slide1 l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Estimating Seed and Invertebrate Production to Predict Waterfowl Carrying Capacity in Moist-soil Habitats PowerPoint Presentation
Download Presentation
Estimating Seed and Invertebrate Production to Predict Waterfowl Carrying Capacity in Moist-soil Habitats

Loading in 2 Seconds...

play fullscreen
1 / 22

Estimating Seed and Invertebrate Production to Predict Waterfowl Carrying Capacity in Moist-soil Habitats - PowerPoint PPT Presentation


  • 265 Views
  • Uploaded on

Estimating Seed and Invertebrate Production to Predict Waterfowl Carrying Capacity in Moist-soil Habitats Matthew J. Gray James T. Anderson, Richard M. Kaminski, and Loren M. Smith What is Carrying Capacity?

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Estimating Seed and Invertebrate Production to Predict Waterfowl Carrying Capacity in Moist-soil Habitats' - paul


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
slide1

Estimating Seed and Invertebrate Production to Predict Waterfowl Carrying Capacity in Moist-soil Habitats

Matthew J. Gray

James T. Anderson, Richard M. Kaminski, and Loren M. Smith

what is carrying capacity
What is Carrying Capacity?

“Maximum number of individuals that can be ‘sustained’ in a given ‘habitat’ for a ‘given amount of time’.”

(Krebs 1993)

“There is a maximum wild density of individuals that can be supported in the environment” (Leopold 1931, Stoddard 1931,Leopold 1933)

“…the heaviest quail/muskrat population that can be expected to survive winter,” is dependent on the “…availability of suitable food and cover...” (Errington 1934, 1936, 1945, 1947)

carrying capacity of food resources

“The amount of food for each species…gives the extreme limit to which ‘a population’ can increase…” (Darwin 1909, The Origin of Species)

Carrying Capacity of Food Resources

“… carrying capacity of food ‘resources’ …” (Leopold 1933)

“… the quantity of food necessary to feed 1 duck for 1 day… ” is equivalent to 1 duck use-day. (Prince 1979, Reinecke et al. 1989)

quantifying waterfowl use days
Quantifying Waterfowl Use-Days

Prince 1979

Reinecke et al. 1989

Reinecke and Loesch 1996

Food Available (g [dry]) x MTE (kcal/g [dry])

WUD =

Daily Energy Requirement (kcal/day)

Available Food for Waterfowl

MTE Constants

DER Constant

Moist-soil Seeds

2.5 kcal/g

292 kcal/day

Aquatic Invertebrates

3.5 kcal/g

why estimate waterfowl use days

Why Estimate Waterfowl Use-days?

To Predict Wetland-Specific Waterfowl Carrying Capacity

  • To Evaluate Management Practices
slide6

Collecting

Sorting

Estimating Seed and Invertebrate Production

Seeds

Invertebrates

Clipping

Field Work

Threshing

Lab Work

Specialized Equipment

Nets, Clippers, Refrigerated Storage, Sieves, Sorting Trays, Dryer, Desiccator, Balance

estimating seed yield of moist soil plants using multiple linear regression

Estimating Seed Yield of Moist-soil Plants using Multiple Linear Regression

Yi = ß0 + ß1 X1i + ß2 X2i + ••• + ßk Xki +εi

Dependent Variable

(Laubhan and Fredrickson 1992)

Y

IL

Seed Yield (g)

IL

Predictor Variables

ID

ID

Phyto-morphological Measurements (cm)

research justification

1)

Plant morphology can vary spatially and temporally

Yi = ßk Xki +εi

Research Justification

2)

Measuring multiple floristic variables can be tedious and confusing

3)

Equations for aquatic invertebrates have not been developed

research objectives

1)

Evaluate Laubhan and Fredrickson’s method in a different location and in different years

Research Objectives

2)

Evaluate different phyto-morphological measurements as predictor variables

3)

Develop a new method to predict seed yield of moist-soil plants with a single, easily measured variable

4)

Develop new regression equations to predict aquatic invertebrate biomass

study areas and years

Prisock Moist-soil Management Complex

Study Areas and Years

(1993, 1994)

