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Mapping wood volume in seasonally dry vegetation of Caatinga in Bahia State, Brazil

ABSTRACT

The Caatinga biome in Brazil comprises the largest and most continuous expanse of the seasonally dry tropical forest (SDTF) worldwide; nevertheless, it is among the most threatened and least studied, despite its ecological and biogeographical importance. The spatial distribution of volumetric wood stocks in the Caatinga and the relationship with environmental factors remain unknown. Therefore, this study intends to quantify and analyze the spatial distribution of wood volume as a function of environmental variables in Caatinga vegetation in Bahia State, Brazil. Volumetric estimates were obtained at the plot and fragment level. The multiple linear regression techniques were adopted, using environmental variables in the area as predictors. Spatial modeling was performed using the geostatistical kriging approach with the model residuals. The model developed presented a reasonable fit for the volume m3 ha with r2 of 0.54 and Root Mean Square Error (RMSE) of 10.9 m3 ha–1. The kriging of ordinary residuals suggested low error estimates in unsampled locations and balance in the under and overestimates of the model. The regression kriging approach provided greater detailing of the global wood volume stock map, yielding volume estimates that ranged from 0.01 to 109 m3 ha–1. Elevation, mean annual temperature, and precipitation of the driest month are strong environmental predictors for volume estimation. This information is necessary to development action plans for sustainable management and use of the Caatinga SDTF in Bahia State, Brazil.

seasonally dry tropical forests; regression kriging; geostatistical modeling

Introduction

Seasonally dry tropical forests (SDTF) harbor a significant proportion of global plant biodiversity ( Silva et al., 2017Silva, J.M.; Leal, I.; Tabarelli, M. 2017. Caatinga: The Largest Tropical Dry Forest Region in South America. Springer, Berlin, Germany. ; Banda-R et al., 2016; Lima et al., 2021Lima, T.L.; Silva, J.A.A.; Longhi, R.V.; Gallo, R.; Hakamada, R.E.; Santos, M.V.F.; Molina, J.R.M.; Machuca, M.A.H.; Muir, J.P.; Ferreira, R.L.C. 2021. Structure, survival, and species diversity in a tropical dry forest submitted to coppicing. Forest Ecology and Management 501: 119700. https://doi.org/10.1016/j.foreco.2021.119700
https://doi.org/10.1016/j.foreco.2021.11...
), contributing extensively to biogeochemical cycles and providing numerous ecosystem services, including water quality control, carbon storage, and carbon sequestration ( Althoff et al., 2016Althoff, T.D.; Menezes, R.S.C.; Carvalho, A.L.; Siqueira Pinto, A.; Santiago, G.A.C.F.; Ometto, J.P.H.B.; von Randow, C.; Sampaio, E.V.S.B. 2016. Climate change impacts on the sustainability of the firewood harvest and vegetation and soil carbon stocks in a tropical dry forest in Santa Teresinha Municipality, northeast Brazil. Forest Ecology and Management 360: 367-375. https://doi.org/10.1016/j.foreco.2015.10.001
https://doi.org/10.1016/j.foreco.2015.10...
; Siyum, 2020Siyum, Z.G. 2020. Tropical dry forest dynamics in the context of climate change: syntheses of drivers, gaps, and management perspectives. Ecological Processes 9: 25. https://doi.org/10.1186/s13717-020-00229-6
https://doi.org/10.1186/s13717-020-00229...
). The knowledge of ecological patterns and processes in SDTF, including for the Caatinga region in northeastern Brazil, has grown gradually in recent years ( Barros et al., 2021Barros, M.F.; Ribeiro, E.M.S.; Vanderlei, R.S.; Paula, A.S.; Silva, A.B.; Wirth, R.; Cianciaruso, M.V.; Tabarelli, M. 2021. Resprouting drives successional pathways and the resilience of Caatinga dry forest in human-modified landscapes. Forest Ecology and Management 482: 118881. https://doi.org/10.1016/j.foreco.2020.118881
https://doi.org/10.1016/j.foreco.2020.11...
); nevertheless, these ecosystems remain chronically understudied. Despite their ecological and biogeographical importance ( Queiroz et al., 2017Queiroz, L.P.; Cardoso, D.; Fernandes, M.F.; Moro, M.F. 2017. Diversity and Evolution of Flowering Plants of the Caatinga Domain. p. 23-63. In: Silva, J.M.C.; Leal, I.R.; Tabarelli, M. eds. Caatinga: The Largest Tropical Dry Forest Region in South America. Springer International Publishing, Berlin. https://doi.org/10.1007/978-3-319-68339-3_2
https://doi.org/10.1007/978-3-319-68339-...
; Pennington et al., 2006Pennington, R.T.; Richardson, J.E.; Lavin, M. 2006. Insights into the historical construction of species-rich biomes from dated plant phylogenies, neutral ecological theory and phylogenetic community structure. New Phytologist 172: 605-616. https://doi.org/10.1111/j.1469-8137.2006.01902.x
https://doi.org/10.1111/j.1469-8137.2006...
), SDTF are among the most threatened forest ecosystems and may be thus at greater risk than humid forests ( Apgaua et al., 2015Apgaua, D.M.G.; Pereira, D.G.S.; Santos, R.M.; Menino, G.C.O.; Pires, G.G.; Fontes, M.A.L.; Tng, D.Y.P. 2015. Floristic variation within seasonally dry tropical forests of the Caatinga Biogeographic Domain, Brazil, and its conservation implications. International Forestry Review 17: 33-44. https://doi.org/10.1505/146554815815834840
https://doi.org/10.1505/1465548158158348...
; Lima et al., 2017Lima, R.B.; Alves Júnior, F.T.; Oliveira, C.P.; Silva, J.A.A.; Ferreira, R.L.C. 2017. Predicting of biomass in Brazilian tropical dry forest: a statistical evaluation of generic equations. Anais da Academia Brasileira de Ciências 89: 1815-1828. https://doi.org/10.1590/0001-3765201720170047
https://doi.org/10.1590/0001-37652017201...
; Sunderland et al., 2015Sunderland, T.; Apgaua, D.; Baldauf, C.; Blackie, R.; Colfer, C.; Cunningham, A.B.; et al. 2015. Global dry forests: a prologue. International Forestry Review 17: 1-9. https://doi.org/10.1505/146554815815834813
https://doi.org/10.1505/1465548158158348...
). The current spatial understanding of the SDTF structure has been largely generated using remote sensing approaches that provide spatially explicit values related to forest area, canopy cover, topography, soil, and climate variables. This information is widely used in statistical and geostatistical models to generate predictive maps of forest attributes ( Crowther et al., 2015Crowther, T.W.; Glick, H.B.; Covey, K.R.; Bettigole, C.; Maynard, D.S.; Thomas, S.M.; et al. 2015. Mapping tree density at a global scale. Nature 525: 201-205. https://doi.org/10.1038/nature14967
https://doi.org/10.1038/nature14967...
; Oliveira et al., 2019Oliveira, G.C.; Francelino, M.R.; Arruda, D.M.; Fernandes-Filho, E.I.; Schaefer, C.E.G.R. 2019. Climate and soils at the Brazilian semiarid and the forest-Caatinga problem: new insights and implications for conservation. Environmental Research Letters 14: 104007. https://doi.org/10.1088/1748-9326/ab3d7b
https://doi.org/10.1088/1748-9326/ab3d7b...
). These maps have improved our understanding of the morphoclimatic characteristics of the Caatinga biome; however, they do not provide population estimates, densities, or wood volume stocks, which are essential factors for sustainable forest management. An accurate analysis of the spatial distribution of timber stock of Caatinga dry vegetation from a local perspective is necessary to guide action plans aimed at environmental sustainability, especially in Bahia State, Brazil. Geostatistical modeling is an alternative mainly because it can predict a variable of interest in unsampled locations and map its spatial distribution using interpolated information from a sampled area ( Seidel and Oliveira, 2016Seidel, E.J.; Oliveira, M.S. 2016. A classification for a geostatistical index of spatial dependence. Revista Brasileira de Ciência do Solo 40: e0160007. https://doi.org/10.1590/18069657rbcs20160007
https://doi.org/10.1590/18069657rbcs2016...
). As the volumetric stock of seasonally dry vegetation across the Caatinga is correlated with environmental covariates ( Silveira et al., 2019Silveira, E.M.O.; Reis, A.A.; Terra, M.C.N.S.; Withey, K.D.; Mello, J.M.; Acerbi-Júnior, F.W.; Ferraz Filho, A.C.; Mello, C.R. 2019. Spatial distribution of wood volume in Brazilian savannas. Anais da Academia Brasileira de Ciências 91: e20180666. https://doi.org/10.1590/0001-3765201920180666
https://doi.org/10.1590/0001-37652019201...
), this study aims to quantify and spatialize volumetric wood stock for Caatinga vegetation in Bahia State to support more sustainable forest management. We analyzed the potential influence of key environmental variables on the spatial behavior of ecosystem wood volume and created predictive maps of the potential volume distribution for the entire Caatinga area of Bahia State.

