Modeling forest dynamics along climate gradients in Bolivia

Abstract

Dynamic vegetation models have been used to assess the resilience of tropical forests to climate change, but the global application of these modeling experiments often misrepresents carbon dynamics at a regional level, limiting the validity of future projections. Here a dynamic vegetation model (Lund Potsdam Jena General Ecosystem Simulator) was adapted to simulate present-day potential vegetation as a baseline for climate change impact assessments in the evergreen and deciduous forests of Bolivia. Results were compared to biomass measurements (819 plots) and remote sensing data. Using regional parameter values for allometric relations, specific leaf area, wood density, and disturbance interval, a realistic transition from the evergreen Amazon to the deciduous dry forest was simulated. This transition coincided with threshold values for precipitation (1400 mm yr −1 ) and water deficit (i.e., potential evapotranspiration minus precipitation) (−830 mm yr −1 ), beyond which leaf abscission became a competitive advantage. Significant correlations were found between modeled and observed values of seasonal leaf abscission (R 2 = 0.6, p <0.001) and vegetation carbon (R 2 = 0.31, p <0.01). Modeled Gross Primary Productivity (GPP) and remotely sensed normalized difference vegetation index showed that dry forests were more sensitive to rainfall anomalies than wet forests. GPP was positively correlated to the El Niño-Southern Oscillation index in the Amazon and negatively correlated to consecutive dry days. Decreasing rainfall trends were simulated to reduce GPP in the Amazon. The current model setup provides a baseline for assessing the potential impacts of climate change in the transition zone from wet to dry tropical forests in Bolivia.

Loss carbon by recent deforestation (2010-2013) in the lowlands and Yungas of Bolivia

“…62 millones de tC/año -es plausible esperar una disminución cercana al 17% del carbono remanente. Las implicaciones de esta pérdida de cobertura vegetal en la capacidad de los bosques remanentes para mitigar las variaciones de temperatura y precipitación esperadas por impacto del cambio climático (ver Seiler et al 2014Seiler et al , 2015 son desconocidas. Un siguiente paso será también estimar qué representó la deforestación ocurrida entre 2010 y 2013 en términos de emisiones netas de CO 2 (e.g.…”

Comparing correlative and process-based modelling approaches in a boreal forest identifies important areas for model development

“…Smith et al (2014), for Europe by Hickler et al (2012)). Generally, an application on a smaller scale requires accounting for study region-specific processes and the ecology of the main tree species (Hickler et al (2004) and Tang et al (2012) for northeastern U.S.; Hickler et al (2012) for Europe; Seiler et al (2014) for Bolivia). Zhang et al (2013) applied LPJ-GUESS to the whole Arctic at an accordingly coarse resolution and reported a good match between observed and predicted treelines, albeit of plant functional types rather than species.…”

“…LPJ-GUESS has been successfully applied on global (Parazoo et al 2014;Steinkamp and Hickler 2015;Knorr et al 2016;Smith et al 2016), European (Sallaba et al 2015;Wu et al 2015;Blanke et al 2016), regional (Manusch et al 2014;Seiler et al 2014;Guoping Tang et al 2014), and even local scale (De Kauwe et al 2014;Ekici et al 2015;Vermeulen et al 2015). Applications include carbon budget quantification (Morales et al 2007;Le Quéré et al 2013;Engström et al 2016) as well as predictions of vegetation distribution Zhang et al 2013;Baudena et al 2015) and agricultural production Olin et al 2015;Pirttioja et al 2015).…”

Effect of changing vegetation on denudation (part 1): Predicted vegetation composition and cover over the last 21 thousand years along the Coastal Cordillera of Chile

“…For regional applications, DGVMs should be tested against present-day data in the study region and process representations should be adapted to specific conditions (Hickler et al, 2012, Seiler et al, 2014. Climate data for simulations of paleovegetation often originates from Global Climate Models (GCMs), which have a rather coarse grid cell resolution.…”