WL Habitat Conservation Final

Course Description

Application of principles of wildlife biology to conservation and management of wild bird and mammal habitats including field applications

Themes of Defining Wildlife

1. Not easy to define

2. Inconsistent definition because of human importance bias

3. Must be alive (no viruses)

4. Wild/Non-domesticated

Wildlife

Non-domesticated terrestrial and aquatic vertebrate species.

Population Growth Rate Equation

Nt+1 = Nt * lambda

BIDE Model of Population Change

Nt+1 = Nt+B+I-D-E

Population Change by Survival/Reproduction

Nt+1 = N*S + N*R

Rule of survival and reproduction

1 = S + R (also lambda = S+R)

r Selection

Fast, emphasis on reproduction, high pressure/harvest

K Selection

Slow, emphasis on survival of adults, low pressure/harvest

Natural Selection

Occurs when individuals with certain genotypes are more likely than others to survive and reproduce, and thus pass on their alleles to the next generation.

Fitness

Total number of offspring an individual adult recruits into the next generation. Made up of fecundity, recruitment, and adult survival.

Conservation

The management of natural resources so it may yield the greatest sustainable benefit to present and future generations.

Principles of Conservation (5)

1. Organism biodiversity is good

2. Untimely extinctions are bad

3. Ecological complexity is good

4. Evolution should be allowed to continue

5. Biodiversity has intrinsic value

North American Model of Wildlife Conservation (7)

1. Wildlife resources are a public trust

2. Markets for game are eliminated

3. Allocation of wildlife is by law

4. Wildlife can be killed only for legitimate purpose

5. Wildlife is considered an international resource

6. Science is the proper tool to discharge wildlife policy

7. Democracy of hunting is standard

Management

The science and art of making decisions and taking actions to manipulate the structure, dynamics, and relations of wildlife populations, habitats, and people to achieve specific human objectives.

Ecosystems

Similar regions within a biome of similar environmental conditions, plants, and animals

Communities

Biotic groupings of plants and other organisms within ecosystems

Factors Limiting Geographic Range (4)

1. Competition with other species

2. Ability to disperse

3. Adequate food and cover resources

4. Environmental tolerance

Habitat

The suite of resources and environmental conditions that determine the presence, survival, and reproduction of a population.

Habitat vs Niche

Habitat is physical place, how environment impacts organism, is GEOGRAPHIC space

Niche: how organisms fit into ecosystem, how species use and affect environment, ENVIRONMENTAL space

Niche

The ecological conditions that a species requires to maintain populations in a given region, together with the impacts that a species has on its resources, other interacting species, its habitat, and environment.

Habitat Use

Demonstrated presence of a particular habitat in an animal or population’s behavioral repertoire

Habitat Selection

A hierarchical process involving a series of innate and learned behavioral decisions made by an animal about what habitat it would use at different spatial scales

Writing equation for multiple interactions

= B0 + B1(length) +B2(species) + B3(species)(length)

Scaling covariates

New x = current x - mean(x)

Intercept is now mass at the mean length while slope is the same

Z-standardizing across covariates

[x-mean(x)]/sd(x)

Biodiversity

Measures the amount of variety in organisms

Biogeography

Spatial patterns of biodiversity

Island Biogeography Theory (IBT) by MacArthur and Wilson

The number of species of a given taxon that become established on an island represents a dynamic equilibrium controlled by the rate of immigration of new species and the rate of extinction of previously established species.

Habitat surrounded by inhospitable matrix and biodiversity influenced by size/proximity of patches

Island Biogeography

The study of the species composition and survival rate on islands

Equilibrium species number

The species richness if an island at which immigration balances extinction, and which remains roughly constant

Drivers of fragmentation vulnerability (7)

1. Wide-ranging species (wolves)

2. Poor dispersers/poor mobility (beetles/butterflies)

3. Species with specialized requirements and temporary habitat (like stop-over sites)

4. Large-patch/patchily-distributed or interior species (pandas, spotted owls)

5. Species with low fecundity or recruitment or adult survival (grizzlies)

6. Species vulnerable to human exploitation/persecution

7. Sensitive to disturbance (wolverines)

Comparative Cost Benefit Analysis Steps (3)

1. Calculate the costs

2. Calculate the benefits

3. Pick the best choice

Reasons Cost-Benefit is Complicated (4)

1. Hard to assign monetary values to ecosystem services

2. Self interest

3. People lie

4. People forget

Cuyahoga River

“Business vs. Environmental Regulations” - False equivalency

Historically: River fires were common due to oil contamination

Went on for over 100 years until environmental legislation was passed

Present: still have spills because of industry around the river

Project Chariot

1950s plan by feds to build artificial harbor in Alaska, using nuclear bombs

Local tribes displeases and UAF concluded fallout would harm terrestrial and aquatic ecosystems

