Utilisation of analogous climate locations to produce resilient biodiversity plantings for infrastructure developments

Developers have an obligation to biodiversity when considering the impact their development may have on the environment, with some choosing to go beyond the legal requirement for planning consent. Climate change projections over the 21st century indicate a climate warming and thus the species selected for habitat creation need to be able to withstand the pressures associated with these forecasts. A process is therefore required to identify resilient plantings for sites subject to climate change. Local government ecologists were consulted on their views on the use of plants of non-native provenance or how they consider resilience to climate change as part of their planting recommendations. There are mixed attitudes towards non-native species, but with studies already showing the impact climate change is having on biodiversity, action needs to be taken to limit further biodiversity loss, particularly given the heavily fragmented landscape preventing natural migration. A methodology has been developed to provide planners and developers with recommendations for plant species that are currently adapted to the climate the UK will experience in the future. A climate matching technique, that employs a GIS, allows the identification of European locations that currently experience the predicted level of climate change at a given UK location. Once an appropriate location has been selected, the plant species present in this area are then investigated for suitability for planting in the UK. The methodology was trialled at one site, Eastern Quarry in Kent, and suitable climate matched locations included areas in north-western France. Through the acquisition of plant species data via site visits and online published material, a species list was created, which considered original habitat design, but with added resilience to climate change.

Interactions between landcover pattern and geospatial processing methods:effects on landscape metrics and classification accuracy

Remote sensing data is routinely used in ecology to investigate the relationship between landscape pattern as characterised by land use and land cover maps, and ecological processes. Multiple factors related to the representation of geographic phenomenon have been shown to affect characterisation of landscape pattern resulting in spatial uncertainty. This study investigated the effect of the interaction between landscape spatial pattern and geospatial processing methods statistically; unlike most papers which consider the effect of each factor in isolation only. This is important since data used to calculate landscape metrics typically undergo a series of data abstraction processing tasks and are rarely performed in isolation. The geospatial processing methods tested were the aggregation method and the choice of pixel size used to aggregate data. These were compared to two components of landscape pattern, spatial heterogeneity and the proportion of landcover class area. The interactions and their effect on the final landcover map were described using landscape metrics to measure landscape pattern and classification accuracy (response variables). All landscape metrics and classification accuracy were shown to be affected by both landscape pattern and by processing methods. Large variability in the response of those variables and interactions between the explanatory variables were observed. However, even though interactions occurred, this only affected the magnitude of the difference in landscape metric values. Thus, provided that the same processing methods are used, landscapes should retain their ranking when their landscape metrics are compared. For example, highly fragmented landscapes will always have larger values for the landscape metric "number of patches" than less fragmented landscapes. But the magnitude of difference between the landscapes may change and therefore absolute values of landscape metrics may need to be interpreted with caution. The explanatory variables which had the largest effects were spatial heterogeneity and pixel size. These explanatory variables tended to result in large main effects and large interactions. The high variability in the response variables and the interaction of the explanatory variables indicate it would be difficult to make generalisations about the impact of processing on landscape pattern as only two processing methods were tested and it is likely that untested processing methods will potentially result in even greater spatial uncertainty. © 2013 Elsevier B.V.