Materials and Methods

The study site comprises 54 % of the state of Bahia, Brazil. We used forest inventory data conducted from 868 plots distributed in 40 fragments of Caatinga dry vegetation (42°5’ W; 13°7’ S, altitude 545 m) ( Figure 1 ). The average altitude is around 200 m and the highest point is 2,033 m above sea level, located in Serra do Barbado, Bahia, Brazil. The study region comprises a mosaic of thorny shrubs and mostly SDTF biome ( Queiroz et al., 2017Queiroz, L.P.; Cardoso, D.; Fernandes, M.F.; Moro, M.F. 2017. Diversity and Evolution of Flowering Plants of the Caatinga Domain. p. 23-63. In: Silva, J.M.C.; Leal, I.R.; Tabarelli, M. eds. Caatinga: The Largest Tropical Dry Forest Region in South America. Springer International Publishing, Berlin. https://doi.org/10.1007/978-3-319-68339-3_2
https://doi.org/10.1007/978-3-319-68339-...
). There is a wide variation of typologies and landscapes on a local scale, which mainly reflect variations in water availability (IBGE, 2019).

Figure 1
– Distribution of plot data by fragments, obtained for Caatinga in the state of Bahia, northeastern Brazil.

The predominant climate is BSwh, BSh, and BWh (Köppen classification), characterized by arid climate with a rainy season in the summer and well-defined dry periods in the winter, with precipitation below 500 mm and average temperature above 18 °C ( Alvares et al., 2013Alvares, C.A.; Stape, J.L.; Sentelhas, P.C.; Moraes Gonçalves, J.L.; Sparovek, G. 2013. Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift 22: 711-728. https://doi.org/10.1127/0941-2948/2013/0507
https://doi.org/10.1127/0941-2948/2013/0...
). The soils that occur in more significant proportions are Latosols, Neosols, Ultisols, Plintosols, and Luvisols ( Santos, 2018Santos, H.G. 2018. Brazilian system of soil classification = Sistema brasileiro de classificação de solos. 5ed. Embrapa, Brasília, DF, Brazil (in Portuguese). ).

Data analysis

The dendrometric data were obtained from forest inventories distributed in 40 fragments. The inventories were carried out in plots assembled from three different sources, Inema, RMFC, and ForestPlots.net (ForestPlots.net et al., 2021), representing research and local consultancies covering the broad aspect of the caatinga vegetation in Bahia State ( Table 1 ).

Table 1
– The database obtained, collection locations, and numbers of plots.

Plot sizes varied according to the objectives of each acquisition source ( Lopez-Gonzalez et al., 2009Lopez-Gonzalez, G.; Lewis, S.L.; Burkitt, M.; Baker T.R.; Phillips, O.L. 2009. ForestPlots.net Database. Available at: www.forestplots.net [Accessed Nov 17, 2021]
www.forestplots.net...
). Most plots had a standard size of 20 × 20 m (400 m2), while others were 10 × 20 m (200 m2), 20 × 30 m (600 m2), 15 × 50 (7500 m2), 20 × 50 (1000 m2), and 100 × 100 m (1 ha). The inclusion diameter was not fixed and the initial CBH (circumference measured at breast height) value showed variation within a range of 4 to 15.7 cm, which was measured on the circumference at 1.30 m above ground level. For better research development, the inclusion diameter CBH ≥ 10 cm was standardized and the extreme outlier values were removed. According to the typologies and fragments in the sampled points, local equations already developed and consolidated were applied to the vegetation divided into arboreal and shrubby Caatinga, which presented R2 of 0.98, for arboreal caatinga: V=9.53089×dbh2.00951×h0.84063 and for shrubby caatinga: V=9.33235×dbh2.01714×h0.66644 . A local equation developed by Pereira et al . (2021) was used for the data referring to the Contendas do Sincorá National Forest, which used the Linearized Spurr model specifically for the locality in question with R2 of 0.86, described according to the equation: LnV=9.935921+1.026668Lndbh2×h . Where, V = volume (m3); dbh = diameter breast height of 1.30 cm from the ground (cm); h = height (m).

