curso lichos dia1

BEGIN WITH A DETOUR … NSB

Estimate Ecological Niche

No Silver Bullets in ENM

• Single algorithms may perform ‘best’ on average• The best algorithm in any given situation, however,

may be other than the ‘best’• NSB thinking suggests that we should not use a

single approach• Use a suite of approaches (e.g., as implemented in

OM, BIOMOD, BIOENSEMBLES, etc.), challenge to predict, choose best for that situation

• Maxent is good, but it is not the only algorithm …

COMPLEXITY OF MODELS AND OVERFITTING

Independent variables

Mod

eled

resp

onse

Independent variables

Mod

eled

resp

onse

Independent variables

Mod

eled

resp

onse

Independent variables

Mod

eled

resp

onse

Which Model is Best?

Which Model is Best?

Least calibration error

Most calibrationerror

Moderate calibrationerror

Which Model is Best?

Least calibration error

Most calibrationerror

Moderate calibrationerror

NEED INDEPENDENT DATA!

Five Goals of Niche Modeling

1. ESTIMATE THE FUNDAMENTAL NICHE2. ESTIMATE THE FUNDAMENTAL NICHE3. ESTIMATE THE FUNDAMENTAL NICHE4. ESTIMATE THE FUNDAMENTAL NICHE5. ESTIMATE THE FUNDAMENTAL NICHE

How Would the Fundamental Niche Look?

• In any one dimension, expected to be unimodal

• In multiple dimensions, expected to be convex• So, simple models are probably better• Need to take sampling and incomplete

representation into account carefully

http://d1vn86fw4xmcz1.cloudfront.net/content/royptb/367/1596/1665/F1.large.jpg

The Area of Distribution

G Physiological requirements

(Abiotic)

A

Favorable bioticenvironment

(Biotic)B

Accessible to dispersal(Movements)

M

Fundamental niche

Existing fundamental

niche

Realized ecological

niche

Existing niches are irregular, concave, and complex

Fundamental niches should be simple and convex

Maxent Model Responses

Current Methods• Popular methods fit highly complex objects to

estimate niches … but which niche?• Complex objects are more likely to correspond

to the existing niche, rather than the fundamental

Simplify:

• Complex response forms created by many current algorithms do not fit well with our understanding of the fundamental niche

• Simple, convex response forms may be much more appropriate as approximations to the fundamental niche

• This thinking will most likely require algorithms that can incorporate incomplete data, as well as uncertainty in inference, but fit simple response forms

Which Model is Best?

UNDERFITTING OVERFITTING

AIC: A Means of Choosing

Complex Models• Fit better with realized

niche and SDM• Risk overfitting• Frequently will be limited in

predictive ability

Simple Models• Fit better with ecological

niche theory• Risk underfitting• May not address the full

complexity of distributional ecology

THRESHOLDING MODELS

Absence DataAbiotic niche

Biotic interactionsAccessibility

“Presence”

E parameter

ENM Thresholding

• Least Training Presence Thresholding seeks the highest threshold level that includes all calibration data, or T100

• The T100 approach fails when there is error in the occurrence data set

• E is a summary of the expected proportion of data in the calibration dataset that will include significant errors

• And “Adjusted LTPT” approach would seek T100-E as a thresholding approach ideal for ENM

POST-PROCESSING MODELS

You Fit Your ENM… And Now?

• Niches to distributions• Consideration of dispersal (non-equilibrium

situations) • Add consideration of factors not able to be

incorporated directly in the model– E.g., land-use change, human presence

You Fit Your ENM… And Now?

• Niches to distributions• Consideration of dispersal (non-equilibrium

situations) • Add consideration of factors not able to be

incorporated directly in the model– E.g., land-use change, human presence

Potential DistributionAbiotic niche

Biotic interactions

Potential and Actual DistributionsAbiotic niche

Biotic interactionsAccessibility

Niches to Distributions

• In geographic space, distributions are the results of the BAM intersection:

GO = A ∩ B ∩ M• In “canonical” ENM, the model output is something

like A, or A∩B if the Eltonian Noise Hypothesis holds

• What does this say about the semantics?– Ecological niche modeling– Species distribution modeling

Niches to Distributions

GO = A ∩ B ∩ M• Need to restrict model outputs via some

hypothesis of M … post hoc?

Test Arena: The Lawrence Species

M and Model Training

M and Model Validation

Model Evaluation

M and Model Comparison

Model Comparison

The Area of Distribution

G Physiological requirements

(Abiotic)

A

Favorable bioticenvironment

(Biotic)B

Accessible to dispersal(Movements)

M

Sampling Effort

Interesting Result

• If model calibration is constrained to M, and if the Eltonian Noise Hypothesis holds, then …

GO = A• Simplifies the modeling process enormously,

and eliminates the need for post-modeling assumptions about M

You Fit Your ENM… And Now?

• Niches to distributions• Consideration of dispersal (non-equilibrium

situations) • Add consideration of factors not able to be

incorporated directly in the model– E.g., land-use change, human presence

What About Non-equilibrium Situations?

• Non-equilibrium:– Species does not inhabit the entire spatial

footprint of its habitable area• E.g., a projection of future-climate potential

distribution of a species• E.g., an invading species that has only established

populations in a small part of its potential range• E.g., a species being evaluated in terms of invasive

potential on other continents

• Need to bring in consideration of dispersal

https://youtu.be/dKdNP42B0Aw

You Fit Your ENM… And Now?

• Niches to distributions• Consideration of dispersal (non-equilibrium

situations) • Add consideration of factors not able to be

incorporated directly in the model– E.g., land-use change, human presence

Often Cannot Include Land Cover…

• Occurrence data are too old, or are heterogeneous temporally

• Occurrence data are not sufficiently finely resolved spatially to permit link to land cover

• Land use change is ongoing, such that there is not a single, stable state

• Solution: incorporate after model calibration

Adding Fine-scale Effects

• When ENMs cannot be calibrated at the fine resolutions of land use

• Can make the assumption that species have particular land-use associations, and that they will be found only under certain land use types

• The coarse, climate-based ENM output can be “clipped” by the finer-resolution land use outputs

town@ku.edu