Noah's ark conservation will not preserve threatened ecological communities under climate change

Background: Effective conservation of threatened ecological communities requires knowledge of where climatically suitable habitat is likely to persist into the future. We use the critically endangered Lowland Grassland community of Tasmania, Australia a

Determining the ecological viability of reclaimed bushland at Newport Lakes Reserve, Melbourne, Victoria

Since European settlement in the 1830s, 12 million ha (65%) of natural forest and woodland has been cleared in Victoria, through agriculture and urban development (Commonwealth of Australia, 1995). In the Western Basalt Plains of Victoria, nearly 100% of original native vegetation has been destroyed (Lunt,1990, Scarlett and Wallbrink et al., 1992). With fewer native areas to preserve, there is great need to reestablish and `reclaim' some of the diversity and area of natural habitat that previously existed. An ultimate goal when creating or `restoring' a location is that at some future time, the restoration will be able to look after itself with a minimum of human management, while still retaining desired ecological values. This goal can be termed ecological viability. This thesis aims to examine the ecological viability of a created bushland environment in the Western Basalt Plains of Victoria, known as Newport Lakes Reserve

Characterising landscape connectivity for conservation planning using a dispersal guild approach

Context Land use changes have modified the extent and structure of native vegetation, resulting in fragmentation of native species habitat. Connectivity is increasingly seen as a requirement for effective conservation in these landscapes, but the question remains: ‘connectivity for which species?’. Objective The aim of this study was to develop and then apply a rapid, expert-based, dispersal guild approach where species are grouped on similar fine-scale dispersal behaviour (such as between scattered trees) and habitat characteristics. Methods Dispersal guilds were identified using clustering techniques to compare dispersal and habitat parameters elicited from experts. We modelled least-cost paths and corridors between patches and individual movement probabilities within these corridors for each of the dispersal guilds using Circuitscape. We demonstrate our approach with a case study in the Tasmanian Northern Midlands, Australia. Results The dispersal guild approach grouped the 12 species into five dispersal guilds. The connectivity modelling of those five guilds found that broadly dispersing species in this landscape, such as medium-sized carnivorous mammals, were unaffected by fragmentation while from the perspective of the three dispersal guilds made up of smaller mammals, the landscape appeared highly fragmented. Conclusions Our approach yields biologically defensible outputs that are broadly applicable, particularly for conservation planning where data and resources are limited. It is a useful first step in multi-species conservation planning which aims to identify those species most in need of conservation efforts

Using dispersal guilds to assess connectivity at the landscape scale: a case study in the Tasmanian Midlands

The objective of this study is to explore the potential for using dispersal guilds with connectivity modelling to characterise landscape connectivity for conservation planning. By using dispersal guilds as the focal conservation target, we can capture a range of responses to fragmentation without having to resort to time‐consuming single species modelling. This approach can identify those groups of species that are most impacted by fragmentation and are likely to benefit most from restoring links within a landscape. As well as developing the dispersal guild concept, we describe a process for engaging experts in eliciting the ecological and dispersal characteristics of target species, and identifying dispersal groups through cluster analysis of these characteristics. We used a case study in the Our study area the Northern Midlands of Tasmania to illustrate this approach

Mapping Scenario Narratives: A Technique to Enhance Landscape-scale Biodiversity Planning

Developing regional scenarios enables planners to engage land managers in discussions about the future, especially in contexts that are complex, uncertain and difficult to control. Richly-crafted qualitative narratives are an effective way to document future scenarios that integrate social, economic and biophysical attributes. Converting such narratives into spatial representations of future landscapes often relies on computational modelling. This paper presents an alternative technique. Key themes from scenario narratives are translated into spatial representations using simple rule sets within a Geographic Information System (GIS). The technique was applied to a case study exploring future scenarios for biodiversity in a predominantly privately-owned agricultural landscape. Iterative analysis of scenarios and their spatial implications enables land managers to explore outcomes from potential interventions and identify strategies that might mitigate the impact of future issues of environmental concern.This paper is an output from the Landscapes and Policy Research Hub. The hub was supported through funding from the Australian Government’s National Environmental Research Programme and involved researchers from the University of Tasmania, The Australian National University, Murdoch University, the Antarctic Climate and Ecosystems Cooperative Research Centre, Griffith University and Charles Sturt University

Mapping scenario narratives: a technique to enhance landscape-scale biodiversity planning

Developing regional scenarios enables planners to engage land managers in discussions about the future, especially in contexts that are complex, uncertain and difficult to control. Richly-crafted qualitative narratives are an effective way to document future scenarios that integrate social, economic and biophysical attributes. Converting such narratives into spatial representations of future landscapes often relies on computational modelling. This paperpresents an alternative technique. Key themes from scenario narratives are translated into spatial representations using simple rule sets within a Geographic Information System (GIS). The technique was applied to a case study exploring future scenarios for biodiversity in a predominantly privately-owned agricultural landscape. Iterative analysis of scenarios and their spatial implications enables land managers to explore outcomes from potentialinterventions and identify strategies that might mitigate the impact of future issues of environmental concern

Current and future climate suitability for a) Natural grasslands and closely related vegetation communities (GTL+GPL+NBA), and b) Natural and derived grasslands and closely related vegetation communities (GTL+GPL+NBA+GCL).

<p>White represents areas that are not climatically suitable. Areas currently climatically suitable are shown in grey, those projected to be climatically suitable by <b>at least</b> one of the six climate models by 2050 are shown in pink and by 2080 in red. The overlap of the current (grey) and the projections to 2050 or 2080 can be seen in dark red. Black indicates the areas where <b>all</b> climate models agree that currently suitable climate will persist by 2080.</p

Area of current good-condition Lowland grasslands that are projected to remain climatically suitable by each climate model by 2050 and 2080.

<p>Area of current good-condition Lowland grasslands that are projected to remain climatically suitable by each climate model by 2050 and 2080.</p

The set of bioclimatic variables used in the Maxent models of grassland communities and species.

<p>* bio3 (isothermality) can be interpreted as the evenness of temperature over the course of a year, or a quantification of how large the day-to-night temperature oscillation is in comparison to the summer-to-winter oscillation. A value of 100 would represent a site where the diurnal temperature range is equal to the annual temperature range.</p><p>**bio31 was not used because the Coefficient of Variation could not be calculated in areas where the standard deviation was zero (large areas of western Tasmania).</p><p>The set of bioclimatic variables used in the Maxent models of grassland communities and species.</p

Extent of current GTL and GPL grasslands in good condition.

<p>Extent of current GTL and GPL grasslands in good condition.</p