Sensitivity of subtropical forest and savanna productivity to climate variability in South America, Uruguay

Abstract

Questions Increasing climate variability has major effects on forest productivity, as well as transitions between forest and savanna ecosystems. While drought‐induced declines in tropical forest productivity and forest loss is a global concern, forest expansion in subtropical South America predicted by climate models has received little attention. In the forest–grassland transition zone encompassing Uruguay, we ask: (1) how does climate variability affect woodland productivity and at what time scales; and (2) how do different woodland types (riparian, hillside and wooded savanna) differ in their sensitivity to climate variability? Location Forests and wooded savanna in Uruguay, subtropical southeast South America. Methods Primary productivity was measured as the fraction of photosynthetically active radiation (fPAR) based on monthly normalized difference vegetation index (NDVI) from multitemporal (1998–2012) SPOT imagery at a 1 km × 1 km resolution, covering 19% of Uruguayan woodlands. The effects of accumulated rainfall and mean temperature on the productivity of riparian and hillside forests and wooded savanna were evaluated using correlations and time series analysis over multiple time windows ranging from 1–24 mo. Results Inter‐annual rainfall variability and seasonal temperatures affected forest productivity even though average conditions were largely non‐limiting. Periods of exceptionally high rainfall and warm winters had positive effects on forest productivity, but hot summers had a negative effect on all woodland productivity. Riparian and hillside forest productivity was equally susceptible to drought, showing similar relationships to accumulated rainfall over multiple time periods, as well as similar susceptibility to high summer temperatures. Wooded savanna – a composite cover of trees and a grass/forb understorey – productivity was linked to seasonal rainfall over 3–6 mo, and in general showed the lowest correlation with inter‐annual variability in rainfall. Conclusions Results suggest functional convergence in the response of riparian and hillside forests to water deficit and hot summers. The increases in forest productivity with increasing rainfall and declining drought risk – as predicted by the IPCC for this region – are a potential mechanism for increased growth and future expansion of forests in this biogeographic transition zone.

Spatial Distribution and Tree Cover of Hillside and Ravine Forests in Uruguay: The Challenges of Mapping Patchy Ecosystems

“…: see Figure 1). These pixels have NDVI values similar to those of pasture and livestock production systems (28) (43) .…”

“…Among the most commonly used indicators for monitoring primary productivity, the Normalized Difference Vegetation Index (NDVI) is derived from red and near infrared bands from satellite imagery (22)(23) (24) . The utility of the NDVI as an indicator of primary productivity is limited in forests with high canopy density due to saturation (25) , but it is widely used for the monitoring of forests, savannas and grasslands in southern South America and African savannas (26) (27) (28) .…”

“…To select the NDVI data corresponding to HRF, an intersection of the national-scale NDVI pixel layer from SPOT imagery was made with the HRF-2015 layer. We obtained a total of 439 HRF pixels; those pixels with >90 % of HRF cover were defined as «pure pixels» (28) . This monthly NDVI time series reflects the behavior of NDVI in areas that are dominated by native forest and have minimal influence from other land cover classes such as tree plantations, bare soil or grasslands.…”

“…Finally, to evaluate the relationship between tree canopy cover and NDVI, each of the 439 pixels were assigned to one of the following canopy cover categories: 0-24 %, 25-49 %, 50-74 % and 75-100 % (Figure 1) (28) . Tree cover class was estimated visually for each pixel, using the tool «create network» to create a grid with cells of 250 x 250 m, which was superimposed with the hillside forest pixels over high resolution ESRI built-in Basemap Layer «World Imagery» in ArcMap 10.01 at a scale of 1:20,000.…”

“…Tree cover class was estimated visually for each pixel, using the tool «create network» to create a grid with cells of 250 x 250 m, which was superimposed with the hillside forest pixels over high resolution ESRI built-in Basemap Layer «World Imagery» in ArcMap 10.01 at a scale of 1:20,000. As this Basemap Layer includes high-resolution imagery GeoEye IKONOS (1m resolution) for most of Uruguay, individual trees were easily visible to visually classify tree cover (28) . To compare the differences in NDVI between the four tree coverage categories, generalized linear models and the Tukey HSD post-test were used.…”