Mean annual temperature and precipitation regimes of 42 forests in six regions. Because the climate may be quite similar in several forests within a region, several locations have highly overlapping symbols in this figure. 

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Context 1

... in study forests and their reference forests, we attribute to human interventions. Because we have conducted fieldwork in the forests in our sample and have collected available historical data, we can be confident that natural disasters have not played a recent role in the conditions of these forests. Thus, the procedure allows us to compare a range of forests and approximate the comparative extent of human influence. Almost all of the reference forests we used are from the data base collected by Alwyn Gentry over a period of about 20 years (Phillips and Miller 2002, the Spatial Analysis of Local Vegetation Indices Across Scales (SALVIAS) Project 2003). Gentry used multiple 0.1-ha transects to obtain quantitative forest data in carefully selected, undisturbed forests representing a wide range of forest types. Gentry selected each forest to be inventoried after extensive interviews in each region to ensure that these forests had experienced minimal human influence. He inventoried a total of 212 forests in 40 countries, although most of his data are from the Western Hemisphere (Gentry 1992, Phillips et al. 1994). Gentry’s methods are described in detail by Phillips and Miller (2002), and the complete data base is available upon request. In Nepal and Uganda, where Gentry did not make measurements, we consulted with scholars and foresters in the region to identify mature, undisturbed forests with forest mensuration data comparable to those of Gentry. To examine climatic differences among our study and reference forests, and to gain a fairly detailed climate dataset for all locations, we acquired global datasets for temperature and precipitation from the University of Delaware Center for Climatic Research website (Willmott and Matsuura 2004). These data are aggregated from monthly time-series data of 1950–1999, compiled from global climate stations and interpolated to produce gridded data at a resolution of 0.5° latitude x 0.5° longitude (Willmott and Feddema 1992, Willmott and Matsuura 1995, Willmott and Robeson 1995). The station coverage in more remote areas is relatively sparse and influences the accuracy of the interpolated values, but the accuracy can be increased by accounting for the elevations of stations (Willmott and Matsuura 1995). The adjusted station data were interpolated to produce a global dataset; then each grid cell value was adjusted according to the average lapse rate. Thus, the interpolated cell values to some degree control for differences in elevation as derived from coarse- scale DEM data. However, given the coarse scale of the DEM used in this process, grid cells with significant within-cell elevation variability may misrepresent the actual temperature regime for all areas in the cell. The precipitation data were interpolated without the elevation factor adjustment. When meteorological stations with extensive, reliable records existed at or near our sites, data from those stations were used. We estimated PET with the Thornthwaite-Mather method (Thornthwaite 1948, Thornthwaite and Mather 1955, 1957, Mather 1978), which uses mean monthly precipitation and temperature data and is adjusted for seasonality and latitude. This method provides a relatively straightforward way for linking local climates to vegetation. The precision of PET calculations can be increased by using more refined methods, such as those developed by Penman (1948), which recognize the role of humidity, wind speed, and radiation on evapotranspiration characteristics. However, those more complex equations require additional field data that were unavailable for this study. The forest types we studied include wet tropical forests, dry tropical forests, and temperate forests, with each forest type having a typical range of PET values (Aber and Melillo 2001), although orographic effects can produce significant differences locally. Wet tropical forests have high PET values, typically 1400 to 2200 mm of water per year. In climates where precipitation is abundant, the AET value is the same as the PET value. Wet tropical forests have abundant precipitation and solar radiation, resulting in year-long growing seasons with little or no water stress on vegetation. Broadleaf evergreen trees typically dominate these forests. In dry tropical forests, PET values typically are 800 to 1400 mm and AET is 70% to 90% of PET, usually with a pronounced dry season. Both coniferous and broadleaf trees occur in these forests. In wet temperate forests, AET and PET are similar, but less solar radiation and cooler temperatures reduce evaporation, and PET is lower at 700 to 800 mm. Coniferous trees typically dominate these forests. In dry temperate forests, PET values are 600 to 800 mm with AET about 90% of PET. Within this range, higher values result in deciduous forests, whereas coniferous forests