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Deforestation threatens the earth's biodiversity and the ecosystem services upon which humans depend. Formal regulation is a key mechanism by which governments seek to protect forests. However, whether regulation can effectively protect remaining areas of the most threatened and most heavily cleared forests is unknown. We addressed this question using forest loss data for Queensland, Australia between 2000 and 2014 under existing vegetation clearing regulation ( Vegetation Management Act 1999 ). This regulation is specifically designed to provide the greatest protection for threatened forest types that have already lost the greatest amount of their original extent. Importantly, enforcement and governance of this regulation is relatively strong allowing an assessment of regulation design. We applied path analysis to model the direct and indirect effects (mediated by variables representing deforestation pressure) of forest protection level on clearing rates. There was strong evidence for a decline in clearing rates over time, except of clearing for non-agricultural purposes. However, threatened forest types, which have already lost > 70% of their original extent and should have the greatest level of protection under the regulation, continue to be cleared 2.7–2.9 times faster than non-threatened forest types. There was also little evidence that the regulation has driven greater reductions over time in the clearing rates of threatened versus non-threatened forests types. There was much greater support for the indirect than direct effect of protection level. This is because protection level was correlated with variables associated with deforestation pressure, resulting in higher clearing rates for threatened compared to non-threatened forest types. We hypothesise that this arises because the additional protection afforded to threatened relative to non-threatened forests is insufficient to counter the continuing higher level of deforestation pressure on threatened forests. We argue that a potential solution is to build explicit targets for forest retention into regulation, below which no further forest loss is permitted. This could be combined with spatially targeted enforcement and incentive strategies where threats are highest. [ABSTRACT FROM AUTHOR]

Feral cats ( Felis catus ) have devastated wildlife globally. In Australia, feral cats are implicated in most recent mammal extinctions and continue to threaten native species. Cat control is a high-profile priority for Australian policy, research and management. To develop the evidence-base to support this priority, we first review information on cat presence/absence on Australian islands and mainland cat-proof exclosures, finding that cats occur across >99.8% of Australia's land area. Next, we collate 91 site-based feral cat density estimates in Australia and examine the influence of environmental and geographic influences on density. We extrapolate from this analysis to estimate that the feral cat population in natural environments fluctuates between 1.4 million (95% confidence interval: 1.0–2.3 million) after continent-wide droughts, to 5.6 million (95% CI: 2.5–11 million) after extensive wet periods. We estimate another 0.7 million feral cats occur in Australia's highly modified environments (urban areas, rubbish dumps, intensive farms). Feral cat densities are higher on small islands than the mainland, but similar inside and outside conservation land. Mainland cats reach highest densities in arid/semi-arid areas after wet periods. Regional variation in cat densities corresponds closely with attrition rates for native mammal fauna. The overall population estimate for Australia's feral cats (in natural and highly modified environments), fluctuating between 2.1 and 6.3 million, is lower than previous estimates, and Australian feral cat densities are lower than reported for North America and Europe. Nevertheless, cats inflict severe impacts on Australian fauna, reflecting the sensitivity of Australia's native species to cats and reinforcing that policy, research and management to reduce their impacts is critical. [ABSTRACT FROM AUTHOR]

Abstract: Many ecological indices convert raw estimates of a variable into a score, and combine scores for multiple variables into a total score. Biodiversity valuation indices, for example, convert raw estimates of variables thought to be surrogates for habitat attributes into a quantitative expression of biodiversity value for use in market-based conservation policies. This study evaluated two potential sources of inaccuracy in total scores: quantitative or model uncertainty; and input data uncertainty or measurement error. Simulated scenarios were run to explore the effects of measurement error and bias on hypothetical sites in a grassy woodland, using two indices of biodiversity value commonly used in Australia. Parameterisation of measurement error was informed by published information on empirical measurement error, as well as results of a field trial using 10 independent observers. As expected, larger observer errors led to less precision in valuations, although coefficients of variation in total scores were often smaller than those of the input variables due to compensatory errors. Unexpectedly, unbiased observer errors in the input variables generated biased valuations of biodiversity. This bias was almost always in one direction – the indices underestimated the true biodiversity value of most sites, high-value sites in particular. Underestimation primarily occurred where raw true values were within benchmark scoring categories but observer error caused a majority of observer estimates to fall into lower scoring categories. The size of benchmark scoring intervals and parameter weightings were the two factors that most influenced the degree of bias. Depending on the index used to calculate biodiversity value, the underestimation effect was more significant for sites with intact woody or herbaceous features, due to scoring intervals and weightings of these features. For market-based biodiversity policies, significant implications include increasing the risk of worse than expected outcomes. This research highlights the imperative to minimise observer error hitherto poorly dealt with, to design and construct quantitative ecological indices in ways that do not exacerbate uncertainty, and to explicitly address uncertainty in decision making. [Copyright &y& Elsevier]