| Abstract: | Landscape pattern indices are common tools of landscape ecologists, affording comparisons of different study areas, or the same study area at different times. Since the advent of popular index-calculating software, more landscapes can be analyzed in short amounts of time, yet the behaviour of landscape pattern indices can vary for different contexts or data characteristics, complicating interpretation. I applied a selected set of landscape pattern indices to fine-resolution (3 m) data representing a highly fragmented landscape – Corn Belt Iowa agriculture – to investigate the performance of landscape pattern indices. Indices measured pattern attributes that affect the viability of small mammal populations, namely habitat proportion and connectivity and landscape grain size and heterogeneity. Results showed that the performance of indices for fine-resolution data can be highly variable, depending upon data and contextual issues like the presence of linear elements and the amount of habitat. For these Corn Belt landscapes good habitat proportions and patch sizes were small (commonly less than 10% and less than 1 ha, respectively), and connectivity was variable depending on the measure. Aggregation and mean nearest neighbour indices performed better than other connectivity indices. Fine-resolution data representing highly fragmented landscapes can raise difficulties for indices of landscape configuration. Landscape pattern indices require improvement to perform better for increasingly available fine-resolution data representing common landscape types. |