typically occur at lower values. Our analysis examined climate data for each study forest and the corresponding reference forest in each region (Fig. 1). There is relatively little variation in annual temperatures and annual precipitation among our sites in the Midwest USA, Nepal, and Uganda. The sites in Bolivia and Brazil have little variance in annual temperatures but are more variable in annual precipitation. Sites in Mesoamerica are the most variable in both temperature and precipitation because of considerable variation in elevation. Two study forests in southern Mexico at approximately 2500 m elevation are cool and fairly dry, whereas a third study forest, in Guatemala, at near 1800 m elevation, is cool and fairly wet. The other study forests, in Guatemala and Honduras, have lower elevations, higher temperatures, and less precipitation. Both Whittaker’s (1975) classification of ecological zones and Holdridge’s (1947, 1967) “life zone” method are sometimes criticized for their similarly arbitrary delineation of zonal boundaries. Holdridge’s selection of a fairly high mean annual temperature to delineate tropical zones often causes lowland tropical rainforests in the tradewind zones to be omitted from tropical forest categories (Richards 1996). This is likely why our Ugandan sites are plotted as marginal dry tropical forests in the Whittaker diagram (Fig. 1), although such forests have tropical attributes and are typically classified as moist “semi-deciduous” or “semi- evergreen” lowland rainforest by localized forest classification systems (Lind and Morrison 1974, White 1983, Lovett and Wasser 1993, Richards 1996). Soil characteristics can affect the rates and types of forest growth that occur in particular climatological conditions. Soil nutrient availability, water retention, and soil genesis affect not only the rate of nutrient uptake but also species-specific soil suitability. Acquisition of detailed soils data is a particular challenge for a study such as this. Unfortunately, the Gentry reference data (Phillips and Miller 2002, SALVIAS Project 2003) do not include soil characteristics for each site. The soil information collected as part of the IFRI protocol is rudimentary, in part because the IFRI protocols are designed to be accessible to international researchers with modest technological resources. Existing secondary data sources of soils information for most sites are limited, at coarse spatial scale, and unlikely to represent the true spatial variability of characteristics in the region. In the absence of detailed soils data, we identified the soil taxonomic orders and suborders for each site using its geographic location and global soils databases (National Resources Cconservation Service (NRCS) 2005, Food and Agricultural Organization/United Nations Educational, Scientific, and Cultural Organization (FAO/UNESCO) 2006). Additional information about the characteristics of these soils came from Soil Survey Staff (1975) and Brady and Weil (1999). Although using higher- order soil classes lacks the specificity of soil characteristics present in each site, the use of these groups provides a basic mechanism to evaluate (1) whether the study forest has soil characteristics similar to the reference forest and (2) the range of soil classes represented within each Whittaker (1975) ecological zone. The soil suborders identified for three reference forests are the same as the study forests within the same ecological zones (Table 1). However, the suborders identified for three other reference forests differ from their associated study forests. In Bolivia, the reference forest has Orthents soil, whereas the study forests have Udepts (NRCS 2005, FAO/ UNESCO 2006). Thus, the soils in these study forests are likely from older parent material than the reference forest. Because Orthents have a shallow soil horizon (common in areas of steep, eroded topography), the reference forest may underestimate the state of mature forests in other soil conditions. In the Nepal sites, the soil suborder of the reference forest is Fluvent, whereas soils of the study forests are Ustepts (NRCS 2005, FAO/UNESCO 2006). Fluvent soils are alluvial Entisols commonly found in floodplains, whereas Usteps are Inceptisols of subhumid climates. The soils of the Brazilian study forests are Udox and Ustults, whereas Aquepts characterize the reference forest (NRCS 2005, FAO/UNESCO 2006). These Brazilian forests are not located in a moisture-limiting environment, so the differences in forest characteristics among the Udox, Ustult, and Aquept soils are likely small. To compare forest conditions among the study forests and the reference forest in each ecological and climatological zone, we developed an index to assess the extent of disturbance (Table 2). This index focuses on mature forests and uses data for trees with dbh >10 cm. Four equally weighted ratios are summed for the overall index of disturbance value. The scale ranges linearly from zero (maximum disturbance) to 4.00 (the reference forest). The first ratio is the total basal area of trees in a study forest to the ...

