Rapid climate change may result in ecological futures with no analog, manifested as novel species assemblages, trophic mismatch, and mass extinction.  Conventional inventory and monitoring approaches are generally not designed to address multiple taxonomic groups, particularly at the spatial and temporal scales demanded by climate change. 

Kenai NWR has legislative mandates “to conserve fish and wildlife populations and habitats in their natural diversity” and “to ensure biological integrity, diversity and environmental health”.  However, a warming and drying climate, commercial oil and gas development, and an urbanizing interface have impacted the 805,000 ha Kenai NWR over the past 50 years and are forecasted to continue changing the landscape and presumably biota in the foreseeable future.

To improve our understanding of spatial and temporal variation at the landscape level, we are developing the Long Term Ecological Monitoring Program to assess change in biota on the sample frame used by the USDA Forest Inventory and Analysis program (FIA). Through a formal agreement with the FIA, we completed our baseline inventory (t1) of 259 permanent terrestrial plots systematically distributed at 5-km intervals in 2004, 2006 and 2008.

In addition to the forested vegetation sampled by the FIA, we sampled vascular and nonvascular plants on non-forested plots, and breeding landbirds, arthropods, and noise on all plots.  All sampling methods were passive, nondestructive (to habitat), relatively inexpensive, and required = 2 visits to a plot in a given sampling year. We recorded 1,087 species including 80 birds, 227 invertebrates, 329 vascular plants, 298 lichens, and 153 mosses. Species richness per 100-m2 plot varied from 1 to 87, with a mean of 36 species.  Co-located data on a grid sample frame set the stage for modeling future distributions of single species and species assemblages based on down-scaled climate forecasts.

These attributes also allow monitoring change in both time and space using multiple metrics, of which occupancy may be the most generally relevant for assessing climate change effects at the landscape scale. In preparation for t2 of a time series in 2014, we are building a DNA barcode library to facilitate bulk sampling of arthropods (i.e., species assemblages) with > 400 invertebrates to date. Our approach provides a statistically-robust spatial framework for landscape monitoring and modeling, allowing us to make inferences at multiple spatial scales, while ensuring the capacity to detect ecological surprises that may result from a rapidly warming climate. 

Results to date show great promise for long-term monitoring and may have broad application to other land management agencies.