Sensitivity Analysis of a Spatially Explicit Fish Population Model Applied to Everglades Restoration Ren é A. Salinas and Louis J. Gross The Institute.

Slides:



Advertisements
Similar presentations
Action Effectiveness Monitoring in the Upper Columbia (Chapter 4) Karl M. Polivka, Pacific Northwest Research Station, USDA Forest Service.
Advertisements

CALCULATING DAILY PARTICULATE PHOSPHORUS LOADS FROM DISCRETE SAMPLES AND DAILY FLOW DATA METHODS RESULTS * y= (flow) – 2.247; * 1 Y= 0.052(flow)^0.1947;
ATLSS and Uncertainty: relativity and spatially-explicit ecological models as methods to aid management planning in Everglades restoration Louis J. Gross,
ATLSS Fish Functional Group Dynamics Model ALFISH Holly Gaff, Rene’ Salinas, Louis Gross, Don DeAngelis, Joel Trexler, Bill Loftus and John Chick.
Lec 12: Rapid Bioassessment Protocols (RBP’s)
The ATLSS High Resolution Topography/Hydrology Model Scott M. Duke-Sylvester ATLSS Project : University of Tennessee Project web-site : .
Evaluating Potential Impacts of Climate Change on Surface Water Resource Availability of Upper Awash Sub-basin, Ethiopia rift valley basin. By Mekonnen.
Nidal Salim, Walter Wildi Institute F.-A. Forel, University of Geneva, Switzerland Impact of global climate change on water resources in the Israeli, Jordanian.
Down-scaling climate data for microclimate models and forecasts Securing the Conservation of biodiversity across Administrative Levels and spatial, temporal.
A Parallel Structured Ecological Model for High End Shared Memory Computers Dali Wang Department of Computer Science, University of Tennessee, Knoxville.
Disturbance regimes in restoration ecology: novel effects and ecological complexity Sarah Marcinko November 11, 2005.
Fig. 1-3: The long run growth rate for the entire population, for different numbers of subpopulations. Fig. 1: high level of growth rate synchrony among.
ALFISHES: A Size-structured and Spatially-explicit Model for Predicting the Impact of Hydrology on the Resident Fishes of the Everglades Mangrove Zone.
Chapter 3.1 The law of conservation of energy states that energy may neither be created nor destroyed. Therefore the sum of all the energies in the system.
Abstract The ATLSS (Across Trophic Level System Simulation) hierarchy of models is designed to utilize varying levels of detail and data availability to.
Models for the masses: bringing computational resources for addressing complex ecological problems to stakeholders Louis J. Gross, Eric Carr and Mark.
ATLSS SESI MODEL: Long-Legged and Short-Legged Wading Birds: Foraging Indices Louis J. Gross, University of Tennessee Provides a relative estimate of quality.
Population Biology: PVA & Assessment Mon. Mar. 14
Abstract The ATLSS (Across Trophic Level System Simulation) hierarchy of models is designed to utilize varying levels of detail and data availability to.
Effects of Intraspecific Competition on Varying Groups of Marigolds Tiffany Landis Microbiology Major Tennessee Technological University Cookeville, TN.
PROJECTING U.S. CROP YIELDS FOR COMPREHENSIVE BIOENERGY ASSESSMENTS historical trends, variability, and spatial clustering ABSTRACT The aggregate and distributional.
Defining Everglades Restoration Success* *i.e., “millions of crayfish” COMPREHENSIVE EVERGLADES RESTORATION PLAN John C. Ogden, Chief Scientist, Everglades.
Population Viability Analysis. Critically Endangered Threatened Endangered Criterion Reduction in population size 10 yrs 3 generations >80% >50% >30%
Chapter 52: Population Ecology 1.What is a population? -Individuals of a single species that occupy the same general area 2.What is the difference between.
Space, Relativity, and Uncertainty in Ecosystem Assessment of Everglades Restoration Scenarios Michael M. Fuller, Louis J. Gross, Scott M. Duke-Sylvester,
Modeling physical environmental impacts on survival: the SHIRAZ model Ecosystem based management FISH 507.
ATLSS Models for Predicting the Impact of Hydrology on Wildlife Populations in the Everglades Mangrove Zone of Florida Bay Jon Cline University of Tennessee.
Florida Fish and Wildlife Conservation Commission Invertebrate Communities as Tools for Establishing Minimum Flows and Levels in Florida Streams.
ATLSS Spatially Explicit Species Index (SESI) Models E. Jane Comiskey, Mark R. Palmer Donald L. DeAngelis, Louis J. Gross.
A model of the formation of fish schools and migrations of fish Simon Hubbard, Petro Babak, Sven Th.Sigurdsson,Kjartan G. Magnússon, ecological modeling,elsevier,2003.
