Optimal planning for mitigating the impacts of roads on wildlife

Abstract

Summary1. Roads have a significant impact on wildlife world-wide. Two of the ways to mitigate the impact of roads are to improve connectivity and reduce mortality through fences and wildlife crossings. However, these are expensive actions that will have different effects in different places. Thus, deciding where and how to act in order to achieve the greatest return on investment is crucial. Currently, there are no quantitative approaches to prioritize different road mitigation options. 2. Here, we use a decision science framework to determine the most cost-effective combination of actions to mitigate the effects of roads on wildlife under budget constraints. We illustrate our approach using a case study of a threatened koala Phascolarctos cinereus population in south-east Queensland. We applied a spatially explicit population model to explore the benefits of three kinds of mitigation actions: no action at all and fences with or without crossings, on different road segments. 3. We explored the trade-off between expected koala population size, relative to the best outcome, and budget. There is a strong demand for mitigation as the already declining population was reduced even further when no mitigation was employed, while applying the most cost-effective combination of mitigation actions minimized that decline. Additionally, uncertainty in species attributes (speed of crossing a road and population growth rate) affected population viability but not the decision about which suite of actions (mitigation measures) to take -so our advice on the best action is robust to uncertainty even if the outcome is not. Most importantly, the trade-off curves between investment and population size are almost linear in this case study. Hence, there is no cheap solution and any reduction in the budget will result in a substantial reduction in expected population size. 4. Synthesis and applications. This is the first time that the problem of mitigating the effects of roads on wildlife was formulated mathematically and systematically using decision science. Our approach is adaptable to a diversity of species and systems affected by road mortality allowing flexibility for a range of mitigation actions and biological outcomes. Our method will allow managers and decision-makers to increase the efficiency of mitigation actions.

Effects of Road Density and Pattern on the Conservation of Species and Biodiversity

“…Two studies even cautioned that fencing was ineffective unless it was continually maintained and breaches in the fencing were repaired in a timely manner [111,113]. Furthermore, many studies suggested that road-related mortality could be further reduced when crossing structures were combined with fencing [97,114,115]. Moreover, one study on koalas (Phascolarctos cinereus) in Australia emphasized that selecting a single mitigation option was not economically viable [114].…”

Methods to Monitor and Mitigate Wildlife Mortality in Railways

“…Over the last few decades, research and investment on mitigation measures to reduce wildlife mortality on roads have increased greatly (Jackson and Griffin 2000;Glista et al 2009;Polak et al 2014;Kociolek et al 2015;Ward et al 2015). In railways the efforts to increase wildlife safety have not been as considerable (van der Grift 1999;Dorsey et al 2015), and these efforts have been concentrated mostly on mitigating the impacts on existing railways, ignoring the expansion of the high-speed railway network (Dorsey et al 2015).…”

Using fine and coarse conservation targets to maximize cost-effectiveness of road mitigation and protected areas

“…Nonetheless, multispecies studies are still a rarity in the road ecology literature and, to our knowledge, so far none has focused on cost-effective mitigation planning for more than one species. Moreover, only a few studies incorporate cost-effectiveness at all into their mitigation planning (Putman et al 2004, Ascensão et al 2013, Polak et al 2014. Multispecies systematic planning is a common practice in other fields of conservation.…”

“…The aims are followed by a list of management actions, state variables and their dynamics, constraints and uncertainties, all of which are incorporated into a framework to try to find the best solution or a range of very good solutions to a conservation problem. Decision science incorporates budget constraints (Possingham et al 2001, Newburn et al 2005, Strange et al 2006, McCarthy and Possingham 2007, McDonaldMadden et al 2008, Tulloch et al 2011, prioritizes actions (Polak et al 2014), prepare for climate change (Hodgson et al 2009), dealing with uncertanties (Regan et al 2005) and facilitates resource allocation (Wilson et al 2006, Martin et al 2007, Game et al 2008). …”

“…Here, we assumed a 50% chance of finding and using the wildlife passage and crossing safely (Polak et al 2014), which is added to the probability of finding a hole in the fence as in For a full list of all parameters, see Table S3.…”