS). However, the BBS was not designed to estimate population sizes. Therefore, we set out to compare the PIF approach with spatially explicit models incorporating roadside and off-road point-count surveys. We calculated population estimates for 81 landbird species in Bird Conservation Region 6 in Alberta, Canada, using land cover and climate as predictors. We also developed a framework to evaluate how the differences between the detection distance, time-of-day, roadside count, and habitat representation adjustments explain discrepancies between the 2 estimators. We showed that the key assumptions of the PIF population estimator were commonly violated in this region, and that the 2 approaches provided different population estimates for most species. The average differences between estimators were explained by differences in the detection-distance and time-of-day components, but these adjustments left much unexplained variation among species. Differences in the roadside count and habitat representation components explained most of the among-species variation. The variation caused by these factors was large enough to change the population ranking of the species. The roadside count bias needs serious attention when roadside surveys are used to extrapolate over off-road areas. Habitat representation bias is likely prevalent in regions sparsely and non-representatively sampled by roadside surveys, such as the boreal region of North America, and thus population estimates for these regions need to be treated with caution for certain species. Additional sampling and integrated modeling of available data sources can contribute towards more accurate population estimates for conservation in remote areas of North America.

scholarly journals Incoherent dimensionality in fisheries management: consequences of misaligned stock assessment and population boundaries

2020 ◽  
Author(s):  
Aaron M Berger ◽  
Jonathan J Deroba ◽  
Katelyn M Bosley ◽  
Daniel R Goethel ◽  
Brian J Langseth ◽  
...  
Keyword(s):  
Population Structure ◽  
Stock Assessment ◽  
Spatially Explicit ◽  
Spatial Simulation ◽  
Management Options ◽  
Spatially Explicit Models ◽  
Fisheries Policy ◽  
Simulation Estimation ◽  
Definition Of ◽  
Dynamic Population

Abstract Fisheries policy inherently relies on an explicit definition of management boundaries that delineate the spatial extent over which stocks are assessed and regulations are implemented. However, management boundaries tend to be static and determined by politically negotiated or historically identified population (or multi-species) units, which create a potential disconnect with underlying, dynamic population structure. The consequences of incoherent management and population or stock boundaries were explored through the application of a two-area spatial simulation–estimation framework. Results highlight the importance of aligning management assessment areas with underlying population structure and processes, especially when fishing mortality is disproportionate to vulnerable biomass among management areas, demographic parameters (growth and maturity) are not homogenous within management areas, and connectivity (via recruitment or movement) unknowingly exists among management areas. Bias and risk were greater for assessments that incorrectly span multiple population segments (PSs) compared to assessments that cover a subset of a PS, and these results were exacerbated when there was connectivity between PSs. Directed studies and due consideration of critical PSs, spatially explicit models, and dynamic management options that help align management and population boundaries would likely reduce estimation biases and management risk, as would closely coordinated management that functions across population boundaries.

Improving management advice through spatially explicit models and sharing information

2009 ◽  
Vol 100 (3) ◽  
pp. 191-199 ◽  
Author(s):  
Carlos Montenegro ◽  
Mark N. Maunder ◽  
Maximiliano Zilleruelo
Keyword(s):  
Spatially Explicit ◽  
Spatially Explicit Models ◽  
Management Advice

Range limits in spatially explicit models of quantitative traits

2012 ◽  
Vol 68 (1-2) ◽  
pp. 207-234 ◽  
Author(s):  
Judith R. Miller ◽  
Huihui Zeng
Keyword(s):  
Quantitative Traits ◽  
Range Limits ◽  
Spatially Explicit ◽  
Spatially Explicit Models

An Object-Oriented Approach to Managing Fuzziness in Spatially Explicit Models Coupled to a Geographic Database

2011 ◽  
pp. 269-300 ◽  
Author(s):  
Vincent B. Robinson ◽  
Phil A. Graniero
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Ecological Modeling ◽  
Simulation Models ◽  
Spatially Explicit ◽  
Loosely Coupled ◽  
Spatially Explicit Models ◽  
Modeling Problem ◽  
Object Oriented Approach ◽  
Oriented Approach

This chapter uses a spatially explicit, individual-based ecological modeling problem to illustrate an approach to managing fuzziness in spatial databases that accommodates the use of nonfuzzy as well as fuzzy representations of geographic databases. The approach taken here uses the Extensible Component Objects for Constructing Observable Simulation Models (ECO-COSM) system loosely coupled with geographic information systems. ECO-COSM Probe objects flexibly express the contents of a spatial database within the context of an individualized fuzzy schema. It affords the ability to transform traditional nonfuzzy spatial data into fuzzy sets that capture the uncertainty inherent in the data and model’s semantic structure. The ecological modeling problem was used to illustrate how combining Probes and ProbeWrappers with Agent objects affords a flexible means of handling semantic variation and is an effective approach to utilizing heterogeneous sources of spatial data.

scholarly journals Transport and Water Age Dynamics in Soils: A Comparative Study of Spatially Integrated and Spatially Explicit Models

2020 ◽  
Vol 56 (3) ◽  
Author(s):  
Mitra Asadollahi ◽  
Christine Stumpp ◽  
Andrea Rinaldo ◽  
Paolo Benettin
Keyword(s):  
Comparative Study ◽  
Spatially Explicit ◽  
Water Age ◽  
Age Dynamics ◽  
Spatially Explicit Models

scholarly journals Conservation planning with spatially explicit models: a case for horseshoe bats in complex mountain landscapes

2017 ◽  
Vol 32 (5) ◽  
pp. 1005-1021 ◽  
Author(s):  
Marie Le Roux ◽  
Mathilde Redon ◽  
Frédéric Archaux ◽  
Jed Long ◽  
Stéphane Vincent ◽  
...  
Keyword(s):  
Conservation Planning ◽  
Spatially Explicit ◽  
Spatially Explicit Models ◽  
Mountain Landscapes ◽  
Horseshoe Bats

scholarly journals Spatially explicit models for exploring COVID‐19 lockdown strategies

2020 ◽  
Vol 24 (4) ◽  
pp. 967-1000 ◽  
Author(s):  
David O'Sullivan ◽  
Mark Gahegan ◽  
Daniel J. Exeter ◽  
Benjamin Adams
Keyword(s):  
Spatially Explicit ◽  
Spatially Explicit Models
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