Upload
rafer
View
28
Download
0
Tags:
Embed Size (px)
DESCRIPTION
Species-of-the-Week. Wood Duck ( Aix sponsa ). Brink of Extinction. By early 1900’s, culminative effects of: 1) wetland drainage (ag. expansion) 2) deforestation 3) overhunting. Habitat Wooded swamps & river bottomlands - PowerPoint PPT Presentation
Citation preview
Species-of-the-WeekWood Duck (Aix sponsa)
Brink of ExtinctionBy early 1900’s, culminative effects of:
1) wetland drainage (ag. expansion)2) deforestation3) overhunting
Habitat• Wooded swamps &
river bottomlands• Natural tree cavities for
nesting (cypress, sycamore, silver maple, black ash)
• Home range changes with flooding events
Food* In water <18”, feed on:
- seeds of trees (e.g., acorns)
- also field grains
* Young = aquatic insects
ReproductionPairing in late Oct into
spring (Mar-July nest)Clutch size = 6-10 eggsBehavior- Dump nests (up to 30+
eggs in 1 nest) = “egg dumping” behavior = intraspecific brood parasitism
- may decrease hatch rates to 10%
Factors Determining Patterns of Habitat Use
Concept of Habitat Selection
• Wildlife perceiving correct configuration of habitat needed for survival – differences based on age/experience/chance? –
• Niche concept
Concept of Habitat Selection
• Hutchison = n-dimensional hypervolume as explanation of the niche
• Fundamental vs. Realized Niche
Species 1Species 2
Testing the Hutchinsonian Niche Concept of Habitat Selection• James – work with birds in Arkansas…
quantified habitat relationships• How do birds select habitat?
• niche gestalt :
Wildlife Habitat Ecology & Mgt• Habitat from an evolutionary perspective
• Species distribution relative to habitat dist’n• Climatic events
• Pleistocene Epoch & dist’n of modern species
habitat interspersion – Leopold’s Law of Interspersion
Habitat Fragmentation1) gap formation2) decrease patch
size3) increase isolation4) increase edge5) conversion of
matrix
Concepts• Habitat = species-specific resources
available (relative quality)• Habitat Use = manner in which species
use resources• Habitat Selection = hierarchical decision
process (innate & learned) of what habitats to use
• Habitat Preference = based on selection of habitat, which are used more than others (preferred vs. avoided)
Concepts• Habitat Availability = accessibility of
resources• Habitat Quality = positive relation with
fitness (not just density)• Critical Habitat = resources essential to
the species….ESA designation….
Scale Dependence of Habitat Selection
1st Order2nd Order3rd Order4th Order
Macrohabitatvs.
Microhabitat
1st order – innate?
2nd order –decisions
3rd &4th order –decisions
Guild Concept• guild = group of species that exploit the
same class of resources in similar way• community guild = no taxonomic
restrictions; guild members chosen based on investigator-defined resources
• assemblage guild = guild members based on taxonomic relations
Models of Habitat Relationships• Model (assess) habitat for wildlife
species, e.g., USFWS• Habitat Suitability Index (HSI)
models- include top 3 environmental variables related to a species’ presence, distribution, & abundance
HSI = (V1 x V2 x V3)1/3 = 0 to 1
• Yellow Warbler HSI
for different forest conditions
HSI models• useful for representing possible major
habitat factors• true value as hypotheses• Do not provide information on:
- population size or trend- behavioral responses
• single-species approach
Emergence of Landscape Ecology
Equilibrium View
• Constant species composition
• Disturbance & succession = subordinate factors
• Ecosystems self-contained• Internal dynamics shape
trajectory• No need to look outside
boundaries to understand ecosystem dynamics
Structure
Function
?
?
?
?