Context 2

... is available upon request. In Nepal and Uganda, where Gentry did not make measurements, we consulted with scholars and foresters in the region to identify mature, undisturbed forests with forest mensuration data comparable to those of Gentry. To examine climatic differences among our study and reference forests, and to gain a fairly detailed climate dataset for all locations, we acquired global datasets for temperature and precipitation from the University of Delaware Center for Climatic Research website (Willmott and Matsuura 2004). These data are aggregated from monthly time-series data of 1950–1999, compiled from global climate stations and interpolated to produce gridded data at a resolution of 0.5° latitude x 0.5° longitude (Willmott and Feddema 1992, Willmott and Matsuura 1995, Willmott and Robeson 1995). The station coverage in more remote areas is relatively sparse and influences the accuracy of the interpolated values, but the accuracy can be increased by accounting for the elevations of stations (Willmott and Matsuura 1995). The adjusted station data were interpolated to produce a global dataset; then each grid cell value was adjusted according to the average lapse rate. Thus, the interpolated cell values to some degree control for differences in elevation as derived from coarse- scale DEM data. However, given the coarse scale of the DEM used in this process, grid cells with significant within-cell elevation variability may misrepresent the actual temperature regime for all areas in the cell. The precipitation data were interpolated without the elevation factor adjustment. When meteorological stations with extensive, reliable records existed at or near our sites, data from those stations were used. We estimated PET with the Thornthwaite-Mather method (Thornthwaite 1948, Thornthwaite and Mather 1955, 1957, Mather 1978), which uses mean monthly precipitation and temperature data and is adjusted for seasonality and latitude. This method provides a relatively straightforward way for linking local climates to vegetation. The precision of PET calculations can be increased by using more refined methods, such as those developed by Penman (1948), which recognize the role of humidity, wind speed, and radiation on evapotranspiration characteristics. However, those more complex equations require additional field data that were unavailable for this study. The forest types we studied include wet tropical forests, dry tropical forests, and temperate forests, with each forest type having a typical range of PET values (Aber and Melillo 2001), although orographic effects can produce significant differences locally. Wet tropical forests have high PET values, typically 1400 to 2200 mm of water per year. In climates where precipitation is abundant, the AET value is the same as the PET value. Wet tropical forests have abundant precipitation and solar radiation, resulting in year-long growing seasons with little or no water stress on vegetation. Broadleaf evergreen trees typically dominate these forests. In dry tropical forests, PET values typically are 800 to 1400 mm and AET is 70% to 90% of PET, usually with a pronounced dry season. Both coniferous and broadleaf trees occur in these forests. In wet temperate forests, AET and PET are similar, but less solar radiation and cooler temperatures reduce evaporation, and PET is lower at 700 to 800 mm. Coniferous trees typically dominate these forests. In dry temperate forests, PET values are 600 to 800 mm with AET about 90% of PET. Within this range, higher values result in deciduous forests, whereas coniferous forests typically occur at lower values. Our analysis examined climate data for each study forest and the corresponding reference forest in each region (Fig. 1). There is relatively little variation in annual temperatures and annual precipitation among our sites in the Midwest USA, Nepal, and Uganda. The sites in Bolivia and Brazil have little variance in annual temperatures but are more variable in annual precipitation. Sites in Mesoamerica are the most variable in both temperature and precipitation because of considerable variation in elevation. Two study forests in southern Mexico at approximately 2500 m elevation are cool and fairly dry, whereas a third study forest, in Guatemala, at near 1800 m elevation, is cool and fairly wet. The other study forests, in Guatemala and Honduras, have lower elevations, higher temperatures, and less precipitation. Both Whittaker’s (1975) classification of ecological zones and Holdridge’s (1947, 1967) “life zone” method are sometimes criticized for their similarly arbitrary delineation of zonal boundaries. Holdridge’s selection of a fairly high mean annual temperature to delineate tropical zones often causes lowland tropical rainforests in the tradewind zones to be omitted from tropical forest categories (Richards 1996). This is likely why our Ugandan sites are plotted as marginal dry tropical forests in the Whittaker