The sites were verified for tree and shrub formations in Caatinga and Caatinga/Cerrado transition areas. Due to the existing volumetric variability at the plot level, the data were grouped at the fragment level and converted to one hectare equivalent (m3 ha–1) to obtain more accurate estimates on a broader scale. After estimating the volumetric stock, the data were associated to a set of predictor variables at each location. We selected 47 geospatial covariates grouped into different subsets: topoclimatic, land cover, and ecosystem heterogeneity. The covariates were obtained using satellite remote sensing and globally distributed terrestrial weather stations in raster format. Each raster layer is a spatially explicit grid image, where each pixel represents the value of the covariate described.

Then, the individual volume of the trees was obtained using local topoclimatic variables composed of elevation (relief of the area) and the set of 19 bioclimatic variables ( Table 2 ) from the WorldClim 2.1 database (https://www.worldclim.org/data/worldclim21.html), obtained with a resolution of 1 km2 ( Fick and Hijmans, 2017Fick, S.E.; Hijmans, R.J. 2017. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37: 4302-4315. https://doi.org/10.1002/joc.5086
https://doi.org/10.1002/joc.5086...
). The geographic and topographic variables were extracted from the map provided by NASA-SRTM (Shuttle Radar Topography Mission) with a spatial resolution of 100 m, referring to the area altitude (elevation). The latitude and longitude covariates were obtained from databases collected in the field.

Table 2
– Bioclimatic variables, latitude, and longitude used to adjust the volumetric prediction model in the Caatinga in the state of Bahia, northeastern Brazil.

The covariates of land cover and spatial heterogeneity of the global habitat were obtained from the EarthEnv platform (http://www.earthenv.org//). The information refers to global products of land cover and ecosystem heterogeneity from remote sensing at 30 arcseconds (1 km) resolution. Itprovides consensus information on 12 land cover classes at 1 km resolution (available in full and reduced) and texture characteristics of Enhanced Vegetation Index (EVI) acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) ( Table 3 ) ( Tuanmu and Jetz, 2015Tuanmu, M.-N.; Jetz, W. 2015. A global, remote sensing-based characterization of terrestrial habitat heterogeneity for biodiversity and ecosystem modelling. Global Ecology and Biogeography 24: 1329-1339. https://doi.org/10.1111/geb.12365
https://doi.org/10.1111/geb.12365...
).

Table 3
– Land cover variables and spatial heterogeneity of the global habitat used to adjust the volumetric prediction model in the Caatinga in the state of Bahia, northeastern Brazil.

All variables that fit the model, except for latitude and longitude, were processed in the R software (R Core Team, 2021). To obtain the values corresponding to each study site (at the plot and/or fragment level), the geospatial covariates were initially obtained for the state of Bahia and later, the extraction of environmental values was performed using the coordinates of each site. For each point sampled using the raster:extract function of the R raster package ( Rocchini et al., 2021Rocchini, D.; Thouverai, E.; Marcantonio, M.; Iannacito, M.; Da Re, D.; Torresani, M.; et al. 2021. Rasterdiv: an Information Theory tailored R package for measuring ecosystem heterogeneity from space; to the origin and back. Methods in Ecology and Evolution 12: 1093-1102. https://doi.org/10.1111/2041-210X.13583
https://doi.org/10.1111/2041-210X.13583...
) in each raster file of the geospatial covariates. This information was then stored and saved in a final matrix and used as predictor variables in the model.

The multiple linear regression model was used at first to create a spatial forecast of the volumetric stock: Y=α+β1X1+β2X2++βnXn+ε . Where, Y = volume (m3 ha–1); α: model intercept; β: regression coefficients, X: environmental variables; ε: random error. No marked independence was assumed within the full set of biophysical variables extracted from the raster layers compiled from the GIS (Geographic Information System) due to the inherently interactive nature of climate, topography, land cover, and environmental heterogeneity in the study site. However, the variance inflation value (VIF) was calculated for each covariate to explain any collinearity between the geospatial covariates and those with a VIF value ≥ 5 were excluded. Then, the step-by-step variable selection procedure was applied using the stepAIC function from R’s car package ( Weisberg and Fox, 2010Weisberg, S.; Fox, J. 2010. An R Companion to Applied Regression. SAGE, Los Angeles CA, USA. ). This function builds all possible candidate submodels nested in the global model, identifies the most plausible subset of covariates, and ranks them according to the corrected values of the Akaike Information Criterion (AICc) and the likelihood weights of the AIC (AICcw). The final equation was verified for the assumptions of independence, normality, and homoscedasticity of the residuals via the graphical analysis and by the Durbin-Watson, Shapiro-Wilk, and Breusch-Pagan tests, respectively. The coefficient of determination (R2) and standard error measurements of the estimate (RMSE) of the final equation was analyzed to ensure that the model was adequate. This modeling aimed to establish an explanatory model of the volumetric stock based on geospatial covariates that potentially govern the spatial distribution of volume in the Caatinga vegetation in Bahia State.

The spherical, exponential, and Gaussian geostatistical theoretical models were adjusted to the experimental semivariograms from the residuals of the final equation. In this procedure, the methods of Maximum Likelihood, Ordinary Least Squares, and Weighted Least Squares were adopted, considering the stationarity assumption of the intrinsic hypothesis ( a ) ( Goovaerts and Goovaerts, 1997Goovaerts, P.; Goovaerts, D.C. 1997. Geostatistics for Natural Resources Evaluation. Oxford University Press, Oxford, UK. ).

Table 4
– Theoretical semivariogram models adjusted to assess the spatial dependence of the Caatinga volume for the state of Bahia, northeastern Brazil.

Cross-validation was used as a criterion, calculating the reduced mean error (EMR) and the standard deviation of the reduced mean error (SER), and overestimation of the model to evaluate the performance and select the semivariogram model that best fits the data set ( Morais et al., 2017Morais, V.A.; Mello, J.M.; Mello, C.R.; Silva, C.A.; Scolforo, J.R.S. 2017. Spatial distribution of the litter carbon stock in the Cerrado biome in Minas Gerais state, Brazil. Ciência e Agrotecnologia 41: 580-589. https://doi.org/10.1590/1413-70542017415006917
https://doi.org/10.1590/1413-70542017415...
). In the adjustment of the theoretical models of the experimental semivariograms, the parameters nugget effect (τ2), threshold (σ2), and reach (ϕ) were determined. For the analysis of the degree of Spatial Dependence (DE) the relation τ2 / (τ2 σ2) and the intervals proposed by Cambardella et al. (1994)Cambardella, C.A.; Moorman, T.B.; Novak, J.M.; Parkin, T.B.; Karlen, D.L.; Turco, R.F.; Konopka, A.E. 1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal 58: 1501-1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x
https://doi.org/10.2136/sssaj1994.036159...
, which consider it a strong spatial dependence (DE < 25 %), moderate (25 % < ED < 75 %), and weak (DE > 75 %). From the selected spatial model, the regression residuals were interpolated by ordinary kriging, obtaining the residual map.

