生境质量计算结果只在原本设置的habitat的0.01之间浮动？

Science, Research & Data
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以林地为例，我设置林地的HABITAT为1，并且设置了相应的敏感度和威胁源。
经过计算后，林地的生境质量都在0.99-1之间，同理，耕地、水体等等都是相似的结果。

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这是否是模型计算出错呢？

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Hi @ecnu_xyl, welcome and thank you for posting. If you can, could you please edit your post & title to also include English? That will help make the question & answer accessible to more people on this forum. I used google translate and I understand your question to be this:

The calculated result of habitat quality only fluctuates between 0.01 of the originally set habitat?

Taking forest land as an example, I set the HABITAT of forest land to 1, and set the corresponding sensitivity and threat source.
After calculation, the habitat quality of forest land is between 0.99-1. Similarly, cultivated land, water bodies, etc. are all similar results.
Is this an error in the calculation of the model?

Could you upload the logfile from the model run so we can see all the parameter values used?

Adjusting the “half-saturation constant” value will affect the distribution of values in the results. Here is guidance from the User’s Guide:

  • Half-saturation constant (required): This is the value of the parameter k in equation (4). By default it is set to 0.05 but can be set equal to any positive floating point number. In general, you want to set k to half of the highest grid cell degradation value on the landscape. To perform this model calibration you will have to the run the model once to find the highest degradation value and set k for your landscape. For example, if a preliminary run of the model generates a degradation map where the highest grid-cell degradation level is 1 then setting k at 0.5 will produce habitat quality maps with the greatest variation on the 0 to 1 scale (this helps with visual representation of heterogeneity in quality across the landscape). It is important to note that the rank order of grid cells on the habitat quality metric is invariant to your choice of k. The choice of k only determines the spread and central tendency of habitat quality scores. It is important to use the same value of k for all runs that involve the same landscape. If you want to change your choice of k for any model run then you must change the parameters for all model runs.