diagram (Fig. 1), although such forests have tropical attributes and are typically classified as moist “semi-deciduous” or “semi- evergreen” lowland rainforest by localized forest classification systems (Lind and Morrison 1974, White 1983, Lovett and Wasser 1993, Richards 1996). Soil characteristics can affect the rates and types of forest growth that occur in particular climatological conditions. Soil nutrient availability, water retention, and soil genesis affect not only the rate of nutrient uptake but also species-specific soil suitability. Acquisition of detailed soils data is a particular challenge for a study such as this. Unfortunately, the Gentry reference data (Phillips and Miller 2002, SALVIAS Project 2003) do not include soil characteristics for each site. The soil information collected as part of the IFRI protocol is rudimentary, in part because the IFRI protocols are designed to be accessible to international researchers with modest technological resources. Existing secondary data sources of soils information for most sites are limited, at coarse spatial scale, and unlikely to represent the true spatial variability of characteristics in the region. In the absence of detailed soils data, we identified the soil taxonomic orders and suborders for each site using its geographic location and global soils databases (National Resources Cconservation Service (NRCS) 2005, Food and Agricultural Organization/United Nations Educational, Scientific, and Cultural Organization (FAO/UNESCO) 2006). Additional information about the characteristics of these soils came from Soil Survey Staff (1975) and Brady and Weil (1999). Although using higher- order soil classes lacks the specificity of soil characteristics present in each site, the use of these groups provides a basic mechanism to evaluate (1) whether the study forest has soil characteristics similar to the reference forest and (2) the range of soil classes represented within each Whittaker (1975) ecological zone. The soil suborders identified for three reference forests are the same as the study forests within the same ecological zones (Table 1). However, the suborders identified for three other reference forests differ from their associated study forests. In Bolivia, the reference forest has Orthents soil, whereas the study forests have Udepts (NRCS 2005, FAO/ UNESCO 2006). Thus, the soils in these study forests are likely from older parent material than the reference forest. Because Orthents have a shallow soil horizon (common in areas of steep, eroded topography), the reference forest may underestimate the state of mature forests in other soil conditions. In the Nepal sites, the soil suborder of the reference forest is Fluvent, whereas soils of the study forests are Ustepts (NRCS 2005, FAO/UNESCO 2006). Fluvent soils are alluvial Entisols commonly found in floodplains, whereas Usteps are Inceptisols of subhumid climates. The soils of the Brazilian study forests are Udox and Ustults, whereas Aquepts characterize the reference forest (NRCS 2005, FAO/UNESCO 2006). These Brazilian forests are not located in a moisture-limiting environment, so the differences in forest characteristics among the Udox, Ustult, and Aquept soils are likely small. To compare forest conditions among the study forests and the reference forest in each ecological and climatological zone, we developed an index to assess the extent of disturbance (Table 2). This index focuses on mature forests and uses data for trees with dbh >10 cm. Four equally weighted ratios are summed for the overall index of disturbance value. The scale ranges linearly from zero (maximum disturbance) to 4.00 (the reference forest). The first ratio is the total basal area of trees in a study forest to the total basal area of trees in the reference forest. The second ratio is the mean dbh of trees in a study forest to the mean dbh of trees in the reference forest. The third ratio is the proportion of tree density to total density, including both trees and saplings, in a given study forest to the proportion of tree density to total density in the reference forest. The fourth ratio is the number of tree species observed in a study forest to the number of tree species observed in the reference forest. Thus, this index produces higher values for forests with large trees (higher mean dbh) that comprise higher proportions of total tree and sapling density and thus results in higher basal area. The fourth ratio produces higher values for study forests with more tree species observed. The species richness ratio indicates how the study forest varies from the reference forest. For some study forests, a species richness that is greater than the reference forest could indicate degradation, high numbers of secondary successional species, or invasive species. In this case, the ratio should be subtracted from the index. However, our study forests did not present these conditions. Fieldwork in the forests is crucial to interpret this ...