To apply the regression kriging, continuous georeferenced cells with dimensions of 100 × 100 m were created for the environmental covariates selected in the resulting volumetric equation with the aid of the ArcMap 10.5 program ( ESRI, 2019ESRI. 2019. Arcgis Desktop: Version 10.8. Environmental Systems Research Institute, Redlands, CA, USA. ). Each cell contained the information of the predictor variables. However, this map presents trends in the estimates and the residual map of ordinary kriging was added through map algebra for its correction, obtaining the final, unbiased map of the volume spatial distribution for the entire Caatinga area in the state of Bahia.

For the predictive validation of ordinary kriging, Standardized Mean (MS), Root Mean Standardized Error Square (RMSS), Mean Standard Error (ASE), Square Root of Mean Error (RMS) were calculated from cross-validation, which provided accuracy of the estimates, based on the data set ( Barni et al., 2016Barni, P.E.; Manzi, A.O.; Condé, T.M.; Barbosa, R.I.; Fearnside, P.M. 2016. Spatial distribution of forest biomass in Brazil’s state of Roraima, northern Amazonia. Forest Ecology and Management 377: 170-181. https://doi.org/10.1016/j.foreco.2016.07.010
https://doi.org/10.1016/j.foreco.2016.07...
; Silveira et al., 2019Silveira, E.M.O.; Reis, A.A.; Terra, M.C.N.S.; Withey, K.D.; Mello, J.M.; Acerbi-Júnior, F.W.; Ferraz Filho, A.C.; Mello, C.R. 2019. Spatial distribution of wood volume in Brazilian savannas. Anais da Academia Brasileira de Ciências 91: e20180666. https://doi.org/10.1590/0001-3765201920180666
https://doi.org/10.1590/0001-37652019201...
). All computations and analyses were performed with the support of R’s geoR package (Ribeiro Junior et al., 2001) and the final maps were generated in ArcGIS version 10.5 ( ESRI, 2019ESRI. 2019. Arcgis Desktop: Version 10.8. Environmental Systems Research Institute, Redlands, CA, USA. ).

Results and Discussion

The average volume of wood estimated by forest inventory was 25.82 m3 ha–1 (± 19.51). The minimum value was 0.16 m3 ha–1, while the maximum was 173.64 m3 ha–1, resulting in high variability for the vegetation studied (CV = 75.6 %) ( Figure 2 ). This high variability is expected for the Caatinga dry vegetation because the ecophysiological conditions that promote tree trunk growth and shape are functionally related to specific environmental factors, such as water stress, temperature, soil type, and topography ( Chaturvedi et al., 2013Chaturvedi, R.K.; Raghubanshi, A.S.; Singh, J.S. 2013. Growth of tree seedlings in a tropical dry forest in relation to soil moisture and leaf traits. Journal of Plant Ecology 6: 158-170. https://doi.org/10.1093/jpe/rts025
https://doi.org/10.1093/jpe/rts025...
; Dexter et al., 2018Dexter, K.G.; Pennington, R.T.; Oliveira-Filho, A.T.; Bueno, M.L.; Miranda, P.L.S.; Neves, D.M. 2018. Inserting tropical dry forests into the discussion on biome transitions in the tropics. Frontiers in Ecology and Evolution 6: 1-7. https://doi.org/10.3389/fevo.2018.00104
https://doi.org/10.3389/fevo.2018.00104...
; García-Cervigón et al., 2020García-Cervigón, A.I.; Camarero, J.J.; Cueva, E.; Espinosa, C.I.; Escudero, A. 2020. Climate seasonality and tree growth strategies in a tropical dry forest. Journal of Vegetation Science 31: 266-280. https://doi.org/10.1111/jvs.12840
https://doi.org/10.1111/jvs.12840...
; Mattos et al., 2015Mattos, P.P.; Braz, E.M.; Domene, V.D.; Sampaio, E.V.S.B.; Gasson, P.; Pareyn, F.G.C. Alvarez, I.A.; Baracat, A.; Araújo, E.L. 2015. Climate-tree growth relationships of Mimosa tenuiflora in seasonally dry tropical forest, Brazil. Cerne 21: 141-149. https://doi.org/10.1590/01047760201521011460
https://doi.org/10.1590/0104776020152101...
). Therefore, the combination of these factors can result in locations with high volume concentration while other areas may have a lower volumetric stock distribution ( Reis et al., 2020Reis, A.A.; Diniz, J.M.F.S.; Acerbi Júnior, F.W.; Mello, J.M.; Batista, A.P.B.; Ferraz Filho, A.C. 2020. Modeling the spatial distribution of wood volume in a Cerrado Stricto Sensu remnant in Minas Gerais state, Brazil. Scientia Forestalis 48: e2844. https://doi.org/10.18671/scifor.v48n125.15
https://doi.org/10.18671/scifor.v48n125....
; Silveira et al., 2019Silveira, E.M.O.; Reis, A.A.; Terra, M.C.N.S.; Withey, K.D.; Mello, J.M.; Acerbi-Júnior, F.W.; Ferraz Filho, A.C.; Mello, C.R. 2019. Spatial distribution of wood volume in Brazilian savannas. Anais da Academia Brasileira de Ciências 91: e20180666. https://doi.org/10.1590/0001-3765201920180666
https://doi.org/10.1590/0001-37652019201...
).

Figure 2
– Wood volume stock distribution of Caatinga vegetation in Bahia State, northeastern Brazil.

