Improving averted loss estimates 399 Pathway A describes situations in which the proposed
protection is insufficient to entirely prevent the loss of the site, for example when certain use rights override the pro- tection mechanism. However, if the impacts caused by any such activity would themselves require an offset, the like- lihood of loss is not elevated, and thus remains negligible (0%in Fig. 3). For example, exploration for and extraction of mineral resources are permitted under several forms of legal land protection in Australia, but their impacts on listed threatened species often must be avoided or offset. Wenote, however, that impacts on offset projects will not always, or in all jurisdictions, be offset in compliance with policy (e.g. the impacted Kalagala offset for the Bujagali hydropower project in Uganda; Esmail, 2017). Pathway B describes situations in which the proposed
form of protection is sufficient to prevent loss of the site. In these cases, the proposed offset action (protecting the site) is sufficient to reduce the likelihood of loss (the mag- nitude of which is determined under the counterfactual scenario) to a negligible level. Pathway C describes situations in which loss of the site
would be neither prevented by the proposed protected ten- ure status nor likely to be subject to an offset requirement. In these situations, an appropriate likelihood of loss assump- tion would be greater than zero, but less than the calculated recent rates of loss at similar, unprotected sites. This ac- knowledges that the protection conferred on the site by the offset will reduce the likelihood of loss, but some resid- ual risk remains. To progress through the decision tree, our method re-
quires landscape-scale assessment of recent rates of loss, and then site-scale evaluation of additional, localized influ- ences on likelihood of loss.
Step 1: Describing recent rates of loss at a landscape scale
In the absence of other data, recent rates of loss calculated at a landscape scale can make a plausible and independently verifiable contribution to predicting future rates of loss at a proposed offset site within that same landscape (Maseyk et al., 2017). This assumption is made on the basis that sites within the same landscape are subject to similar an- thropogenic influences. Although this may not hold true under certain circumstances, such as a change of regulations affecting vegetation removal (Evans, 2016; Rhodes et al., 2017; Simmons et al., 2018), it provides a useful starting point for estimating Pwo. If estimates do deviate from Pwo, additional evidence is required to support this. Recent work in Australia illustrates the implementation of this method, where it was used to estimate risk of forest loss (equivalent to likelihood of loss) across Australia by meas- uring change in forest extent resulting from human inter- vention within a recent 10-year period (2005–2014) using
forest extent and change imagery (Maseyk et al., 2017). The change in forest extent was then used to calculate the annual rate of primary deforestation within local govern- ment areas across Australia, expressed as a proportion of the remaining forest extent. Finally, the mean annual rate of deforestation during 2005–2014 was calculated for each local government area (Fig. 4). These rates were multiplied by 20 (the Australian policy requires risk of loss to be calculated within a 20-year time horizon, referred to as ‘foreseeable future’) to estimate the risk of loss for each local government area. These risks of loss figures have been recommended as a basis for estimates required by the Australian Offsets Assessment Guide (Miller et al., 2015; Maseyk et al., 2017). Using remotely sensed land-cover data is an accepted and repeatable method by which to deter- mine land-cover change in forest and woodland ecosystems. However, it has some limitations, including being less reli- able at higher resolutions (e.g. property scale) and in- sufficiently sensitive to capture patterns of loss at low resolutions (e.g. country scale), and being less reliable for non-forest habitat types. Additional research should focus on understanding the spatial and temporal scales most use- ful for using past biodiversity losses to estimate future like- lihood of loss, including situations in which assessment at a larger scale (e.g. local government area) mayobscure hetero- geneity within the area caused by factors such as soil type, production potential, or proximity to existing settlements or desirable areas for residential expansion. Future assess- ment of data on past loss rates should also be refined to ex- clude loss driven by development that would have triggered an offset requirement (type I impacts), which will also im- prove the accuracy of likelihood of loss estimates, par- ticularly when concentrated activities such as urbanization can skew data when evaluated at larger scales. We also sug- gest thatmethods for identifying change in habitat types for which remotely sensed data are less readily available or ac- curate (e.g. habitat types dominated by non-woody or short- stature vegetation) are needed to improve this approach.
Step 2: Consider any additional site-specific factors influencing likelihood of loss
Once recent rates of loss at similar sites have been described, a site-specific assessment of likelihood of loss is required to ascertain (1) whether any additional factors that influence likelihood of loss are at play, and (2) whether activities in the wider landscape that contribute to loss are occurring, or are likely to occur, at the proposed offset site. If there is no evidence to suggest otherwise, it is possible to assume that a given offset site will be subject to the same rate of loss as other similar rates of loss in the landscape; however, there may be good reasons why such an assumption does not hold. Consequently, we suggest that conclusions and
Oryx, 2021, 55(3), 393–403 © The Author(s), 2020. Published by Cambridge University Press on behalf of Fauna & Flora International doi:10.1017/S0030605319000528
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