Habitat Quality Algorithm question

Hi,

I am running the HQ model to observe the degradation of two types of crops corn and hay. I ran the model for each crop and then ran the model with both plants together. I observed that the degradation of corn was higher than that of both corn and hay combined. I wanted to know how does the algorithm account for the interaction between two or more threats.

Specifically, how does the model account for the degradation and the interactions between the threats based on the pixels of the input map data? For instance, an input map from the Crop Data Layer which provides LULC maps with pixel sizes of 30 x 30m. Does the model account for the degradation that one pixel of a particular threat has on each surrounding pixel of the same threat? In what way is this considered?

I am trying to get a better understanding of the pixel interactions within the model. Specifically, how does a single pixel of a threat affect the surrounding pixels? I ask this because I am curious if the degradation is being double-counted.

Hi! Just following up on this. Any help or insight would be really appreciated!

Hello,

Thanks for your question about the habitat quality model. The final habitat quality score comes from the land use/land cover (LULC) map input and the threat layer or layers the user provides. The threat layers have the potential to degrade the habitat quality scores of the land cover map. The spatial extent over which they act and the strength of that effect is based on 1) the threats data table (which includes maximum distance a threat acts over, its weight, and whether the impact decays linearly or exponentially), and 2) the sensitivity table (which specifies the sensitivity of each LULC class to the different threats). If a threat, such as agriculture, is included as both a LULC class and as a threat layer, you may want to specify that the agriculture LULC has a sensitivity of 0 to the agriculture threat layer, so that the impact of agriculture is not double counted in agricultural pixels.

The impacts of different threats on a given pixel are added together to calculate a pixel’s degradation score. See equation (3) in the habitat quality model user’s guide for more details. The degradation score is then translated into the final habitat quality value, also factoring in that pixel’s LULC-based habitat suitability. See equation (4) in the user’s guide for the details. While the degradation scores on a pixel are added, the relationship between a degradation score and final habitat quality is not linear, so the next effects of multiple threats on habitat quality won’t simply be additive.

I hope that helps you interpret your results, and please let me know if I can clarify anything further!

Best,
Lisa