The multiple regression analysis confirms these hypotheses by suggesting that all parameters related to the selected environmental variables are significant ( p < 0.05) ( Table 5 ) and the residuals show homogeneity of variance ( Figure 3A ) and are normally distributed (Shapiro-Wilk p < 0.01; Figure 3B ). The total variation of volumetric data explained by regression (R2) was 54 %, while the root mean square error (RMSE) was 10.9 m3 ha–1. These values are considered acceptable due to the significant variation found for wood volume in the region and agree with other studies ( Almeida et al., 2014Almeida, A.Q.; Mello, A.A.; Neto, A.L.D.; Ferraz, R.C. 2014. Empiric relations between dendrometric characteristics of the Brazilian dry forest and Landsat 5 TM data. Pesquisa Agropecuária Brasileira 49: 306-315. https://doi.org/10.1590/S0100-204X2014000400009
https://doi.org/10.1590/S0100-204X201400...
; Reis et al., 2020Reis, A.A.; Diniz, J.M.F.S.; Acerbi Júnior, F.W.; Mello, J.M.; Batista, A.P.B.; Ferraz Filho, A.C. 2020. Modeling the spatial distribution of wood volume in a Cerrado Stricto Sensu remnant in Minas Gerais state, Brazil. Scientia Forestalis 48: e2844. https://doi.org/10.18671/scifor.v48n125.15
https://doi.org/10.18671/scifor.v48n125....
; Silveira et al., 2019)Silveira, E.M.O.; Reis, A.A.; Terra, M.C.N.S.; Withey, K.D.; Mello, J.M.; Acerbi-Júnior, F.W.; Ferraz Filho, A.C.; Mello, C.R. 2019. Spatial distribution of wood volume in Brazilian savannas. Anais da Academia Brasileira de Ciências 91: e20180666. https://doi.org/10.1590/0001-3765201920180666
https://doi.org/10.1590/0001-37652019201...
. In similar ecosystems, these studies found variations in the R2 value of 53 %, 55 %, and 60 %, respectively, and slightly larger error measurements.

Table 5
– Estimated coefficients in multiple linear regression and statistical significance (t-test a < 0.05).

Figure 3
– Regression residuals and estimated wood volume (A); distribution of the residual relative to the normal distribution on a reference line (B).

The modeling captures this significant variation due to the incorporation of environmental variables into the equation. Therefore, vegetation volume stocks are possibly not explained only by the dendrometric data of diameter and height but also by the inherently site-specific environmental conditions, such as temperature, precipitation, and altitude ( Balvanera and Aguirre, 2006Balvanera, P.; Aguirre, E. 2006. Tree diversity, environmental heterogeneity, and productivity in a Mexican tropical dry forest. Biotropica 38: 479-491. https://doi.org/10.1111/j.1744-7429.2006.00161.x
https://doi.org/10.1111/j.1744-7429.2006...
; Song et al., 2021Song, X.; Cao, M.; Li, J.; Kitching, R.L.; Nakamura, A.; Laidlaw, M.J.; et al. 2021. Different environmental factors drive tree species diversity along elevation gradients in three climatic zones in Yunnan, southern China. Plant Diversity 43: 433-443. https://doi.org/10.1016/j.pld.2021.04.006
https://doi.org/10.1016/j.pld.2021.04.00...
; Tilk et al., 2017Tilk, M.; Tullus, T.; Ots, K. 2017. Effects of environmental factors on the species richness, composition and community horizontal structure of vascular plants in Scots pine forests on fixed sand dunes. Silva Fennica 51: 3. https://doi.org/10.14214/sf.6986
https://doi.org/10.14214/sf.6986...
) or even other ecological factors intrinsically linked to vegetation types, such as different successional stages, structures, diversity, and anthropization levels ( Segura et al., 2002Segura, G.; Balvanera, P.; Durán, E.; Pérez, A. 2002. Tree community structure and stem mortality along a water availability gradient in a Mexican tropical dry forest. Plant Ecology 169: 259-271. https://doi.org/10.1023/A:1026029122077
https://doi.org/10.1023/A:1026029122077...
; Álvarez-Yépiz et al., 2008Álvarez-Yépiz, J.C.; Martínez-Yrízar, A.; Búrquez, A.; Lindquist, C. 2008. Variation in vegetation structure and soil properties related to land use history of old-growth and secondary tropical dry forests in northwestern Mexico. Forest Ecology and Management 256: 355-366. https://doi.org/10.1016/j.foreco.2008.04.049
https://doi.org/10.1016/j.foreco.2008.04...
; Lima et al., 2017Lima, R.B.; Alves Júnior, F.T.; Oliveira, C.P.; Silva, J.A.A.; Ferreira, R.L.C. 2017. Predicting of biomass in Brazilian tropical dry forest: a statistical evaluation of generic equations. Anais da Academia Brasileira de Ciências 89: 1815-1828. https://doi.org/10.1590/0001-3765201720170047
https://doi.org/10.1590/0001-37652017201...
; Rito et al., 2017Rito, K.F.; Arroyo-Rodríguez, V.; Queiroz, R.T.; Leal, I.R.; Tabarelli, M. 2017. Precipitation mediates the effect of human disturbance on the Brazilian Caatinga vegetation. Journal of Ecology 105: 828-838. https://doi.org/10.1111/1365-2745.12712
https://doi.org/10.1111/1365-2745.12712...
).

In general, the average annual temperature (BIO1), precipitation of the driest month (BIO14), and the relief of the area are prominent factors for the variation in wood volume in the Caatinga in Bahia State ( Figure 4 ). In tropical dry forests, these specific variables directly influence the environmental stress of the area, controlling water availability in the soil and in the plant ( Ocón et al., 2021Ocón, J.P.; Ibanez, T.; Franklin, J.; Pau, S.; Keppel, G.; Rivas-Torres, G.; Shin, M.E.; Gillespie, T.W. 2021. Global tropical dry forest extent and cover: a comparative study of bioclimatic definitions using two climatic data sets. PLOS One 16: e0252063. https://doi.org/10.1371/journal.pone.0252063
https://doi.org/10.1371/journal.pone.025...
), and reflecting in growth patterns, trunk shape, and consequently in wood volume ( Brandeis et al., 2005Brandeis, T.J.; Kuegler, O.; Knowe, S.A. 2005. Equations for merchantable volume for subtropical moist and wet forests of Puerto Rico. U.S. Department of Agriculture, Forest Service, Southern Research Station, Asheville, NC, USA. Available at: https://www.srs.fs.usda.gov/pubs/rp/rp_srs039.pdf [Accessed Dec 18, 2021]
https://www.srs.fs.usda.gov/pubs/rp/rp_s...
; Derroire et al., 2016Derroire, G.; Balvanera, P.; Castellanos-Castro, C.; Decocq, G.; Kennard, D.K.; Lebrija-Trejos, E.; Leiva, J.A.; Per-Christer, O.; Powers, J.S.; Power J.S.; Rico-Gray, V.; Tigabu, M.; Healey, J.R. 2016. Resilience of tropical dry forests: a meta-analysis of changes in species diversity and composition during secondary succession. Oikos 125: 1386-1397. https://doi.org/10.1111/oik.03229
https://doi.org/10.1111/oik.03229...
).

Figure 4
– Variables selected by the regression model to estimate wood volume in the Caatinga in Bahia. The description of the variables is given in Table 3. Bioclimatic variables: BIO 1 = Average annual temperature (°C); BIO 14 = Rainfall of the driest month (mm); Land Cover Consensus: VCM = Cultivated and Managed Vegetation; AM = Mixed Trees; ARB = Shrubs; Global Habitat Heterogeneity: COR = Correlation; SIM = Simpson’s index.

