Conservation Strategies in A Changing World.

—Despite the scarcity of sustained funding to promote continuous record collection, scientists and citizens around the world are now generating large volumes of monitoring data that vary in quality, format, supporting documentation, and accessibility. Complex interactions between climate, fauna, flora, and human land use challenge the understanding and forecasting of the mechanisms of change. Diverse models are now being run at various spatial and temporal scales to understand past climate variability and its impacts, generate future climate and land use scenarios, and project potential future impacts to the planet’s inhabitants. Estimates of the uncertainty associated with past observations and climate proxies, and with the results from climate and climate impacts models, are often discussed but rarely quantified in a useful way to help land managers. Ultimately, social choices and political decisions hold the keys to the future in terms of emissions, land use (agriculture, urbanization, industrialization, energy resource acquisition), and conservation efforts. Conservation practitioners and land managers are struggling to synthesize the wealth of available information and heed warnings of the unpredictable human response to change. They have to identify the relevant and usable datasets, become aware of gaps in knowledge and information, and translate evolving science results into on-the-ground climate-aware strategies. Many agencies and NGOs are currently involved in synthesizing observations and simulations, developing land management strategies, and implementing those they judge are most likely to succeed or at least cause the least harm. Collaboration and effective information sharing is essential to work effectively towards common goals. This paper includes examples of sources of climate change information, a brief summary of the types of models currently used in climate change science projects, and illustrations of collaborative efforts that address climate change issues specifically focused on the Gyrfalcon in panarctic regions. Received 8 August 2011, accepted 19 September 2011. BACHELET, D. 2011. Conservation strategies in a changing world. Pages 385–398 in R. T. Watson, T. J. Cade, M. Fuller, G. Hunt, and E. Potapov (Eds.). Gyrfalcons and Ptarmigan in a Changing World, Volume II. The Peregrine Fund, Boise, Idaho, USA. http://dx.doi. org/10.4080/gpcw.2011. 0317 The “woodification” of the entire state would greatly increase the potential extent of the area burned by wildfires. More recently, Feng et al. (2011) used 16 more recent future climate projections in the Koppen-Trewartha climate classification method and projected a 33 to 44% decrease in tundra extent (from current) by the end of the century, accompanied by a northward shift of coniferous forests and needle-leaf trees. Field observations are now also supporting the model results. Beck et al. (2011) use tree ring data and satellite information to show evidence of growth increases at the boreal–tundra ecotone in contrast with drought-induced productivity declines throughout interior Alaska. The Arctic Council's working group for environmental monitoring (the Arctic Monitoring and Assessment Programme) substantiated the increased warming in the Arctic, with warmer summers and shorter winters, increased melting of the icecap, and declining snow cover driving negative albedo feedbacks, confirming what models had projected: the arctic environment is changing fast (AMAP 2011). Gyrfalcons and their principal prey, ptarmigan, which are widely distributed in the Arctic, will likely be significantly affected by these projected changes to their habitats and food sources. This paper includes a brief discussion of the climate change information available, a quick summary of the types of models currently used in climate change science, and introduces collaborative efforts that address climate change issues specifically focused on the Gyrfalcon in panarctic regions. CLIMATE CHANGE INFORMATION AVAILABLE Changes in arctic ecosystems associated with recent climate change have been rapid and widespread across terrestrial, freshwater, and marine systems (Post et al. 2009). Modelers try to capture both changes in climate and in ecosystems responses and better simulate potential trends in those changes, allowing local populations and land managers to prepare for what comes next. However, despite recent efforts to gather extensive datasets for the region to test the models, long-term data are lacking and existing data include some uncertainty due to the remoteness and sparse population monitoring change. Climate Records. —Climate records are used by climate modelers to test and calibrate their models, using subsets of the full dataset to do so. When data are scarce, models are less likely to produce robust representations of current conditions, let alone project realistically into the future. Around the Arctic Circle, there are few meteorological stations, so historical climate data sets are very limited and interpolations between meteorological stations may provide uniform maps of climate but their uncertainty is large. Measurements of climate variables are difficult per se: there is much heterogeneity on the landscape that may not be captured by a sparse meteorological station network, so eventually the reliability of climate models trying to simulate such patterns suffers. The length of records from meteorological stations may also vary. The age of a station is of course the main cause for the differences in record length between stations. Less obvious is the amount of “in-filling” performed to remove gaps in data collections. Gaps in data records can be caused by an instrument that has stopped working, some disturbance to the equipment, or even a complete change of station instrumentation. In-filling can be done by using data from the closest meteorological station using the correlation between station records, and using those to complete the dataset assuming weather records continue to be correlated through time. When only a few days are missing, a simple linear interpolation between days can also be used to complete the record. To illustrate the problem of the spatial distribution of meteorological stations and their variable length of records in the Arctic, Figure 1 shows the density of long-term meteorological stations in North America (Groisman et al. 2005). The red stations have about 100 years worth of records, the blue stations have about 80 years, and the green stations have in gen-