BIODIVERSIDADE ACREANA: Predicting geographical distribution models of high-value timber trees in the Amazon Basin using remotely sensed data

Cássia Da Conceição Prates-Clark^a^,^b^,^,, Sassan S. Saatchi^b^,^c and Donat Agosti^d

^aSchool of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK
^bCenter for Tropical Research, University of California, Los Angeles, CA 90095, USA
^cJet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
^dNaturmuseum der Burgergemeinde Bern, 3005 Bern, Switzerland
Received 13 May 2006; revised 17 September 2007; accepted 21 September 2007. Available online 26 November 2007.

Abstract

Species distribution models were developed for three high economic value timber trees (Calophyllum brasiliensis, Carapa guianensis and Virola surinamensis) that are heavily harvested in the Amazon Basin. A combination of habitat measurements extracted from remote sensing data (MODIS, QSCAT and SRTM) and bioclimatic surfaces was examined to ascertain the most influential factors determining the occurrence of these tree species. The prediction of species’ occurrence rates was tested separately for each species distribution model and the results were examined for their ability to accurately map the spatial distribution of these tree species. By evaluating the omission and commission rates we concluded that species distribution models based on remote sensing data contributed significantly in quantifying environmental properties used to summarize the ecological niche of each tree species. Specific vegetation characteristics (such as percentage of tree cover, vegetation moisture and roughness, annual NDVI and mean LAI during the dry LAI) showed the dependence of these species’ occurrence in more densely vegetated forests. Areas with high leaf area (even during the dry months) and areas with high vegetation moisture were predicted as potential species habitat for C. brasiliensis. The density vegetation during the dry season and vegetation phenology were strongly correlated with climate differences, such as variations in air temperature and precipitation seasonality for V. surinamensis. Lower elevation areas with more exuberant vegetation and a high greenness index were among the most important factors accounting for the geographical distribution of C. guianensis. Species distribution models are increasingly important in many fields of research and conservation. The potential of remotely sensed data to monitor environmental changes in tropical areas, along with the understanding of ecosystem function, are both critical for conservation of biodiversity and the long-term process of sustaining ecosystems.

Keywords:

Amazon Basin

; Climate variables; Ecological niche; Remote sensing

data

; Species

distribution model; Timber trees

Article Outline

1. Introduction
2. Methods
3. Data analysis
4. Results
5. Discussion
6. Conclusion
Acknowledgements
Appendix A. Supplementary data
References

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Fig. 1. Data points of species’ occurrence for Calophyllum brasiliensis, Carapa guianensis and Virola surinamensis.

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Fig. 2. The predictive potential geographical maps for Calophyllum brasiliensis based on: (a) ‘Climate-model’, (b) ‘RS-model’ and (c) ‘RS and Climate’.

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Fig. 3. The predictive potential geographical maps for Carapa guianensis based on: (a) ‘Climate-model’, (b) ‘RS-model’ and (c) ‘RS and Climate’.

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Fig. 4. The predictive potential geographical maps for Virola surinamensis based on: (a) ‘Climate-model’, (b) ‘RS-model’ and (c) ‘RS and Climate’.

Table 1.

List of remote sensed data and products used in this study

Data record	Instrument	Vegetation/landscape parameter	RS metrics at 5 km resolution
Monthly NDVI (2000–2001)	MODIS	Vegetation type and seasonality	NDVI-1: maximum NDVI
			NDVI-2: annual mean NDVI
			NDVI-3: mean NDVI wet months
			NDVI-4: mean NDVI dry months

Monthly (2000–2001) leaf area index (LAI)	MODIS	Vegetation type, seasonality, productivity	LAI-1: maximum LAI
			LAI-2: annual mean LAI
			LAI-3: mean LAI wet months
			LAI-4: mean LAI dry months

Percent tree cover (2000–2001)	MODIS	Forest cover and heterogeneity	VCF: continuous field product

Scatterometer backscatter monthly composites at 1 km (1999–2004)	QuickSCAT	Vegetation moisture, leaf/wood density	QSCAT-H: mean backscatter HH
			QSCAT-V: mean backscatter VV
			QSCAT-SH: std. backscatter HH
			QSCAT-SV: std. backscatter VV

Digital elevation (100 m resolution) 2000	SRTM	Surface elevation	SRTM-HGT: mean elevation
			SRTM-STD: ruggedness factor

Table 2.

The potential predictor variables used to generate the RS and Climate distribution models

RS	Climate
Calophyllum brasiliensis
LAI of driest quarter and its seasonality (LAI-4 and LAI-2)	Annual precipitation
Vegetation moisture and roughness (QSCAT-H)	Precipitation of the wettest month

Percent of tree cover	Precipitation of the wettest quarter
	Precipitation of coldest quarter
	Temperature seasonality

Carapa guianensis
Elevation (SRTM)	Temperature annual range
Vegetation moisture and roughness (QSCAT-H)	Mean diurnal range
Maximum NDVI	Minimum temperature of coldest month
LAI of driest quarter (LAI-4)	Temperature seasonality

Virola surinamensis
Elevation (SRTM)	Mean diurnal range
LAI of driest quarter (LAI-4)	Temperature seasonality

Canopy moisture and roughness (QSCAT-H, -V and -SV)	Annual precipitation
	Precipitation of the wettest month

Table 3.

Comparisons of ‘RS’, ‘Climate’ and ‘RS and Climate’ models performances for Calophyllum brasiliensis, Carapa guianensis and Virola surinamensis

	Balance threshold ^a	Fraction of predicted area	AUC
Calophyllum brasiliensis
RS	1.501	0.888	0.728
Climate	2.007	0.858	0.751
RS and Climate	1.537	0.575	0.877

Carapa brasiliensis
RS	0.500	0.678	0.880
Climate	4.504	0.440	0.898
RS and Climate	1.502	0.625	0.825

Virola surinamensis
RS	2.001	0.624	0.855
Climate	1.250	0.625	0.852
RS and Climate	1.250	0.575	0.877

^a Balance threshold was based on training omission rate and fraction of predicted area.Table 4.

Species’ occurrence probabilities (%) on areas where the species have been observed

	RS	Climate	RS and Climate
Calophyllum brasiliensis
Average	56.05	55.78	55.40
>30	74.68	76.83	83.54
>40	69.62	67.07	68.35
>50	60.76	57.32	55.70
>75	35.44	26.83	32.91

Carapa guianensis
Average	55.56	55.44	52.72
>30	75.68	82.43	77.03
>40	67.57	70.27	64.86
>50	60.81	55.41	56.76
>75	25.68	21.62	21.62

Virola surinamensis
Average	54.03	54.78	53.06
>30	74.38	71.90	76.03
>40	68.60	66.12	66.94
>50	60.33	56.20	58.68
>75	27.27	25.62	24.80

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Corresponding author at: School of Geographical Sciences, University of Bristol, University Road, Bristol, BS8 1SS, UK.