Climate data and climate-based seed zones for Mexico: guiding reforestation under observed and projected climate change

Seed zones for forest tree species have been used for decades to guide seed movement in reforestation programs, ensuring that seedlings are well adapted to their planting environments. Seeds may be collected and planted anywhere within a zone, but not across zone boundaries. The zones are geographic delineations that often track ecosystem boundaries, and comprise areas of similar climate and other environmental conditions. However, under climate change, this management approach is no longer valid. Local seed sources become increasingly lagged behind the environments to which they are optimally adapted. Here, I develop a climate-based seed zone system for Mexico to address observed and projected climate change. For climate-based geographical delineations, I develop an interpolated climate database for past conditions (1901-2015) as well as for future projections for the 2020s, 2050s. and 2080s. While high quality interpolated climate data for temperature variables are widely available, existing datasets for precipitation have a number of shortcomings. Precipitation patterns in complex terrain, such as orographic lift effects and rain shadows, are generally difficult to model, and high quality products are only available for some regions of the world, namely for the United States, western Canada, and Europe. To address this issue for Mexico and other parts of the world where high quality precipitation grids are not available, I start with the compilation of precipitation weather station records from nine open-access databases (CRU, GHCN, FAO, WMO, ECA, R-HydroNet and USFS). The database was cross-checked for errors, duplicates were removed, location and elevation errors corrected, and missing precipitation values were estimated where possible. The database was then spatially subsampled, retaining only the most reliable records with a balanced spatial and elevational distribution, targeting one station per 40km grid cell and per 100m elevation interval. The resulting database contained 45,888 stations from an original 98,631 stations, excluding duplicates. This represents an approximately 50% larger compilation than any of the original databases, even after spatial subsampling. Subsequently, I developed a new interpolation approach that models monthly long-term precipitation patterns for the 1961-1990 normal period, based on weather station data, wind measurements, and topographical exposure. The model was implemented through a local, universal kriging approach that uses wind speed, wind direction, as well as topographic aspect and slope to build an exposure covariate. This covariate was used to improve predictions of precipitation patterns, such as orographic lift on windward facing slopes and rain shadows on leeward facing slopes of mountain ranges. The resulting product consists of monthly estimates of precipitation at a resolution of 2.5 arcminutes (approximately 4km) with global coverage. The new precipitation layers were integrated with existing data products for temperature, monthly historical anomalies layers for 110 years, and 90 future climate projections from the CMIP5 multimodel database into a comprehensive data package for Latin America. Estimates of more than 50 monthly, seasonal, and annual variables, including many biologically relevant climate variables such as growing and chilling degree days, beginning and end of the frost-free period, and drought indices are included. In total, the database includes approximately 18,000 climate surfaces that are accessible with a software front-end to query the database. I provide guidance for researchers and natural resource managers to select relevant climate variables, and future projections for climate change impact and adaptation planning and research. In collaboration with the Government of Mexico, I then use the new database to develop climate change adaptation strategies to guide reforestation and afforestation programs. I propose a new seed zone classification system that is based on bands of climate variables that are often related to local adaptation of tree populations of climate, delineating 32 zones that cover most of Mexico. I find that climate change observed over the last decades (1961-1990 reference period versus 1991-2015) has already resulted in substantial shifts of these seed zones towards warmer and drier conditions, with an additional shift of a similar magnitude expected by the 2050s. We recommend moving seed sources from warm, dry locations towards currently wetter and cooler planting sites, to compensate for climate change that has already occurred and is expected to continue for the next decades.

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