Cooper GBA region biodiversity persistence time series

Abstract

This annual time series dataset was compiled by the Geological and Bioregional Assessment Program from source data referenced within the dataset and/or metadata. The dataset represents the estimated proportion of species occurring in each location expected to persist in the long-term based on spatial patterns in habitat condition. This product was derived by combining the habitat condition annual time series with a spatial projection of a generalized dissimilarity model of plant community compositional turnover across Australia. The grid for each year can be combined with the summed similarity of each location to all other locations (also provided) to derive an estimate for the Cooper GBA region or any sub-region within it, of the total proportion of species in that region expected to persist in the long-term, given changes in habitat condition.

Attribution

Geological and Bioregional Assessment Program

History

To assess the impacts of changes in habitat condition on plant biodiversity in the Cooper GBA region, we applied the habitat based methodology developed by Ferrier et al. (2004) that has subsequently been used in a variety of biodiversity assessments (Allnutt et al. 2008; UNEP-WCMC 2016). This approach uses the predicted spatial patterns in compositional dissimilarity between site-pairs to estimate the total proportion of biodiversity retained over a region at each time point (P).

Specifically, for each and every ≈250 m grid cell i across the focal region, we estimate the proportion (pi) of species historically occurring in this cell that are likely to be retained within remaining habitat anywhere in their range. For the present analysis, we used an existing continental model of plant community compositional dissimilarity at 250 m resolution (Mokany et al. 2018), while the derivation of spatial layers of habitat condition is described in the data entry: Cooper_habitat_condition_time_series.

The overall proportion of species (P) retained across the focal region can then be estimated as a weighted average of the pi values for all n individual grid cells, to incorporate the effects of compositional overlap between grid cells, using the summed similarity (Σsij) of all other grid cells to each focal cell i.

We applied this approach to estimate the proportion of the original plant diversity retained across the Cooper GBA region at each time point, being 1 year intervals past-to-present (2001-2018). For this analysis, we assessed biodiversity persistence across all locations within the assessment regions, incorporating information on habitat condition and community compositional similarity from a broader area, being the assessment region plus a 50 km buffer. This approach incorporates the effects of habitat condition and biodiversity patterns beyond the study regions in calculating expected biodiversity persistence within the regions, given that many of the species occurring within the regions are likely to have much broader spatial distributions. The biodiversity analyses were undertaken using CSIRO’s bilbi61 package in Python (Ware 2020).

REFERENCES Allnutt, T.F., Ferrier, S., Manion, G., Powell, G.V.N., Ricketts, T.H., Fisher, B.L. et al. (2008). A method for quantifying biodiversity loss and its application to a 50-year record of deforestation across Madagascar. Conservation Letters, 1, 173-181. Ferrier, S., Powell, G.V.N., Richardson, K.S., Manion, G., Overton, J.M., Allnutt, T.F. et al. (2004). Mapping more of terrestrial biodiversity for global conservation assessment. Bioscience, 54, 1101-1109. Mokany, K., Harwood, T.D., Ware, C., Williams, K.J., King, D., Nolan, M. et al. (2018). Enhancing landscape data: capacity building for GDM analyses to support biodiversity assessment. In. CSIRO Canberra, p. 77. Rosenzweig, M.L. (1995). Species Diversity in Space and Time. Cambridge University Press, Cambridge. UNEP-WCMC (2016). Exploring approaches for constructing Species Accounts in the context of the SEEA-EEA. In: (eds. King S, Brown C, Harfoot M & Wilson L). United Nations Environment Program - World Conservation Monitoring Centre, Cambridge, p. 160. Ware, C. (2020). bilbi61: Python package. In. CSIRO Hobart.