This file was created by scanning the printed publication. Errors identified by the software have been corrected; however, some errors may remain. An Accuracy Assessment of a Spatial Bioclimatic Model D.W. ~ c ~ e n n eB.G. ~ ' ,~ a c k e f ,M.F. ~ u t c h i n s o n R.A. ~ , Sims4 Abstract.-Spatially explicit models of forest ecosystems require the integration of biophysical data at a range of scales. Prediction of biotic response should be based on spatially explicit models of the driving environmental processes, in particular those related to climate and topography. Ongoing research in the forests of Ontario have focused on the development of suitably scaled digital elevation models and mesoscaled models of climatic averages. These in turn have been used to spatially extend various biological survey data. In this paper we present some accuracy assessments of the spatial climate models and subsequent differences in species' predicted distributions. INTRODUCTION Resource managers require reliable spatial information about species' distributions to help make decisions such as establishing monitoring sites or protected area networks. How important a particular landscape is to a species often depends on where else it does or could occur. Spatial information can help provide context. However considerable effort is required to develop these data. Typical resource inventories such as aerial photographs or remotely sensed data provide relatively little information about non-commercial andlor non-overstory species, so some other type of modelling is required. Theory would suggest that ecological response, such as a species' distribution, is a function of a complex myriad of factors including disturbance regimes, human inputs (eg. land use history), biological regimes (eg. interspecies competition) and a number of physical determinants. The latter include thermal, radiation, moisture and nutrient regimes. Data on climate, terrain and soil parent material are the basic inputs to modelling these regimes across landscapes. If spatial data on the ' Senior Economist, Canadian Forest Service - Sault Ste. Marie, hturio, Cmvlda Senior Lecturer, Department of Geography, The Australian National University, Canberra, Australia. Senior Fellow, Centrefor Resource and Environmental Studer, The Australian National University, Canberra Australia 4 Research Scientist, CaMdian Forat Service - Sault Ste. Marie, Ontario, Canada. regimes are available and species' responses can be calibrated with the regimes, then there becomes an enhanced capability to extrapolate information gained from analysis of field based observations. This type of empirical framework is directly relevant to the problem of spatially quantifying species' distributions, abundance, and productivity (eg. Mackey 1994). In this paper, some accuracy issues associated with a spatial climate model and the problem of extrapolating site data to quantify a species' (jack pine, Pinus banksiana) potential distribution are investigated. A SPATIAL CLIMATE MODEL FOR THE PROVINCE OF ONTARIO It is well accepted that climate exerts a strong control on both the distribution and productivity of plant species. However, correlations between various climate parameters and the ecological response of interest is often poor. One reason for this is that weather stations are located well away from field survey points and any estimates of climatic parameters involve some interpolation. Some relatively new methods for interpolating climate provide opportunities for generating more reliable spatial estimates of climatic parameters ex situ than previously possible (see Hutchinson 1995, 1987). Applications of these methods to estimate long term monthly mean climate values at points of survey and subsequently explain spatial variation in ecological response have been successful (eg. Nix 1986, Mackey 1994, Yee and Mitchell 1991). The methods make use of partial thin plate smoothing splines (Eq. 1) to interpolate historical weather station data: where f is an unknown smooth to be estimated, yjare a set of p known functions and the p, are a set of unknown parameters which have to be estimated. The x, generally represent coordinates in two or three dimensional space. The ei are zero mean random errors. Hutchinson (1995) describes the estimation procedures in detail. In applications to date, climate surfaces have been developed as a function of latitude, longitude and elevation (eg. Nix 1986, Mitchell 1991, Mackey et al. 1996) although any number of independent variables are possible in principal (eg. distance from a major water body). These three parameters are known to be important determinants of long-term climate. The resultant climate surfaces can also be mapped relatively easily in a GIs using software which couples the functions to a Digital Elevation Model (DEM) (see Hutchinson 1993). A DEM is a regular grid of latitude, longitude and elevation. The main advantage of the thin plate spline techniques over competing methods is that they do not require any a priori estimation of the spatial autocovariance structure. In addition, data smoothing can be optimized by minimizing the generalized cross validation statistic (Hutchinson 1995, Hutchinson and Gessler 1994). Mackey et al. (1996 in press) used these methods to generate climate surfaces for the province of Ontario as a function of x, y (latitude and longitude) and z (km above sea level). Table 1 summarizes the standard errors of the basic surfaces of monthly mean maximum temperature, monthly mean minimum temperature and monthly precipitation. Besides mapping the surfaces and secondary indices in a GIs by coupling the functions to a DEM, estimates of climatic values can be generated at field survey locations. The latitude, longitude and elevation (x, y and z) of each survey location are required. Hence, estimates of various climate variables can be estimated and climateiplant or planthima1 relations can be subsequently examined. Spatial predictions can be made by linking the statistical functions to spatial databases of the independent variables. Table l.