Mississippi

Noxubee National Wildlife Refuge

Southern High Plains

12 Playa Lakes Floyd County

Texas

(1994, 1995)

slide11

Flower Width & Length

Pedicel

Methods:Plant Morphological Study

5 species::Echinochloa crusgalli, Cyperus erythrorhizos, Polygonum hydropiperoides, Panicum dichotomiflorum, Rynchospora globularis

n = 60 plants/species/year, 1993 and 1994

L & F (1992)

New Variables

  • Plant Height
  • Inflorescence Length
  • Infl. Base Diameter
  • Infl. Volume
  • # of Inflorescences
  • Number of Pedicels
  • Number of Flowers
  • Flower Width
  • Flower Height

Seed Processing followed L&F (1992)

slide12

Methods:Dot Study

5 species::Echinochloa crusgalli, Setaria viridis, Panicum agrostoides, Panicum dichotomiflorum, Rynchospora globularis

n = 30 plants/species/year, 1994

Preparation

Processing

  • Plant Press
  • 7 days
  • Room Temperature
  • Pedicels Separated
  • Dot grid (9 dots/cm2)
  • Dots Obscured by Seed Counted

Seed Processing followed L&F (1992)

slide13

Methods:Aquatic Invertebrate Study

Invertebrate Collection and Processing

Water-Column (5-cm diameter)

Epiphytic Sample (0.25-m2 plot)

Benthic Core (5-cm diameter)

  • 20 subsamples/playa
  • 2 sampling episodes/week
  • September-January
  • Sorted and identified
  • Dried to constant mass
  • g dry inverts/playa/week/m2
slide14

Methods:Aquatic Invertebrate Study

Predictor Variables

Water Variables:

  • Conductivity
  • Dissolved Oxygen
  • Temperature
  • pH
  • Water Depth

Induced Variables:

  • Inundation duration
  • Treatment (managed, unmanaged)
statistical analysis
Statistical Analysis

Simple and Multiple Linear Regression

Assumptions:

  • Residual Normality
  • Residual Homoscedasticity

Residual Plots & Outlier Diagnostics

Model Development:

All-possible Variable Selection Procedure

  • Greatest R2adjusted
  • Lowest MSE
  • Mallow’s Cp  p
  • Greatest R2predicted (PRESS)

Multicollinearity:

VIF > 10, EV  0, CN > 10

results and discussion

OKAY!!!

HERE’S THE GOOD

STUFF!!

Results and Discussion

seed prediction results 4 models

Our Data L & F

Best Model

L & F (1992)

Dot Model

R2adjusted

Seed Prediction Results: 4 Models

0.68-0.920.78-0.970.79-0.96 0.92-0.97

R2predicted

0.23-0.880.31-0.97NAV 0.91-0.96

MSE

0.002-0.39 0.001-0.18 NAV 0.001-0.009

Cp

48.2-495.0 3.9-6.6 NAV NAP

VIF

1.1-34.83.9-12.0 NAV NAP

NAV= Not Available,NAP = Not Applicable

slide18

Invertebrate Prediction Results (Single Variable Models)

R2adjusted

R2predicted

MSE

  • Increasing p,Increased R2< 0.03
  • Increasing p,Increased VIF > 10

Conductivity

0.6040.582 333.14

Treatment

0.5870.562 347.48

pH

0.5810.564 352.83

DO

0.4940.483 426.40

Depth

0.4690.451 449.09

Time

0.3960.379 508.49

Temperature

0.3710.365 529.34

summary of results

Summary of Results

Simple linear regression models can explain as much variation in seed yield and aquatic invertbrate biomass and predict as well or better than multiple regression models.

Seed (g) = 0.003 x DOTS

Seed Yield/

Invert Biomass

Inverts (g) = 0.023 x COND

Dots Obscured/Conductivity

management applicability

(Single-variable Models)

Management Applicability

Easy

Fast

Reasonable Cost

  • Plant Press ($35)
  • Dot Grid ($10)
  • Conductivity/pH Meter ($50-350)
future research needs

Future Research Needs

Additional Models

Spatial Replication

Systems, Regions

Temporal Replication

funding agencies

Waterways Experiment Station (Wetland Research Program)

Funding Agencies

U.S. Fish & Wildlife Service

Regions 2 and 4

U.S. Geological Survey

U.S. Army Corps of Engineers

Biological Resources Division

Texas Tech University

Mississippi State University

Department of Range, Wildlife, and Fisheries

Department of Wildlife and Fisheries