This dataset represents a historic annual time-series of estimated habitat condition across the study region, from 2001 to 2018 inclusive. These spatial grids were developed by combining two spatial data products: (1) a new analytical approach called Compere, which contrasts the vegetation cover of each location with a set of environmentally similar locations across the region, for each year; (2) a temporally static spatial layer on the total length of all linear anthropogenic disturbances, in each 500 m grid cell across the study region. The resultant habitat condition spatial time-series is therefore intended to combine information on both localised (e.g. roads, seismic surveys) and dispersed (e.g grazing, fire) influences on the habitat condition for biodiversity of each location, ranging continuously from '0' (completely degraded) to '1' (pristine).
Geological and Bioregional Assessment Program
To derive a past-to-present time-series of habitat condition across the buffered study regions, we combined two spatial data products. The first set of spatial data come from a new analytical approach called Compere, which contrasts the vegetation cover of each location with a set of environmentally similar locations across the region. For the present purpose, we applied the Absolute Range Ratio (ARR) metric from Compere, which for each location at a time-point is the observed vegetation cover divided by the maximum vegetation cover across all the environmentally similar locations at that time-point. The ARR spatial layers were available for each year from 2001 to 2018 inclusive. To translate the ARR spatial layers to better represent a habitat condition metric (h_ARR), we rescaled the ARR values as:
with the scalar k specifying the minimum habitat condition value set to 40 % for the present analysis.
The second spatial dataset used in deriving habitat condition for biodiversity was a spatial layer on the total length of all linear disturbances in each 500 m grid cell across the study regions. We converted this layer into a habitat condition metric (hL) by assuming complete habitat loss for a width of 10 m for all linear disturbances, then taking the inverse of the proportion of each grid cell area that was disturbed.
These two data sources on habitat condition provide complementary information. The h_ARR condition metric derived from the Compere analysis is useful in detecting the broadscale impacts of actions such as grazing and fire management across the region on habitat condition. In contrast, the hL condition metric derived from the spatial data on linear disturbances is useful in identifying known disturbances, such as roads, fence-lines and seismic survey lines. We therefore combined these two condition metrics, using a conservative approach of taking the minimum condition value from each of these metrics, for each grid cell:
assuming a constant level of linear disturbance over the time period of the Compere analysis (2001-2018). This provided an historic annual time series of habitat condition across each study region.