Known Known

We understand it’s an issue, and understand the issue

ex: job cost, building costs,…

Known Unknown

We understand it’s an issue and that we don’t fully understand the issue

ex: environmental impacts of projects

Unknown Unknown

We don’t even realize it’s a problem, nor do we understand it

ex: Whiskey fungus on town, Berkeley pit

Ecosystem Services Worth

Highly complicated

Estimate of $10/person/acre/year

Possible over $125 trillion (more than global GDP)

Uses of Cost-Benefit Analysis

1. In conjunction with power analyses, useful for allocating funds on wildlife research

2. In conjunction with parameter estimates from research to determine which management actions are the most cost effective

Precautionary Principle

An approach to risk management, where, if it is possible that a given policy/action might cause harm to the public or the environment (and if there is still not scientific agreement on the issue), the THE POLICY OR ACTION IN QUESTION SHOULD NOT BE CARRIED OUT.

Cost/Benefit Timescale Differences

Often, see lots of benefits shortly after action, with the costs not setting in for years to decades

Problems with Limited Research Funding (2)

1. Make sure doing something useful with the money

2. Make sure we don’t “over do” it

Standard way sample size is determined

= ($ available - salary - equipment)/per unit cost

Power Analysis

Used to determine the most cost=effective sample size for an experiment or observational study given a required significance level, statistical power, and effect size.

Before-After-Control-Impact Design

Used to study ecological responses in large experimental units for which replication is difficult or impossible.

Two units are monitored over time; one unit receives an intervention at some intermediate time, while the other is left as an undisturbed control.

The pre-intervention differences in the response between units are compared to the post-intervention differences, with a large disparity interpreted as evidence of an effect of the intervention.

BACI Variables

Resulting treatment value is a combination of:

Beta-0: intercept (starting, control value)

Beta-1: Site effect (difference between initial control and initial treatment)

Beta-2: Year effect (difference between initial control and next control)

Beta-3: Treatment effect

Considerations for Power Analysis (3)

1. Increasing sample size increases precision and power

2. The ‘right’ sample size may be a function of cost

3. The right answer might be ‘don’t do the project’

Benefits of Power Analysis (4)

1. Confidence in study

2. Competitive advantage for sources of funding

3. Bargaining power with funding agencies

4. More thorough understanding of what your analyses will look like, and how you should format your experiment

Path to Overabundance- Snow geese

1. 1950-1990 Increased survival/fecundity through increased food availability (waste rice and corn) leading to population explosion

2. Decimation of arctic ecosystems

3. 1998 Conservation Order, unlimited spring bag w/ shotguns and electronic calls w/ goal of 50% reduction by 2005

4. Estimates show effective for 10% of the population (southern breeding)

5. Lead to drop in juvenile survival and fidelity

Why did snow goose harvest efforts fail to reduce goose populations? (3)

1. Underestimation of abundance

2. Underestimation of harvest potential (geese good at avoiding harvest)

3. Harvest of adults was not completely additive (compensatory)

False Positives

Unsupported correlations, “leaf bird”, wrong tag ID reported

False Negatives

Fail to detect elements that are present

Elements Controlling Observation Potential (2)

1. Availability for detection (is it there, binary)

2. Detection Probability Given Availability (how easy to observe)

Zi - Latent State

The ecological truth of if animals present or not

Bernoulli of psi

Yij - Data

Was the animal actually observed at instance ij.

Zi * probability of detection

Outcomes of Z/Y interactions (4)

1. Z = 0, Y = 1: False Positive

2. Z = 0, Y = 0: Truth

3. Z = 1, Y = 1: Truth

4. Z = 1, Y = 0: False Negative

Pij

Probability of detecting an individual at a site (i) on a specific visit (j)

Pi*

Probability of detecting an individual at a site across all of the multiple visits

Pi* Equation

= 1- PRODUCT(1-Pij)

Categorical Covariates

Binary or categories such as degraded/restored that have set constraints

Continuous Covariates

Variables that are of a non-categorizable distribution, such as island size and distance from mainland

Occupancy probability and equation (psi)

logit(psi) = Beta0 + Beta1*xi

B1 is the influence that the presence of the confounding variable has on occupancy

Where xi is confounding variable (restored/grazed)

Graphed on x with y being proportion of sites at that state with occupancy

Detection probability and equation

logit(Pij) = Alpha0 + Alpha1*xi

A1 is the influence that the presence of the confounding variable has on occupancy

Where xi is confounding variable (restored/grazed)

Graphed on x with y being proportion of sites at that state with occupancy*pij

Relationship between pij and p*

p* initially increases rapidly with p increasing before leveling off to 1

Scale and Surveys

Want to see what occupancy can tell us about population dynamics, but home ranges can fluctuate temporarily and spatially. Ideally want to match home ranges.