Volumetric stocks are also significantly influenced by the indices or occurrences of the area with cultivated and managed vegetation, mixed trees, and shrubs. For example, the negative values of the regression coefficients suggest that the volumetric stocks tend to decrease with each increase in the occurrence of these areas. This pattern is expected because land use characteristics generally correlate negatively with wood stocks in any natural forest ( Álvarez-Yépiz et al., 2008Álvarez-Yépiz, J.C.; Martínez-Yrízar, A.; Búrquez, A.; Lindquist, C. 2008. Variation in vegetation structure and soil properties related to land use history of old-growth and secondary tropical dry forests in northwestern Mexico. Forest Ecology and Management 256: 355-366. https://doi.org/10.1016/j.foreco.2008.04.049
https://doi.org/10.1016/j.foreco.2008.04...
; Burgos and Maass, 2004Burgos, A.; Maass, J.M. 2004. Vegetation change associated with land-use in tropical dry forest areas of Western Mexico. Agriculture, Ecosystems & Environment 104: 475-481. https://doi.org/10.1016/j.agee.2004.01.038
https://doi.org/10.1016/j.agee.2004.01.0...
; Colón and Lugo, 2006Colón, S.M.; Lugo, A.E. 2006. Recovery of a subtropical dry forest after abandonment of different land uses. Biotropica 38: 354-364. https://doi.org/10.1111/j.1744-7429.2006.00159.x
https://doi.org/10.1111/j.1744-7429.2006...
). Furthermore, changes in ecological patterns can alter the vegetation structure and consequently the volumetric distribution at different spatial scales ( Segura et al., 2002Segura, G.; Balvanera, P.; Durán, E.; Pérez, A. 2002. Tree community structure and stem mortality along a water availability gradient in a Mexican tropical dry forest. Plant Ecology 169: 259-271. https://doi.org/10.1023/A:1026029122077
https://doi.org/10.1023/A:1026029122077...
; Monteiro Junior et al., 2019). The removal of native vegetation for crops is a procedure that favors the advance of mechanized agriculture, especially in areas of dense vegetation and predominantly flat relief ( Silveira et al., 2019Silveira, E.M.O.; Reis, A.A.; Terra, M.C.N.S.; Withey, K.D.; Mello, J.M.; Acerbi-Júnior, F.W.; Ferraz Filho, A.C.; Mello, C.R. 2019. Spatial distribution of wood volume in Brazilian savannas. Anais da Academia Brasileira de Ciências 91: e20180666. https://doi.org/10.1590/0001-3765201920180666
https://doi.org/10.1590/0001-37652019201...
). These trends in land use, with the increase in cultivated areas and reduction of dense caatinga vegetation with greater volume, were also observed by Blackie et al. (2014)Blackie, R.; Baldauf, C.; Gautier, D.; Gumbo, D.; Kassa, H.; Parthasarathy, N.; et al. 2014. Tropical dry forests: The state of global knowledge and recommendations for future research. CIFOR. Available at: https://www.cifor.org/knowledge/publication/4408/ [Accessed Dec 13, 2021]
https://www.cifor.org/knowledge/publicat...
for the northeastern region in Brazil. On the other hand, the Simpson diversity index is the only positive predictor variable, suggesting that the greater the richness and diversity of species, the greater the volumetric stock found in the region, as expected for most mature, dry tropical forests (Banda-R et al., 2016; Powers et al., 2009Powers, J.S.; Becknell, J.M.; Irving, J.; Pèrez-Aviles, D. 2009. Diversity and structure of regenerating tropical dry forests in Costa Rica: Geographic patterns and environmental drivers. Forest Ecology and Management 258: 959-970. https://doi.org/10.1016/j.foreco.2008.10.036
https://doi.org/10.1016/j.foreco.2008.10...
).

The residual semivariogram analysis shows a nugget effect of 14.7, a sill effect of 117.6, and a distance of 14.3453 km. These parameters were adjusted using the distance of 44.3198 km and indicate the exponential model with greater accuracy with a reduced mean error of 0.004 m3 ha–1, overestimation of –24.75 m3 ha–1, and standard deviation of 2.24 m3 ha–1 ( Figure 5 ). The nugget effect value indicates that the semivariogram model explained the residual variation ( Dixon and Uddameri, 2016Dixon, B.; Uddameri, V. 2016. GIS and Geocomputation for Water Resource Science and Engineering. John Wiley, Hoboken, NJ, USA. ). The degree of spatial dependence (GD) of the study was 11 %, indicating a robust spatial dependence of the residues, according to Cambardella et al. (1994)Cambardella, C.A.; Moorman, T.B.; Novak, J.M.; Parkin, T.B.; Karlen, D.L.; Turco, R.F.; Konopka, A.E. 1994. Field-scale variability of soil properties in central Iowa soils. Soil Science Society of America Journal 58: 1501-1511. https://doi.org/10.2136/sssaj1994.03615995005800050033x
https://doi.org/10.2136/sssaj1994.036159...
, classified as strong (< 25 %), moderate (25 % to 75 %), poor (> 75 %).

Figure 5
– Experimental univariate semivariogram for the residual volumetric stock of (m3 ha–1) as a function of environmental variables of Caatinga vegetation in the state of Bahia, northeastern Brazil.

This result indicates that the spatial model explained approximately 46 % (1 – R2) of the wood volume variation. The semivariogram corresponds to an intrinsic characteristic of regionalization. The intrinsic hypothesis is a fundamental concept in the theory of regionalized variables ( Calder and Cressie, 2009Calder, C.A.; Cressie, N. 2009. Kriging and variogram models. In: Kitchin, R.; Thrift, N. International Encyclopedia of Human Geography, Elsevier, Oxford, UK. ), implying that the intrinsic function describes the spatial behavior of the regionalized variable within the space. Spatial variation is stationary if the variogram is the same in any sample ( Sen, 1989Sen, Z. 1989. Cumulative semivariogram models of regionalized variables. Mathematical Geology 21: 891-903. https://doi.org/10.1007/BF00894454
https://doi.org/10.1007/BF00894454...
). From a practical perspective, areas with an extensive range of variation in the estimated values tend to interfere directly with the performance of the semivariogram, as observed in this study. Another relevant point is that the shape and size of the sample configuration can affect the theoretical estimators and the description of the spatial dependence structure and the spatial estimates of unmeasured values ( Kestring et al., 2015Kestring, F.B.F.; Guedes, L.P.C.; Bastiani, F.; Uribe-Opazo, M.A. 2015. Thematic maps comparison of different sampling grids for soybean productivity. Engenharia Agrícola 35: 733-743 (in Portuguese, with abstract in English). https://doi.org/10.1590/1809-4430-Eng.Agric.v35n4p733-743/2015
https://doi.org/10.1590/1809-4430-Eng.Ag...
). Therefore, one of the main limitations of using geostatistical techniques is the number of samples needed to form the ideal sampling grid, which spatially represents the distribution of the variable under study ( Araújo et al., 2019Araújo, E.J.G.; Morais, V.A.; David, H.C.; Scolforo, J.R.S.; Mello, J.M.; Ebling, A.A. 2019. Spatialization of tree species diversity in the State of Minas Gerais. Floresta e Ambiente 26: e20150206. https://doi.org/10.1590/2179-8087.020615
https://doi.org/10.1590/2179-8087.020615...
).

