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

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

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.

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.

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

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.)

Habitat Quality: Sensitivity table Example from InVEST sample data

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)

Habitat Quality: Accessibility 1 .8 .2

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

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.

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

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

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.

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

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

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

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

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

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

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

Carbon Sequestration

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.

InVEST T0 Fisheries Model

InVEST T0 Fisheries Model

InVEST T0 Fisheries Model Red Drum juvenile habitat