-Number of data point. and approximate standard errors of fitted climate surfaces for Ontario. Climate Variable Number of Data Points monthly mean max. temp. 47 1 monthly mean min .temp. 47 1 Standard Error monthly precipitation (old surface) monthly precipitation (new surface) 470 (less Ignace) Climate Estimate Errors Associated With Incorrect Field Survey Locations Considerable effort has been placed on the collation of existing biological survey data in Ontario so that these methods can be applied. For example, there are now over 4100 forest ecosystem classification (FEC) plots in existence throughout the province (Sims and Uhlig 1992). These data are essentially vegetation surveys in which numerous characteristics were measured in 10 by 10 metre plots and included understorey and overstory plants, soil properties and some mensurational attributes. Some of the newer plots used GPS technology to record location data. If location information was not already recorded, the original survey records and topographic maps were used to record x, y and z (it is also possible to use a DEM to derive estimates of elevation if only x and y are available). The importance of accurate location information will vary spatially. For example; steeper climatic gradients will exist where elevation changes are more abrupt, or near very large water bodies such as the Great Lakes. To gauge the importance of location information across the province, an analysis of the location data associated with observations of jack pine was performed. Of the 4100 FEC plots, 951 contained jack pine. The x,y,z coordinates of each of these were perturbed in two ways. Figure 1 reports an example where the elevation (z) values were increased by 50 metres (elevations above sea level in Ontario range from 0 metres near Hudson Bay to over 610 metres northwest of Lake Superior). New climate values were generated with these increased elevation values and compared to the original estimates. Results for three climatic values (growing season length, maximum temperature of the hottest month and precipitation of the coldest quarter) are shown in figure 1. The original range of values at the 95 1 plots is also reported in the figure. In addition to the elevation perturbations, x and y coordinates were systematically altered. Figure 1 reports the results of increasing x and y by six minutes. Across the range of the plot locations, the implication in terms of distance is approximately 13 krn northwest of each "true" geographic plot position. The changes in elevation and latitudenongitude produced similar changes in growing season length. The changes in temperature and precipitation were consistent with the relative scaling of the input data. The differences from the original values are marginal. The biggest impact was on precipitation in the coldest quarter. In this case the elevation change caused a general decrease in values across all sites (from - 1.5% to -5.7% in relation to the original values). The lat/long shift resulted in a -6.9% to 9.9% change in relation to the original values. These differences are for the most part within the standard errors reported in table 1. The results illustrate the spatially varying dependency of climate on these 3 variables. SPATIAL PREDICTIONS OF THE RANGE OF JACK PINE IN ONTARIO As mentioned, the ability to generate grids of climatic values and estimates of climate at points of survey offer opportunities to develop spatial predictions of animal-plantklimate relations. A variety of approaches are possible depending on the nature of the biological site data including the use of statistical relationships (eg. presence of an organism = f (minimum temperature of the coldest month). An approach that has proven useful when only presence data is available is a grid matching procedure (Nix, 1986). In this case a climatic profile of the species is generated, based on observations of where it occurs and the estimates of the climatic values at each of those locations. This profile is then matched to grids of those climatic parameters. This is the BIOCLIMlBIOMAP procedure developed by Nix (1986) and colleagues at the Australian National University (McMahon et al. 1995). BIOCLIM makes use of climate surfaces, DEMs and species' location information to produce bioclimatic profiles. These profiles essentially describe the climatic conditions (eg. the range of mean annual temperatures, annual precipitation) sampled by a plot network. BIOMAP takes the BIOCLIM output a b 1%diff. = -0.6to 1.5 1 2 3 4 5 Growing season length (range of original values = I62 to 21 1 days) -0.6 -0.4 -0.2 0.0 0.2 0.4 -0.6 -0.4 -0.2 0.0 0.2 0.4 Maximum temperature of the hottest month (range of original values = 19.8to 26.5'C) 4 % diff. = -6.9to 9.9 800 -12 -8 % diff. = -1.5to -5.7 800 -4 0 4 8 12 -12 -8 -4 0 4 8 12 Precipitation of the coldest quarter (range of original values = 69 to 272 mm) Figure 1. Differences in three climate variable estimates at 951 FEC plots when perturbing 1atitudeAongitude +6 minutes (column a) and elevation +50 metres (column b). The range of differences as a percentage of original values is presented for each case (% dim. and identifies (flags) grid cells which match the bioclimate profile for the selected climatic variables. The resulting data can be input into a GIs and interpreted as a spatial prediction of the climatic domain of the species. In this way, landscapes can be identified which are climatically suitable for the species. Whether the species actually occurs will depend on the influence of other environmental processes. This approach to predicting the potential domain of a species is relatively transparent to