The spatial distribution map of residuals ( Figure 6 ) shows a balanced distribution between under and overestimates, suggesting model adequacy and reliable estimates for observations in unsampled locations ( Silveira et al., 2019Silveira, E.M.O.; Reis, A.A.; Terra, M.C.N.S.; Withey, K.D.; Mello, J.M.; Acerbi-Júnior, F.W.; Ferraz Filho, A.C.; Mello, C.R. 2019. Spatial distribution of wood volume in Brazilian savannas. Anais da Academia Brasileira de Ciências 91: e20180666. https://doi.org/10.1590/0001-3765201920180666
https://doi.org/10.1590/0001-37652019201...
).

Figure 6
– Spatial distribution of residuals from volumetric stocks of Caatinga vegetation in the state of Bahia, northeastern Brazil.

These results are like those found by Vasques et al. (2016)Vasques, G.M.; Coelho, M.R.; Dart, R.O.; Oliveira, R.P.; Teixeira, W.G. 2016. Mapping soil carbon, particle-size fractions, and water retention in tropical dry forest in Brazil. Pesquisa Agropecuária Brasileira 51: 1371-1385. https://doi.org/10.1590/S0100-204X2016000900036
https://doi.org/10.1590/S0100-204X201600...
, Silveira et al. (2019)Silveira, E.M.O.; Reis, A.A.; Terra, M.C.N.S.; Withey, K.D.; Mello, J.M.; Acerbi-Júnior, F.W.; Ferraz Filho, A.C.; Mello, C.R. 2019. Spatial distribution of wood volume in Brazilian savannas. Anais da Academia Brasileira de Ciências 91: e20180666. https://doi.org/10.1590/0001-3765201920180666
https://doi.org/10.1590/0001-37652019201...
, and Li et al. (2020)Li, Y.; Li, M.; Liu, Z.; Li, C. 2020. Combining kriging interpolation to improve the accuracy of forest aboveground biomass estimation using remote sensing data. IEEE Access 8: 128124-128139. https://doi.org/10.1109/ACCESS.2020.3008686
https://doi.org/10.1109/ACCESS.2020.3008...
. The authors applied the regression kriging for soil carbon and water mapping and for volume and biomass estimation in tropical dry forests. They concluded that regression kriging is more expressive when residues showed symmetrical spatial distribution. In addition, the low values of the estimation errors obtained corroborate these results (Standardized Mean = 0.0257; Square Root of the Mean Standard Error = 0.87; Mean Standard Error = 11.5; and Square Root of the Mean Error = 9.58), considered acceptable for geostatistical kriging estimates ( Benítez et al., 2016Benítez, F.L.; Anderson, L.O.; Formaggio, A.R. 2016. Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data. Acta Amazonica 46: 151-160. https://doi.org/10.1590/1809-4392201501254
https://doi.org/10.1590/1809-43922015012...
). Both underestimated (negative) and overestimated (positive) values demonstrate not only the excellent performance of the regression model, but also the standard kriging map of wood volume stock residues ( Silveira et al., 2019Silveira, E.M.O.; Reis, A.A.; Terra, M.C.N.S.; Withey, K.D.; Mello, J.M.; Acerbi-Júnior, F.W.; Ferraz Filho, A.C.; Mello, C.R. 2019. Spatial distribution of wood volume in Brazilian savannas. Anais da Academia Brasileira de Ciências 91: e20180666. https://doi.org/10.1590/0001-3765201920180666
https://doi.org/10.1590/0001-37652019201...
).

The global predictive map generated by the regression model ( Figure 7 ) and the map corrected by the regression kriging technique ( Figure 8 ) show the same pattern in the distribution of the volumetric stock, with estimates ranging from 0.01 m3 ha–1 to ~ 110 m3 ha–1, showing a similar variation with the data observed at the plot level. The mesoregion corresponding to the Vale dos São Francisco presented the lowest volumetric stocks, as indicated in the map. In this location, municipalities were sampled, namely Sento Sé, Xique-Xique, Gentio do Ouro, and Campo Alegre de Lourdes. The northeastern and central-northern parts of Bahia also revealed lower wood stocks for most areas that comprise these regions. Places with the highest concentrations of wood volume are in the central-northern region and part of the central-southern areas of the state.

Figure 7
– Global model of wood volume (m3 ha–1) for Caatinga vegetation in Bahia State, northeastern Brazil.

Figure 8
– Wood volume map (m3 ha–1) obtained by the regression kriging for Caatinga vegetation in the state of Bahia, northeastern Brazil.

The decrease in wood volume from the central to the northern part of the state can be explained mainly by precipitation, temperature, and karst relief associated to soils with limestone in the valleys and slopes (IBGE, 2019). Low water availability in the soil in these areas associated to the presence of rocky outcrops with the occurrence of slabs in slightly flat areas also represents a limiting factor for the development of biometric characteristics of plants, resulting in low values of volume wood biomass ( Wagner et al., 2014Wagner, F.; Rossi, V.; Aubry-Kientz, M.; Bonal, D.; Dalitz, H.; Gliniars, R.; Stahl, C.; Trabucco, A.; Hérault, B. 2014. Pan-tropical analysis of climate effects on seasonal tree growth. PLOS One 9: e92337. https://doi.org/10.1371/journal.pone.0092337
https://doi.org/10.1371/journal.pone.009...
, 2016; Zappi et al., 2015Zappi, D.C.; Filardi, F.L.R.; Leitman, P.; Souza, V.C.; Walter, B.M.T.; Pirani, J.R.; et al. 2015. Growing knowledge: an overview of seed plant diversity in Brazil. Rodriguésia 66: 1085-1113. https://doi.org/10.1590/2175-7860201566411
https://doi.org/10.1590/2175-78602015664...
). This region is also characterized by a set of mountains that reach high altitudes and rough relief, resulting from a high erosion rate and occupation of the areas by rocky outcrops, limiting vegetation development(IBGE, 2019).

