1. InVEST Biodiversity: Habitat Quality
San Rafael falls, Yasuni, Ecuador – one of the most biodiverse places on the planet

2. Habitat Quality: Model Overview
Biodiversity is not treated as an ecosystem service
Instead, it’s used to assess overlaps and tradeoffs
InVEST models habitat quality and rarity as indicators of the status of biodiversity
Areas with high quality are generally better able to maintain biodiversity
Overlaps and tradeoffs among biodiversity conservation, ecosystem service provision, and other land uses
Useful for
- Assessing conservation status of landscape where species presence/absence data are scarce (especially for future scenarios)
- assessing changes to species habitat under different scenarios
Habitat quality: Ability of environment to provide conditions for appropriate individual and population persistence
Habitat rarity: The relative commonness of the habitat relative to the baseline land use scenario
Areas with decreasing habitat extent and quality are likely to see a decline in biodiversity resilience, persistence, breadth and depth

3. Habitat Quality: Model Overview
Habitat quality depends on:
Suitability of the habitat for the species of interest
Does it prefer grassland, open canopy forest
or closed canopy forest?
Proximity and intensity of threats
Proximity: how far away is the threat?
Intensity: how severe a threat is it?
In this model, we can consider a single specific species, a more general group of species (like forest birds) or even larger categories like all mammals, or biodiversity in general, not species-specific.
Generally human threats.
Two major categories of threats: habitat loss and fragmentation; pollution, roads, cities etc.

4. Habitat Quality: Threats
Degradation of habitat depends on:
Distance between habitat and threat
Relative weight of threat
Are cities more damaging than roads?
Sensitivity of habitat to the threat
Is forest more sensitive to roads than a coffee plantation would be?
How quickly the impact decays with distance
Accessibility / Protection status
Relative weight: Are mines more damaging than roads?
Sensitivity of habitat: Forest might be more sensitive to a road than a coffee plantation
Distance decay: in this model can choose between linear and exponential
Protection status: The better protected the habitat is, the less likely it is to get degraded by illegal harvesting, hunting etc. This does not help threats like air pollution though.

5. Habitat Quality: Model Inputs
distance
impact
Threat
distance
National
Park
Habitat
Not habitat
distance
impact
Threat

6. Habitat Quality: Land Cover
Land cover map
Current, future, baseline
Suitability
Sensitivity
Land cover: Current, past, baseline
Suitability, sensitivity, accessibility, threat weights are scored from 0 to 1.
Threats require a table listing the threats, threat weights, and spatial impact
- Spatial impact of threats is either linear or exponential, and indicates how quickly the threat decays over space and what is the maximum distance that a threat can affect a habitat.
Also rasters of the location and intensity of the threats (presence/absence or weighted)
All threat quantities should be given in the same scale and metric. (presence/absence, density – kilometers etc)
Threat weight applies to all habitats
Weight differs only if the relative values differ (.1, .1 and .4 are the same as .2, .2 and .8)
Accessibility: Map of how accessible the land is – are there legal protections, physical barriers (elevation etc) against threats?
Half saturation constant: Used to create habitat quality from habitat degradation. It should be set to half of the highest degradation score, to get the greatest variation on the 0-1 scale for habitat quality (useful for visualizing the result.) Doesn’t change the ranking order for habitat quality, just the spread. Use the same value for all runs that involve the same landscape (comparing alternative scenarios.)

7. Habitat Quality: Sensitivity table
Example from InVEST sample data

8. Habitat Quality: Threats
Roads (prds)
Threat table
Maps
Urban (urb)
Threats require a table listing the threats, threat weights, and spatial impact
- Spatial impact of threats is either linear or exponential, and indicates how quickly the threat decays over space and what is the maximum distance (km)that a threat can affect a habitat.
Also rasters of the location and intensity of the threats (presence/absence or weighted)
All threat quantities should be given in the same scale and metric. (presence/absence, density – kilometers etc)
Threat weight applies to all habitats
Weight differs only if the relative values differ (.1, .1 and .4 are the same as .2, .2 and .8)
- Decay – 0 for exponential, 1 for linear
Half saturation constant: Used to create habitat quality from habitat degradation. It should be set to half of the highest degradation score, to get the greatest variation on the 0-1 scale for habitat quality (useful for visualizing the result.) Doesn’t change the ranking order for habitat quality, just the spread. Use the same value for all runs that involve the same landscape (comparing alternative scenarios.)
Decay rate
impact
distance
MAX_DIST (km)

9. Habitat Quality: Accessibility1 .8 .2

10. Habitat Quality: Assigning values.5 1
Species will not
live in habitat
Species may
live in habitat
Preferred
habitat
Suitability
Habitat is not
sensitive to threat
Habitat is somewhat sensitive
Habitat is
very sensitive
Sensitivity
Can give any value between 0 and 1 or can do a binary 0 or 1 for suitable/unsuitable etc.
Can give values for overall biodiversity or a specific species or group (forest birds, for example), then the output will only apply to that species/group.
This information should come from experts (conservation biologists), literature review
Somewhat
protected
Access
Inaccessible
Not protected
Threat
weight
Low impact
on habitat
Some impact
on habitat
High impact
on habitat

11. Habitat Quality: Model Outputs
Habitat degradation
relative to rest of landscape
Habitat quality
Habitat quality (0-1): Values closer to 1 mean better habitat quality
Habitat degradation: Positive integer values. As degradation increases, quality decreases. Used to set the half saturation constant.
Habitat rarity (0-1): Given relative to a baseline (past or ideal) map. Values closer to 1 mean that the habitat has the greatest decrease in extent.

