Occupancy models

Mrs. Young | July 17, 2025

This method also belongs to Ecology-guided Modelling.

Table of Contents

1. Description & principle
2. Reference articles
3. Packages
4. Assessment table

Description & principle

A clear, technical yet accessible explanation of the method, its core principle(s).

Major variants

optional

If the method has variants that seem important, either already widespread or promising and well documented.

Further online resources

References to useful online resources to get started, e.g. explanation blogs

Reference articles

Method

• One or a few key academic references that introduce or formalize the method.

Research applications

With RS data in Ecology / Biodiversity

• A

Without RS data (Ecology domain)

optional

• B

Packages

Python

R

Code Cells

optional

Assessment table

Category	Criteria	Assessment
Outcome	Objective	Effect estimation, Predictive task + interpretability, Detection, Scenario projection
	Estimand	CATE
	Validity	Moderate confidence
Data compatibility	Type	Panel data (many samples)
	Required TS length	≥ 10
	Handles few samples	10 to 100
	Handles huge datasets (n)	Yes
	Handles missing data	Partially
	RS-data proven	Few applications
Assumptions	Functional form	Linear, Quadratic, Non-linear, Additivity, Assumption-free
	No unobserved confounders	Recommended
	No interference	Recommended
	Well-defined treatments	Recommended, Relaxes assumption
	Common support (positivity)	Recommended
	Causal Markov Condition	Required
	Faithfulness	Required
	IDD	Relaxes assumption
	Model specific assumption	Parallel trends, Stationarity, Normality of random effects, Good covariate balance, Instrument validity
Model properties	Requires explicit processes	Optional
	Exposure type	All
	Number of variables	Multivariate
	Handles lag effects	Possible
	Propagates uncertainty	Inherent capacity, Model-specific tools
	Parametric nature	Parametric, Semi-parametric
Packages	Language	R, Python
	Usage	User-friendly, Technical but well documented

References