Wildlife Response to Environmental Arctic Change November, 2008 Wildlife Conservation Society ABR Inc. UAF Institute of Arctic Biology UAF International.
PCB 3043L - General Ecology Data Analysis. OUTLINE Organizing an ecological study Basic sampling terminology Statistical analysis of data –Why use statistics?
Ecology & Organisms A survey of the most important concepts.
The ATLSS Vegetative Succession Model Scott M. Duke-Sylvester ATLSS Project : University of Tennessee Project web-site : .
BioComplexity: New Approaches to Big, Bad Problems, or the Same Old Dreck? Louis J. Gross The Institute for Environmental Modeling Departments of Ecology.
Integrated Ecological Assessment February 28, 2006 Long-Term Plan Annual Update Carl Fitz Recovery Model Development and.
Please note: this presentation has not received Director’s approval and is subject to revision.
5-1 ANSYS, Inc. Proprietary © 2009 ANSYS, Inc. All rights reserved. May 28, 2009 Inventory # Chapter 5 Six Sigma.
A Decision Making Tool for Sustainable Forestry: Harvest Patterns and Biodiversity Risk J.M. Reed 1, D.W. DesRochers 1, and S.H. Levine 2 1 Biology Dept,
Click on a lesson name to select. Population Biology Lesson 6.
1 Population Ecology. 2 Environmental Variation Key elements of an organism’s environment include: – temperature – water – sunlight – Soil – Classical.
Sources of Fish Decline Habitat disruption Breeding areas Larval development areas Bottom structure.
Parallel Landscape Fish Model for South Florida Ecosystem Simulation Dali Wang 1, Michael W. Berry 2, Eric A. Carr 1, Louis J. Gross 1 The ATLSS Hierarchy.
PCB 3043L - General Ecology Data Analysis.
Hydro-Thermo Dynamic Model: HTDM-1.0
1 MET 112 Global Climate Change MET 112 Global Climate Change - Lecture 12 Future Predictions Eugene Cordero San Jose State University Outline  Scenarios.
Uncertainty and Reliability Analysis D Nagesh Kumar, IISc Water Resources Planning and Management: M6L2 Stochastic Optimization.
Water Management Options Analysis Sonoma Valley Model Results Sonoma Valley Technical Work Group October 8, /08/2007.
Modelling population dynamics given age-based and seasonal movement in south Pacific albacore Simon Hoyle Secretariat of the Pacific Community.
1 Section 8.4 Testing a claim about a mean (σ known) Objective For a population with mean µ (with σ known), use a sample (with a sample mean) to test a.
CPUE analysis methods, progress and plans for 2011 Simon Hoyle.
 Occupancy Model Extensions. Number of Patches or Sample Units Unknown, Single Season So far have assumed the number of sampling units in the population.
Tracking life history of each particle Particles could be divided into three groups (Fig. 7) The red group’s period of copepodite stage shortened when.
Wading Bird Habitat Suitability:
Dynamics of Ecosystems: Population Ecology
Helsinki, Finland, November 2016
Variations of hydrogen in the thermosphere: nature and causes
David A. Dippold1, Robert T. Leaf1, and J. Read Hendon2
STABILIZING WORLD POPULATION
Across Trophic Level System Simulation
How can the science of ecological modeling be used as a management tool in assessing the relative impact of alternative hydrology scenarios on populations.
Foundations of Spatially Explicit Species Index Models
PCB 3043L - General Ecology Data Analysis.
HW due Thurs Read chapter 5 pg Do Making Connections pg.127
Across Trophic Level System Simulation (ATLSS)
South Florida Water Depth Assessment Tool (SFWDAT)
Department of Computer Science, University of Tennessee, Knoxville
Unit 2: Communities & Populations
Why Florida needs The University of Tennessee: Everglades Restoration, Computing, Mathematics and Public Policy Louis J. Gross The Institute for Environmental.