12. Habitat Quality: Limitations
Threats treated as additive
Habitat has artificial boundary
Threats treated as additive: Sometimes the collective effects of threats are larger than the simple sum would suggest
Habitat has artificial boundary: Modeled habitat is typically within a larger landscape, so the effects outside this boundary are not taken into account. Can improve this by analyzing a larger landscape around the area of interest.
Patch size/connectivity: Small or isolated patches may not actually be suitable for a species (like large mammals)
Does not consider patch size or connectivity

13. Habitat Quality: Freeport
Model Outputs from Invest Biodiversity 3.0 – (2.4.3)
Future scenarios are based on 1 meter of sea level rise by the year 2100
Each scenario used 12 habitat threat layers – 8 static and 4 dynamic
Habitat stressor levels were based on prior published research efforts
Degradation tables show mean scores by habitat (higher values = more degradation)
Habitat quality is summed for all habitats (higher values = better quality)
Habitat degradation and/or quality is used as a proxy for biodiversity

14. Habitat Quality: Freeport
HABITAT STRESSORS /THREATS
ALLRD – all public roads
BEACH – beach areas accessible to human uses/trampling
COMFISH – commercial fisheries
DEVLND – developed land
FACIL – industrial facilities
LEVEE – existing levees
PORT – port infrastructure and facilities
RAIL – railroads
RECFSH – recreational fisheries
REFIN – oil refineries
SHIP – shipping lanes
WASTE – waste water outflows
Threats is red are dynamic and change with each time slice, the remaining threats are static and do not change through time.

15. Habitat Quality: Freeport
BUILT DEFENSE SCENARIO - TIME SERIES FOR DEGRADATION
2006, 2025, 2050, 2075, 2100

16. Habitat Quality: Hands On
BUILT DEFENSE SCENARIO
MEAN DEGRADATION SCORES BY HABITAT TYPE AND YEAR

17. Habitat Quality: Hands On
NATURAL DEFENSE SCENARIO - TIME SERIES FOR DEGRADATION
2006, 2025, 2050, 2075, 2100

18. Habitat Quality: Hands On
NATURAL DEFENSE SCENARIO
MEAN DEGRADATION SCORES BY HABITAT TYPE AND YEAR

19. Habitat Quality: Hands On
BUILT DEFENSE 2100
NATURAL DEFENSE 2100
DEGRADATION

20. Habitat Quality: Hands On
With developed land included.
Without developed land included.

21. Test case from Placencia, Belize
Identification and Valuation of Adaptation Options in Coastal-Marine Ecosystems:
Test case from Placencia, Belize

22. Carbon
Sequestration

24. Seagrasses Carbon Stock
Accumulation of Carbon
in Sediments
Mangrove and Littoral Forests Carbon Stock
Mangrove and Littoral Forests Accumulation of Carbon
in Sediments
Integrated Adaptation scenarios #1 and #2 emphasize green approaches to climate adaptation, such as restoration and conservation of mangrove and littoral forests to protect the shoreline for residents and tourists, and to sustain livelihoods through fishing for lobster (Fig. 4). These scenarios also include some seawalls for shoreline protection (Fig. 5). The Integrated Adaptation scenario builds on a coastal zoning scenario that came out of our work with the Belize National government and blends strong conservation goals with current and future needs for coastal development and marine uses. It assumes that conservation would be implemented in areas that are currently protected, undeveloped, or proposed for conservation status in the Belize Integrated Coastal Zone Management Plan. We assigned areas for implementation of restoration for climate adaptation in areas where restoration activities are already underway and areas proposed for restoration (Fig. 4).
Figure 4. Proposed green strategies for the Integrated Adaptation scenario include an MPA (blue), fourteen mangrove restoration sites (red), and two private reserves (orange and dark green). Output from the Habitat Risk Assessment model from the Belize government coastal zone planning process is shown for mangroves in yellow (medium risk) and green (low risk). We assumed that medium risk sites within the locations where green strategies are proposed would be low risk in the future, as these strategies would reduce the impact of human activities on mangroves.

26. InVEST T0 Fisheries Model

27. InVEST T0 Fisheries Model

28. InVEST T0 Fisheries Model
Red Drum juvenile
habitat