interpretation. However there are limitations; all of the selected climatic variables used in the BIOMAP predictions are assumed to have an equal impact on the species' distribution. Statistical options are limited when the available data indicate only where a species is present; ie. there are no observations of where it is absent. BIOCLIM/BIOMAP is a repeatable and useful method for analyzing these types of survey data and can be easily updated with new observations. However the spatial prediction is also contingent on the representativeness of the underlying biological site data and quality of the climate surfaces. Figure 2a is a BIOMAP of jack pine based on analysis of 951 province-wide FEC plots. Future work by the authors will provide a detailed discussion of this type of analysis for a suite of commercial tree species in Ontario). A set of bioclimate parameters (eg. annual mean temperature, precipitation in the warmest and coldest quarters) derived from the monthly mean climate variables were coupled to a 1-kilometre DEM of the province in order to produce the BIOMAP. The resulting dark areas represent the "core" climate domain for the species. Grid cells are identified as a core area if all of their climatic parameter values fall within 10-90% of the survey data climatic values (ie. the 10-90 percentiles of the survey climatic parameters are the climatic conditions where most of the site data occur). The lightest shade of grey indicates those grid cells that fall within the range of climatic values defined by the survey data, indicating more marginal conditions. Noted in figure 2a is a "hole" near Ignace, Ontario, which suggests the area is outside the climatic domain of jack pine. This outcome has been noticed with BIOMAP analysis of several plants and animals and has been a source of some interest given an a priori expectation that the region would be quite suitable to many of the species. During an investigation of the spatial pattern of the bioclimatic parameters in that region, it was found that precipitation during the coldest quarter is significantly drier there than the surrounding area. Figure 3a shows the precipitation surface for that area in close detail and also identifies the locations of FEC plots in that part of the province. Clearly there are no FEC plots in this area. Thus, being an empirical approach, the BIOMAP output is doing what would be expected; producing a "hole" at those grid cells where at least one of the climate parameters has values outside the range of the climatic values for known occurrences of the species. An implication is that the area is outside the range of jack pine. Ontario, intermediate area (b) r// core area a yu i Figure 2. Climatic domain analysis for jack pine in Ontario using original precipitation surface (a) and new precipitation surface (b) Map (c) is the difference between (a) and (b). . One question is whether the underlying climate surface is robust (see figure 3a). However, the apparent anomaly is a reflection of the input data. It was determined that one weather station (Ignace), at the centre of the "hole" was reporting much drier conditions than nearby stations. For example, long-term November to April precipitation is 138mm compared to 171-212mm for nearby stations. As well as having a shorter history of record collection than other stations, further investigations revealed that Canadian Atmospheric Environment Service experts had several reasons to be suspicious of the quality of data from this particular station. In fact, this Ignace station is now closed. Given this, a new precipitation surface was created without the Ignace station data. Results are shown in figure 3b. The steep gradient associated with precipitation of the coldest quarter has been smoothed. The standard error of the monthly precipitation surfaces using data from 470 weather stations ranged from 3.3% to 8.2% compared to 3.2% to 7.6% (see table 1). The implication in terms of the predicted climatic range of jack pine is shown in figure 2b. The "Ignace hole" shown in figure 3a is now part of the core climatic range for jack pine. The difference between figures 2a and 2b is shown in figure 2c. Although there are some changes across the province, the most significant changes occurred in the Ignace area where grid cells that were flagged as outside the species range are now part of its core range. CONCLUDING COMMENTS Reliable, quantitative spatial data should ideally be used to help make assessments about the potential of landscapes to support particular species. Species distributions must be modelled through integration of various data types and sensitivity analysis of the results to input values should occur. Empirical models are generally driven by the input data, in this paper these are the climate and biological data. The results presented here reiterate one truism well known to ecological researchers: data quality control should be a paramount concern. However it is clear that some data quality issues will not arise without extensive investigation. Prior to the analysis of site data in conjunction with climate, we were quite satisfied with the Ontario climate model. In this case the quest to derive spatial information from site-based observations revealed an area of Ontario that lacked FEC plots and had questionable weather station data. From a narrower perspective, the analysis summarized in figure 1 suggests considerable scope to make use of historical field data in Ontario. There are numerous vegetation and wildlife survey datasets available in Ontario to which latitude, longitude and elevation could be appended. The analysis of differences in climate estimates in this paper should allay concerns about moderate errors in location records. Figure 3. Precipitation in coldest quarter at the 'Ignace hole' (48:45N--50:00N, 91:15W --92:45W) with nearby FEC plots overlaid.