The central-southern portion of the state holds the remnants with the most significant volumes of wood, ranging from 55 to 108.2 m3 ha–1. These regions have greater water availability (IBGE, 2019), providing better conditions for plant growth ( Chaturvedi et al., 2013Chaturvedi, R.K.; Raghubanshi, A.S.; Singh, J.S. 2013. Growth of tree seedlings in a tropical dry forest in relation to soil moisture and leaf traits. Journal of Plant Ecology 6: 158-170. https://doi.org/10.1093/jpe/rts025
https://doi.org/10.1093/jpe/rts025...
; Rozendaal et al., 2020Rozendaal, D.M.A.; Phillips, O.L.; Lewis, S.L.; Affum-Baffoe, K.; Alvarez Davila, E.; Andrade, A.; et al. 2020. Competition influences tree growth, but not mortality, across environmental gradients in Amazonia and tropical Africa. Ecology 101: e03052. https://doi.org/10.1002/ecy.3052
https://doi.org/10.1002/ecy.3052...
; Sanaei et al., 2018Sanaei, A.; Ali, A.; Chahouki, M.A.Z. 2018. The positive relationships between plant coverage, species richness, and aboveground biomass are ubiquitous across plant growth forms in semi-steppe rangelands. Journal of Environmental Management 205: 308-318. https://doi.org/10.1016/j.jenvman.2017.09.079
https://doi.org/10.1016/j.jenvman.2017.0...
). However, small patches of low volumetric stock are visible in the central portion of this region (less than 20 m3 ha–1). Three factors can explain this result. First, it may reflect areas with anthropic disturbances, such as exploitation of vegetation for charcoal and firewood production, crops, and livestock, which are in an advanced degradation stage, leading to a lower wood volume in this region. Second, the low volume concentration in these areas may be due to climatic effects related to a geographic barrier, creating an unfavorable situation for plant growth. An extensive mountain range constitutes this geographic barrier range with high altitudes, which produces an orographic effect on the rainfall regime in specific locations in the central portion of the state Bahia (Silveira et al ., 2019). In addition, cold fronts that come from the southern region of Brazil reach part of this region, decreasing humidity in the winter months (IBGE, 2019), therefore directly influencing the biomechanical development of plants ( Lines et al., 2012Lines, E.R.; Zavala, M.A.; Purves, D.W.; Coomes, D.A. 2012. Predictable changes in aboveground allometry of trees along gradients of temperature, aridity and competition. Global Ecology and Biogeography 21: 1017-1028. https://doi.org/10.1111/j.1466-8238.2011.00746.x
https://doi.org/10.1111/j.1466-8238.2011...
) and leading to the death of some trees in periods of drought ( Aguirre-Gutiérrez et al., 2019Aguirre-Gutiérrez, J.; Oliveras, I.; Rifai, S.; Fauset, S.; Adu-Bredu, S.; Affum-Baffoe, K.; et al. 2019. Drier tropical forests are susceptible to functional changes in response to a long-term drought. Ecology Letters 22: 855-865. https://doi.org/10.1111/ele.13243
https://doi.org/10.1111/ele.13243...
; Krishnadas et al., 2021Krishnadas, M.; Sankaran, M.; Page, N.; Joshi, J.; Machado, S.; Nataraj, N.; Chengappa, S.K.; Kumar, V.; Kumar, A.; Krishnamani, R. 2021. Seasonal drought regulates species distributions and assembly of tree communities across a tropical wet forest region. Global Ecology and Biogeography 30: 1847-1862. https://doi.org/10.1111/geb.13350
https://doi.org/10.1111/geb.13350...
). Third, these sites tend to have sandier soils, such as Cambisol and Litholic Neosol ( Santos, 2018Santos, H.G. 2018. Brazilian system of soil classification = Sistema brasileiro de classificação de solos. 5ed. Embrapa, Brasília, DF, Brazil (in Portuguese). ; IBGE, 2019). These soils generally have low fertility, which creates unfavorable conditions for plant growth, along with the physical characteristics, low precipitation, and high temperatures ( Cao and Sanchez-Azofeifa, 2017Cao, S.; Sanchez-Azofeifa, A. 2017. Modeling seasonal surface temperature variations in secondary tropical dry forests. International Journal of Applied Earth Observation and Geoinformation 62: 122-134. https://doi.org/10.1016/j.jag.2017.06.008
https://doi.org/10.1016/j.jag.2017.06.00...
; Silveira et al., 2019Silveira, E.M.O.; Reis, A.A.; Terra, M.C.N.S.; Withey, K.D.; Mello, J.M.; Acerbi-Júnior, F.W.; Ferraz Filho, A.C.; Mello, C.R. 2019. Spatial distribution of wood volume in Brazilian savannas. Anais da Academia Brasileira de Ciências 91: e20180666. https://doi.org/10.1590/0001-3765201920180666
https://doi.org/10.1590/0001-37652019201...
).

These interconnected factors regulate the availability and volumetric stock in the caatinga and are hypotheses that deserve further investigation. Maps that predict thoroughly wood volume are needed, as they can simplify research into priority areas for sustainable timber production, conservation unit creation, and biomass and carbon mapping. These advances are urgently needed in light of increasing deforestation rates and different forms of land use that potentially threaten the Caatinga biome.

Conclusions

Using geostatistics is a promising tool to generate predictive maps of volumetric wood stocks. This information is necessary to develop action plans for sustainable management and use of the Caatinga seasonally dry forests in Bahia. The spatial distribution of volume stocks is partly controlled by temperature, precipitation, relief, and vegetation heterogeneity. The regression model suggests an excellent potential to estimate volumetric stock from environmental variables to predict wood volume where they are not measured. Additional studies with a larger sample population and using other variables can improve the model for the Caatinga dry vegetation.

Acknowledgments

We thank the ForestPlots.net, Inema and caatinga forest management network teams for permission and availability of data to conduct the research. This project was supported by ForestPlots.net approved Research Project #142. “Mapping the biomass and carbon stock, volume and diversity in tropical forests”, and the U.K. NERC Newton Fund project “Nordeste” (NE/N012550/1). The authors thank the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for financial support in a research grant for the first and third authors of this work.

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Edited by

Edited by: Antonio Gama-Rodrigues

Publication Dates

  • Publication in this collection
    07 Apr 2023
  • Date of issue
    2023

History

  • Received
    10 Aug 2022
  • Accepted
    07 Oct 2022
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