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Abstract. Thin plate smoothing splines provide accurate, operationally straightforward and computationally efficient solutions to the problem of the spatial interpolation.of annual mean rainfall for a standard period from point data which contains many short period rainfall means. The analyses depend on developing a statistical model of the spatial variation of the observed rainfall means, considered as noisy estimates of standard period means. The error structure of this model has two components which allow separately for strong spatially correlated departures of observed short term means from standard period means and for uncorrelated deficiencies in the representation of standard period mean rainfall by a smooth function of position and elevation. Thin plate splines, with the degree of smoothing determining by minimising generalised cross validation, can estimate this smooth function in two ways. First, the spatially correlated error structure of the data can be accommodated directly by estimating the corresponding non-diagonal error covariance matrix. Secondly, spatial correlation in the data error structure can be removed by standardising the observed short term means to standard period mean estimates using linear regression. When applied to data both methods give similar interpolation accuracy, and error estimates of the fitted surfaces are in good agreement with residuals from withheld data. Simplified versions of the data error model, which require only minimal summary data at each location, are also presented. The interpolation accuracy obtained with these models is only slightly inferior to that obtained with more complete statistical models. It is shown that the incorporation of a continuous, spatially varying, dependence on appropriately scaled elevation makes a dominant contribution to surface accuracy. Incorporating dependence on aspect, as determined from a digital elevation model, makes only a marginal further improvement.

Aim Interest in species distribution models (SDMs) and related niche studies has increased dramatically in recent years, with several books and reviews being prepared since 2000. The earliest SDM studies are dealt with only briefly even in the books. Consequently, many researchers are unaware of when the first SDM software package (BIOCLIM) was developed and how a broad range of applications using the package was explored within the first 8 years following its release. The purpose of this study is to clarify these early developments and initial applications, as well as to highlight BIOCLIM's continuing relevance to current studies.Location Mainly Australia and New Zealand, but also some global applications.Methods We outline the development of the BIOCLIM package, early applications (1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991) and its current relevance.Results BIOCLIM was the first SDM package to be widely used. Early applications explored many of the possible uses of SDMs in conservation biogeography, such as quantifying the environmental niche of species, identifying areas where a species might be invasive, assisting conservation planning and assessing the likely impacts of climate change on species distributions.Main conclusions Understanding this pioneering work is worthwhile as BIOCLIM was for many years one of the leading SDM packages and remains widely used. Climate interpolation methods developed for BIOCLIM were used to create the WorldClim database, the most common source of climate data for SDM studies, and BIOCLIM variables are used in about 76% of recent published MAXENT analyses of terrestrial ecosystems. Also, some of the BIOCLIM studies from the late 1980s, such as measuring niche (both realized and fundamental) and assessing possible impacts of climate change, are still highly relevant to key conservation biogeography issues.

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The application of trivariate thin-plate smoothing splines to the interpolation of daily weather data is investigated. The method was used to develop spatial models of daily minimum and maximum temperature and daily precipitation for all of Canada, at a spatial resolution of 300 arc s of latitude and longitude, for the period 1961-2003. Each daily model was optimized automatically by minimizing the generalized cross validation. The fitted trivariate splines incorporated a spatially varying dependence on ground elevation and were able to adapt automatically to the large variation in station density over Canada. Extensive quality control measures were performed on the source data. Error estimates for the fitted surfaces based on withheld data across southern Canada were comparable to, or smaller than, errors obtained by daily interpolation studies elsewhere with denser data networks. Mean absolute errors in daily maximum and minimum temperature averaged over all years were 1.18 and 1.68C, respectively. Daily temperature extremes were also well matched. Daily precipitation is challenging because of short correlation length scales, the preponderance of zeros, and significant error associated with measurement of snow. A two-stage approach was adopted in which precipitation occurrence was estimated and then used in conjunction with a surface of positive precipitation values. Daily precipitation occurrence was correctly predicted 83% of the time. Withheld errors in daily precipitation were small, with mean absolute errors of 2.9 mm, although these were relatively large in percentage terms. However, mean percent absolute errors in seasonal and annual precipitation totals were 14% and 9%, respectively, and seasonal precipitation upper 95th percentiles were attenuated on average by 8%. Precipitation and daily maximum temperatures were most accurately interpolated in the autumn, consistent with the large well-organized synoptic systems that prevail in this season. Daily minimum temperatures were most accurately interpolated in summer. The withheld data tests indicate that the models can be used with confidence across southern Canada in applications that depend on daily temperature and accumulated seasonal and annual precipitation. They should be used with care in applications that depend critically on daily precipitation extremes.

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The Haliburton forest region in Ontario, Canada, with cumulus cloud formations. Photo by Mark Primavera, Natural Resources Canada. C limate is a fundamental driver of life. Plant and animal distribution, abundance, and productivity are all closely tied to environmental regimes driven by temperature, precipitation, and solar radiation patterns. Critical biological processes, such as plant bud burst, flowering, and migration, both of animal populations and vegetation communities, are also linked to climate and weather conditions. Furthermore, human activities in many sectors, including food production, building construction, recreation, and power generation (solar, wind, hydroelectric), are closely connected to climate.Not surprisingly, given the pervasive influence of climate, there is a high demand for reliable spatial climate data [indeed, this was very much the theme at the recent World Climate Conference 3: Better climate information for a better future (see www.wmo.int/wcc3/page_en.php); Munang et al. 2010]. In forestry and many other sectors, there is often a need for estimates well away from meteorological stations, which tend to be clustered near agricultural and urban areas. This need is met by "spatial" climate models, which can provide  Natural resources Canada, Canadian forest service, and their partners have developed spatial spline models and gridded datasets for North america for a wide variety of variables, time steps, and spatial resolutions.

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