Island Biogeography Case Study on Observational Studies Steps (5)

1. Make a bunch of fake islands varying randomly by size and distance to mainland

2. Create the occupancy probability model as:

   1. logit(psi) = Beta0 +Beta1*size + Beta2*distance

3. Calculate the occupancy probability

4. Simulate occupancy for each island using respective probability

5. Simulate island surveys overtime on each island at some p

Effect of p on precision

The closer p is to 0.5, the less precise the model becomes

How to get Psi from Logit(Psi)

= (e^x)/(1+e^x)

Markovian Process

Probability of events depends on previous events or states

Fate Trees

Helps determine probability of next-time events based on current events using Persistence, Colonization, Extinction…

Persistence Probability (Phi)

The probability that a population that is present at a site will still be at the site at the next time-step

= 1- extinction probability

Colonization Probability (Y, upsilon)

The probability that an unoccupied site will be colonized between t and t+1

= 1-non-colonization probability

Effect of Markovian Process on Latent State (now Zi,t)

= Bernoulli(Zi,t-1 * Phi i,t-1 ((1-Zi,t-1) * Upsilon i,t-1)

Two elements because can either persist or be colonized and that depends on if was present in the previous time-step

Notes for persistence and colonization probability

Persistence based only on island size (logit(Phi) = B0,phi + B1,phi*size)

Colonization based only on distance to mainland (logit(Upsilon) = B0,up + B1,up *size)

How to Alter Metapopulation Preferences/Groups

Trap → Sink, remove cue (drop habitat preference)

Trap→ Source, improve habitat (improve quality)

Perceptual Trap/Undervalued Resource → Source, introduce cue (improve preference)

Wolf Monitoring

1. Field-Intensive (verify packs, counts, collars)

Issues with Intensive Monitoring

1. Challenging

2. Costly

3. Less funding

4. No longer Viable

5. Minimums don’t reflect full picture of population

Patch Occupancy Model

1. Divide region into grid with cells the size of a pack’s range

2. Classify with presence/absence using…

   1. Uncertain detections (public wolf sightings)

   2. Certain Detections (field monitoring and pack locations)

3. Create week-by-week ENCOUNTER HISTORIES to estimate probability of occupancy

4. Use Territory Size and Pack Size to estimate number of individuals

Integrated Patch Occupancy Model Sub-models (3)

1. Occupancy Model

2. Territory Model

3. Group Model

Territory Covariates (4, 1 max, 3 min)

Maximize:

1. Food (higher density, smaller territory)

Minimize:

1. Competition (higher density, smaller territory)

2. Travel

3. Mortality Risk (human density increase, smaller territory)

Suggests wolves select ECONOMICALLY

Pack Size Covariates (4)

1. Prey abundance and vulnerability (higher density, smaller size)

2. Pack densities (higher density, higher size)

3. Mortalities/Risk Factors (more removals/hunting, smaller size)

4. Terrain (higher ruggedness, smaller size)

Wolf iPOM Model Elements

1. Occupancy Model: area occupied

2. Territory Model: territory size

3. Group Model: Pack size*#packs*dispersers/loners

Integrated Patch Occupancy Model Benefits (4)

1. Requires limited data possible

2. Improved accuracy over outdated counts

3. Predicts over many spatial scales

4. Biologically based

Compensatory Mortality

Removal of animals that were more likely to die anyways or less likely to contribute to population growth (males)

Increases number of resources for remaining individuals

Compensatory Natality

Removing animals increases fecundity of remaining animals

Spotted Owls

High pair and site fidelity, high adult survival, BUT low fecundity

“Nest” in shallow depressions of existing structures like debris platforms or tree deformities, in stands with high canopy closure

Timeline of Spotted Owl Conservation

1977 preservation of old growth forests became a priority

1981 calls for 1000-acre buffer around every owl

1990 Listed as threatened and declines persist. Caused massive timber logging declines. Emergence of barred owl encroachment

Issues Facing Spotted Owls (3)

1. Climate change

2. Habitat loss

3. Interspecific competition

Ecosystem resilience

The ability for an ecosystem to persist through and/ or regenerate after a disturbance event.

What Makes Fire (3)

1. Heat

2. Oxygen

3. Fuel

Is a self-sustaining chemical reaction

Elements for Fire Regimes Across Time and Space

1. Microsite, Seconds (Flame): Heat, Oxygen, Fuel

2. Wildfire, Days (Fire Behavior): Weather, Topography, Fuel

3. Regional/Global, Decades (Fire Regime): Climate, Ignition, Vegetation

Benefits of Fire (6)

1. Clean/clear landscape

2. Ceremonial/Medicinal

3. Manipulate Plant Traits

4. Pest Management

5. Animal Gathering

6. Prevent Destructive Fires