Matching Scales of Management and Processes in Landscape Genetics

In spatial and landscape ecology, scale issues regularly confound the analysis and interpretation of data. One particularly important scale issue for conservation is that there are often mismatches between the scales at which management decisions are taken and the scales at which ecological processes operate (Cumming et al. 2006; Pelosi et al. 2010). One solution to this is to match the scale (resolution) of management explicitly to the scale of the ecological processes that you are managing. However, this is commonly not possible; administrative boundaries that define the scales at which management occurs are often determined based on a range of political and administrative criteria that may not relate well to the ecology of the system. In this case we need approaches that bridge the scale mismatch.

In a paper led by Rachael Dudaniec and Jonathan Rhodes that we published in Molecular Ecology last year (Dudaniec et al. 2013) we developed an approach for dealing with this exact problem for landscape genetics studies. Our approach utilises multi-level statistical models and we applied it in South East Queensland, Australia to a koala genetic data set. In South East Queensland, koala management and conservation decisions are made predominantly by at the scale (resoultion) of Local Government Authorities or Local Councils (of which there are 11) and almost certainly, these do not match the scale at which koala gene flow occurs across the region.

We applied our new approach to capture processes occurring at scales relevant to both gene flow and and management when identifying landscape drivers of koala gene flow across the region. In doing so, we were able to demonstrate that we could identify drivers of gene flow simultaneously at the resolution of individuals (which relates to the scale of ecological processes) and at the resolution of Local Councils (which relates to the scale of management decision making).

At the scale of individuals, the key drivers of gene flow were the presence of forest cover and highways. But interestingly, the key driver of gene flow at the resolution of Local Councils was simply how far apart Local Councils are despite testing for the effect of forest cover and urban cover at that resolution. At the resolution of Local Councils, gene flow becomes unimportant at large distances.

Apart from developing a new approach for dealing with scale mismatches, our paper suggests that within Local Councils, gene flow can be maintained through the protection of native forest cover and improving connectivity across highways. But, at the resolution of Local Councils, our ability to manage gene flow through landscape management is low when Local Councils are separated by large distances. This suggest that our best bet for managing gene flow may be within Local Councils or between adjacent Local Councils where we can influence gene flow through landscape management. Managing for connectivity across broader spatial extents may have limited success. This conclusion also agrees with a simulation study led by Lorenzo Cattarino also published last year (Cattarino et al. 2013).

Although we only looked at one species here, the question about which scale to manage connectivity at is an important one. This is especially true in the context of the push for large scale corridors, such as the Great Eastern Ranges Intiativeand Gondwana Link. Our studies suggests that you may be better off managing local connectivity rather than large-scale connectivity – although this is yet to be formally tested.

References

Cattarino, L., C. McAlpine, and J. R. Rhodes. 2013. The consequences of interactions between dispersal distance and resolution of habitat clustering for dispersal success. Landscape Ecology 28:1321-1334.

Cumming, G. S., D. H. M. Cumming, and C. L. Redman. 2006. Scale mismatches in social-ecological systems: causes, consequences, and solutions. Ecology and Society 11.

Dudaniec, R. Y., J. R. Rhodes, J. Worthington Wilmer, M. Lyons, K. E. Lee, C. A. McAlpine, and F. N. Carrick. 2013. Using multi-level models to identify drivers of landscape genetic structure among management areas. Molecular Ecology 22:3752-3765.

Pelosi, C., M. Goulard, and G. Balent. 2010. The spatial scale mismatch between ecological processes and agricultural management: Do difficulties come from underlying theoretical frameworks? Agriculture Ecosystems & Environment 139:455-462.