Emergence of Landscape Ecology
Dynamic View
• Disturbance & ecosystem response = key factors
• Disturbance counter equilibrium
• Ecosystems NOT self-contained
• Multiple scales of processes, outside & inside
• Essential to examine spatial & temporal context
Structure
Function
Scale• What’s the big deal?• Seminal pubs
– Allen & Starr (1982) – Hierarchy: perspectives for ecological complexity
– Delcourt et al. (1983) – Quaternary Science Review 1:153-175
– O’Neill et al. (1986) – A hierarchical concept of ecosystems
Ecological Scaling: Scale & Pattern
• Acts in the “ecological theatre (Hutchinson 1965) are played out across various scales of space & time
• To understand these dramas, one must select the appropriate scale
Tem
pora
l Sca
le
Spatial ScaleFine
Shor
t
Coarse
Long
Recruitment
Treefalls
Windthrow
Secondary Succession
Species Migrations
SpeciationExtinction
Fire
Ecological Scaling: Scale & Pattern• Different patterns emerge, depending
on the scale of investigation
Am
eric
an R
edst
art
Least Flycatcher
Am
eric
an R
edst
art
Least Flycatcher
Local Scale(4 ha plots)
Regional Scale(thousands of ha)
Ecological Scaling: Components of Scale• Grain: minimum
resolution of the data– Cell size (raster data)– Min. polygon size
(vector data)
• Extent: scope or domain of the data– Size of landscape or
study area
Ecological Scale
• Scale characterized by:– grain: smallest
spatial resolution of datae.g., grid cell size,
pixel size, quadrat size (resolution)
Fine Coarse
Ecological Scale
• Scale characterized by:– extent: size of
overall study area (scope or domain of the data)
Small Large
Ecological Scaling: Components of Scale
• Minimum Patch Size: min. size considered > resolution of data (defined by grain)
Ecological Scaling: Definitions• Ecological scale & cartographic scale are exactly opposite
– Ecological scale = size (extent) of landscape– Cartographic scale = ratio of map to real distance
Scale in Ecology & Geography
• ecological vs. cartographic scale
Ecology Geography
Small(Fine)
Fine resolutionSmall Extent
Coarse resolutionLarge Extent
Large(Broad)
Coarse resolutionLarge extent
Fine resolutionSmall extent
Scale in Ecology & Geography
• ecological vs. cartographic scale– e.g., map scale
1:24,000 vs. 1:3,000fine vs. coarselarge vs. small extent
1:24,000
1:200,000
Ecological Scaling: Components of Scale• Grain and extent are
correlated• Information content
often correlated with grain
• Grain and extent set lower and upper limits of resolution in the data, respectively.
Ecological Scaling: Components of Scale• From an organism-
centered perspective, grain and extent may be defined as the degree of acuity of a stationary organism with respect to short- and long-range perceptual ability
Ecological Scaling: Components of Scale• Grain = finest
component of environment that can be differentiated up close
• Extent = range at which a relevant object can be distinguished from a fixed vantage point
Fine CoarseScale
ExtentGrain
Ecological Scaling: Components of Scale• From an anthropocentric
perspective, grain and extent may be defined on the basis of management objectives
• Grain = finest unit of mgt (e.g., stand)
• Extent = total area under management (e.g., forest)
Ecological Scaling: Components of Scale• In practice, grain and extent often dictated by scale of
available spatial data (e.g., imagery), logistics, or technical capabilities
Ecological Scaling: Components of Scale• Critical that grain and extent be defined for a study and
represent ecological phenomenon or organism studied.• Otherwise, patterns detected have little meaning and/or
conclusions could be wrong
Scale: Jargon• scale vs. level of organization
Space - Time
Space - Time
Space - TimeIndividual
Population
Community
Ecological Scaling: Implications of Scale• As one changes scale, statistical relationships may
change:– Magnitude or sign of correlations– Importance of variables– Variance relationships
Implications of Changes in Scale
• Processes and/or patterns may change• Hierarchy theory = structural
understanding of scale-dependent phenomena
ExampleAbundance of forest insects sampled at different distance Intervals in leaf litter,
Implications of Changes in Scale
05
1015202530354045
PredatorPrey
Insects sampled at 10-m intervals for 100 m
Implications of Changes in Scale
05
1015202530354045
PredatorPrey
Insects sampled at 2000-m intervals for 20,000 m
Identifying the “Right” Scale(s)
• No clear algorithm for defining • Autocorrelation & Independence • Life history correlates • Dependent on objectives and organisms
• Multiscale analysis!• e.g., Australian leadbeater’s possum
Multiscale Analysis
• Species-specific perception of landscape features : scale-dependent– e.g., mesopredators in Indiana
• Modeling species distributions in fragmented landscapes
Hierarchy Theory
• Lower levels provide mechanistic explanations
• Higher levels provide constraints
Scale & Hierarchy Theory
• Hierarchical structure of systems = helps us explain phenomena–Why? : next lower level–So What? : next higher level
• minimum 3 hierarchical levels needed
Constraints (significance)
Level of Focus (level of interest)
Components (explanation)