Environmental stressors affecting hot spots of marine biodiversity
Presentation transcript:

Sensitivity Analysis of a Spatially Explicit Fish Population Model Applied to Everglades Restoration Ren é A. Salinas and Louis J. Gross The Institute for Environmental Modeling, University of Tennessee, Knoxville, Tennessee ABSTRACT ALFISH is a spatially explicit population level model for freshwater fish in the south Florida Everglades. The model divides the region into 500m x 500m cells. The models objective is to predict fish biomass for wading birds under different hydrological scenarios associated with Everglades restoration. Determining model parameter sensitivity is critical to its reliability in assessing alternative scenarios. Two important model components are pond distribution and available lower trophic level resources. Pond distribution is important in providing fish refuges during periods when water level is low. Initial estimates suggest 34% of cells have ponds. ALFISH's sensitivity to pond distribution was assessed using alternative pond distributions and the same hydrologic data. Pond spatial distribution was varied to include spatial Poisson, uniform and clustered. Pond density was also varied. Food resources for the fish were varied using constant maxima and minima across season as well as two alternative, seasonally varying cases. Results suggest that ALFISH is more sensitive to pond spatial distribution than density, but lower trophic level concentration interacts with this. SENSITIVITY ANALYSIS An important analysis of a model is its sensitivity to parameters. In the case of ALFISH, there is no pond map for the entire restudy region. We wanted to know what aspects of pond densities the model was most sensitive to. We looked at variations in pond number and pond spatial distribution (Fig 2). A) 100%B) 50%C) 0% D) 25%PoissonE) 25% Uniform F) 25% Clustered Fig. 2: Pond maps used in sensitivity analysis. A) 100% of cells with a pond B) 50% of cells with a pond(Poisson) C) 0% (No ponds) D) 25% of cells with a pond (Poisson) E) 25% of cells with a pond (Uniform) F) 25% of cells with a pond (clustered Poisson) Fig. 1. ATLSS subregions associated with the restudy area of south Florida INTRODUCTION The ATLSS Landscape Fish Model (ALFISH)* is a spatially-explicit, size structured functional group model that estimates freshwater fish densities across the south Florida Restudy area (Fig. 1) for a given hydrological scenario associated with Everglades restoration. Fish densities are vital for the survival of endangered wading bird species. ALFISH divides the landscape into 500m X 500m cells and runs on 5-day increments. Two important aspects of the model are alligator hole and other pond type densities and lower trophic level resource concentration. Ponds act as refuges for fish as water level decreases (drydowns). Ponds are assumed to be 50m 2 in area. Our model assumes 5% of fish in a cell can enter ponds during a drydown, 5% can enter an adjacent cell, and 90% die. ALFISH assumes seasonal cycles of lower trophic level concentrations. OBJECTIVES Determine the effects of density and spatial distribution of alligator holes and other pond types to freshwater fish under a specific hydrologic regime in a spatially-explicit model of south Florida. Determine the effects of various concentrations of lower trophic level resources on freshwater fish under a specific hydrologic regime in a spatially-explicit model of south Florida. ALFISH divides lower trophic level resources into five categories and assumes a seasonal cycle for each (Fig. 3). We tested three alternative scenarios: 1) Constant with the maximum value for each type, 2) Constant with the minimum value for each type, and 3) Inverse seasonal cycle for each type. HYDROLOGY The Calibration Validation hydrology data set (Fig. 4) was used for the analysis. This data set, which runs from 1979 through 1995, most accurately reflects the history of actual water levels determined by the South Florida Management District. Fig. 3. Lower trophic level seasonal cycles used by ALFISH. Fig. 5.Time series graphs of fish density averaged yearly. The three graphs are defined as follows: 1) 1st Scenario, 2) (1st Scenario - 2nd Scenario), 3) 2nd Scenario. A) 100% difference 0% C) 50% difference 25%D) 25% Poisson difference 25% Clustered F) Maximum LTL difference Seasonal LTLE) Maximum LTL difference Minimum LTL RESULTS Because ALFISH is designed as a qualitative tool, it is most useful when comparing two scenarios. Table 1 shows the percent difference, (difference/2nd Scenario)*100, at three distinct years that involve fish density recovery from a drydown. Fig. 5 shows time series graphs comparing fish densities averaged over each year. Fig. 6 shows the spatial distribution of fish during a drydown. Table 1 CONCLUSIONS Our results suggest that the model must have ponds for the fish to survive (Fig 5A, Table 1). Within the range of 25% and 100% pond density, the percent difference, although significant, does not increase exponentially through time (Fig 5B and 5C, Table 1). This is important since in the absence of ponds the percent difference increases over time. This suggests that deviations from our current estimate of 34% pond density will not significantly affect qualitative results. Our results also show that pond spatial distribution can have a major effect on fish density (Fig. 5D, Fig. 6, Table 1). These results suggests that it is important that we understand where there may be large gaps in pond distribution. Finally, within the range of lower trophic level resource concentrations that we are using, the effects on fish densities are uniform through time and thus qualitatively similar (Figs. 5E and 5F, Table 1). Therefore, unless we intend to change the resource densities by an order of magnitude, the present seasonal cycle need not be changed. A) B) B) 100% difference 25% Fig. 4 A) 5 - Day increments B) Yearly Averages Fig. 6 Spatial difference map of the 25% Clustered and 25% Poisson pond scenarios showing total fish densities on October 13, *Gaff, H. et al A dynamic landscape model for fish in the Everglades and its application to restoration. Ecological Modelling 127: