Insights on biodiversity drivers to predict species richness in tropical forests at the local scale

Disentangling the relative importance of different biodiversity drivers (i.e., climate, edaphic, historical factors, or human impact) to predict plant species richness at the local scale is one of the most important challenges in ecology. Biodiversity modelling is a key tool for the integration of these drivers and the predictions generated are essential, for example, for climate change forecast and conservation planning. However, the reliability of biodiversity models at the local scale remains poorly understood, especially in tropical species-rich areas, where they are required. We inventoried all woody plants with stems ≥ 2.5 cm in 397 plots across the Andes-Amazon gradient. We generated and mapped 19 uncorrelated biodiversity drivers at 90 m resolution, grouped into four categories: microclimatic, microtopographic, anthropic, and edaphic. In order to evaluate the importance of the different categories, we grouped biodiversity drivers into four different clusters by categories. For each of the four clusters of biodiversity drivers, we modelled the observed species richness using two statistical techniques (random forest and Bayesian inference) and two modelling procedures (including or excluding a spatial component). All the biodiversity models produced were evaluated by cross-validation. Species richness was accurately predicted by random forest (Spearman correlation up to 0.85 and explained variance up to 67%). The results suggest that precipitation and temperature are important driving forces of species richness in the region. Nonetheless, a spatial component should be considered to properly predict biodiversity. This could reflect macroevolutionary underlying forces not considered here, such as colonization time, dispersal capacities, or speciation rates. However, the proposed biodiversity modelling approach can predict accurately species richness at the local scale and detailed resolution (90 m) in tropical areas, something that previous works had found extremely challenging. The innovative methodology presented here could be employed in other areas with conservation needsWe thank the Consejería de Educacion (Comunidad de Madrid, Spain), National Geographic Society (8047-06, 7754-04), National Science Foundation (DEB#0101775, DEB#0743457, DEB#1557094), Spanish Ministry of Economy and Competitiveness (CGL2016-75414-P), Centro de Estudios de América Latina (Universidad Autonoma de Madrid – Banco Santander), Consejería de Educacion, Cultura y Deportes (Junta de Comunidades de Castilla-La Mancha, SBPLY/21/180501/000241), Spanish Ministry of Economy and Competitiveness (PID2019-106341GB-I00) for funding our research. The full dataset can be requested from the Madidi Project (https://madidiproject.weebly.com/

Elevational gradients in β-diversity reflect variation in the strength of local community assembly mechanisms across spatial scales

Despite long-standing interest in elevational-diversity gradients, little is known about the processes that cause changes in the compositional variation of communities (β-diversity) across elevations. Recent studies have suggested that β-diversity gradients are driven by variation in species pools, rather than by variation in the strength of local community assembly mechanisms such as dispersal limitation, environmental filtering, or local biotic interactions. However, tests of this hypothesis have been limited to very small spatial scales that limit inferences about how the relative importance of assembly mechanisms may change across spatial scales. Here, we test the hypothesis that scale-dependent community assembly mechanisms shape biogeographic β-diversity gradients using one of the most well-characterized elevational gradients of tropical plant diversity. Using an extensive dataset on woody plant distributions along a 4,000-m elevational gradient in the Bolivian Andes, we compared observed patterns of β-diversity to null-model expectations. β-deviations (standardized differences from null values) were used to measure the relative effects of local community assembly mechanisms after removing sampling effects caused by variation in species pools. To test for scale-dependency, we compared elevational gradients at two contrasting spatial scales that differed in the size of local assemblages and regions by at least an order of magnitude. Elevational gradients in β-diversity persisted after accounting for regional variation in species pools. Moreover, the elevational gradient in β-deviations changed with spatial scale. At small scales, local assembly mechanisms were detectable, but variation in species pools accounted for most of the elevational gradient in β-diversity. At large spatial scales, in contrast, local assembly mechanisms were a dominant force driving changes in β-diversity. In contrast to the hypothesis that variation in species pools alone drives β-diversity gradients, we show that local community assembly mechanisms contribute strongly to systematic changes in β-diversity across elevations.We conclude that scale-dependent variation in community assembly mechanisms underlies these iconic gradients in global biodiversityThe Madidi Project has been funded by the National Science Foundation (DEB-0101775 and DEB-0743457), the Comunidad de Madrid, the National Geographic Society (NGS 7754-04 and NGS 8047-06), the Taylor Fund for Ecological Research, the Andrew W. Mellon Foundation, the Centro de Estudios de América Latina, Universidad Autónoma de Madrid, and Christopher Davidson and Sharon Christoph. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

Mature Andean forests as globally important carbon sinks and future carbon refuges

It is largely unknown how South America’s Andean forests affect the global carbon cycle, and thus regulate climate change. Here, we measure aboveground carbon dynamics over the past two decades in 119 monitoring plots spanning a range of >3000 m elevation across the subtropical and tropical Andes. Our results show that Andean forests act as strong sinks for aboveground carbon (0.67 ± 0.08 Mg C ha−1 y−1) and have a high potential to serve as future carbon refuges. Aboveground carbon dynamics of Andean forests are driven by abiotic and biotic factors, such as climate and size-dependent mortality of trees. The increasing aboveground carbon stocks offset the estimated C emissions due to deforestation between 2003 and 2014, resulting in a net total uptake of 0.027 Pg C y−1. Reducing deforestation will increase Andean aboveground carbon stocks, facilitate upward species migrations, and allow for recovery of biomass losses due to climate change.Fil: Duque, Alvaro. Universidad Nacional de Colombia; ColombiaFil: Peña, Miguel A.. Universidad Nacional de Colombia; ColombiaFil: Cuesta, Francisco. Universidad de Las Américas; EcuadorFil: González Caro, Sebastián. Universidad Nacional de Colombia; ColombiaFil: Kennedy, Peter. University of Minnesota; Estados UnidosFil: Phillips, Oliver L.. University of Leeds; Reino UnidoFil: Calderón Loor, Marco. Universidad de Las Américas; EcuadorFil: Blundo, Cecilia Mabel. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Carilla, Julieta. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Cayola, Leslie. Missouri Botanical Garden; Estados UnidosFil: Farfán Ríos, William. Washington University in St. Louis; Estados UnidosFil: Fuentes, Alfredo. Missouri Botanical Garden; Estados UnidosFil: Grau, Hector Ricardo. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Homeier, Jürgen. Universität Göttingen; AlemaniaFil: Loza-Rivera, María I.. Missouri Botanical Garden; Estados UnidosFil: Malhi, Yadvinder. University of Oxford; Reino UnidoFil: Malizia, Agustina. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Malizia, Lucio Ricardo. Universidad Nacional de Jujuy; ArgentinaFil: Martínez Villa, Johanna A.. Université du Québec a Montreal; CanadáFil: Myers, Jonathan A.. Washington University in St. Louis; Estados UnidosFil: Osinaga Acosta, Oriana. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Peralvo, Manuel. No especifíca;Fil: Pinto, Esteban. No especifíca;Fil: Saatchi, Sassan. Jet Propulsion Laboratory; Estados UnidosFil: Silman, Miles. Center For Energy, Environment And Sustainability; Estados UnidosFil: Tello, J. Sebastián. Missouri Botanical Garden; Estados UnidosFil: Terán Valdez, Andrea. No especifíca;Fil: Feeley, Kenneth J.. University of Miami; Estados Unido

Mature Andean forests as globally important carbon sinks and future carbon refuges

It is largely unknown how South America’s Andean forests affect the global carbon cycle, and thus regulate climate change. Here, we measure aboveground carbon dynamics over the past two decades in 119 monitoring plots spanning a range of >3000 m elevation across the subtropical and tropical Andes. Our results show that Andean forests act as strong sinks for aboveground carbon (0.67 ± 0.08 Mg C ha−1 y−1) and have a high potential to serve as future carbon refuges. Aboveground carbon dynamics of Andean forests are driven by abiotic and biotic factors, such as climate and size-dependent mortality of trees. The increasing aboveground carbon stocks offset the estimated C emissions due to deforestation between 2003 and 2014, resulting in a net total uptake of 0.027 Pg C y−1. Reducing deforestation will increase Andean aboveground carbon stocks, facilitate upward species migrations, and allow for recovery of biomass losses due to climate change.Fil: Duque, Alvaro. Universidad Nacional de Colombia; ColombiaFil: Peña, Miguel A.. Universidad Nacional de Colombia; ColombiaFil: Cuesta, Francisco. Universidad de Las Américas; EcuadorFil: González Caro, Sebastián. Universidad Nacional de Colombia; ColombiaFil: Kennedy, Peter. University of Minnesota; Estados UnidosFil: Phillips, Oliver L.. University of Leeds; Reino UnidoFil: Calderón Loor, Marco. Universidad de Las Américas; EcuadorFil: Blundo, Cecilia Mabel. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Carilla, Julieta. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Cayola, Leslie. Missouri Botanical Garden; Estados UnidosFil: Farfán Ríos, William. Washington University in St. Louis; Estados UnidosFil: Fuentes, Alfredo. Missouri Botanical Garden; Estados UnidosFil: Grau, Hector Ricardo. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Homeier, Jürgen. Universität Göttingen; AlemaniaFil: Loza-Rivera, María I.. Missouri Botanical Garden; Estados UnidosFil: Malhi, Yadvinder. University of Oxford; Reino UnidoFil: Malizia, Agustina. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Malizia, Lucio Ricardo. Universidad Nacional de Jujuy; ArgentinaFil: Martínez Villa, Johanna A.. Université du Québec a Montreal; CanadáFil: Myers, Jonathan A.. Washington University in St. Louis; Estados UnidosFil: Osinaga Acosta, Oriana. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Peralvo, Manuel. No especifíca;Fil: Pinto, Esteban. No especifíca;Fil: Saatchi, Sassan. Jet Propulsion Laboratory; Estados UnidosFil: Silman, Miles. Center For Energy, Environment And Sustainability; Estados UnidosFil: Tello, J. Sebastián. Missouri Botanical Garden; Estados UnidosFil: Terán Valdez, Andrea. No especifíca;Fil: Feeley, Kenneth J.. University of Miami; Estados Unido

Elevation and latitude drives structure and tree species composition in Andean forests: Results from a large-scale plot network

Our knowledge about the structure and function of Andean forests at regional scales remains limited. Current initiatives to study forests over continental or global scales still have important geographical gaps, particularly in regions such as the tropical and subtropical Andes. In this study, we assessed patterns of structure and tree species diversity along ~ 4000 km of latitude and ~ 4000 m of elevation range in Andean forests. We used the Andean Forest Network (Red de Bosques Andinos, https://redbosques.condesan.org/) database which, at present, includes 491 forest plots (totaling 156.3 ha, ranging from 0.01 to 6 ha) representing a total of 86,964 identified tree stems ≥ 10 cm diameter at breast height belonging to 2341 identified species, 584 genera and 133 botanical families. Tree stem density and basal area increases with elevation while species richness decreases. Stem density and species richness both decrease with latitude. Subtropical forests have distinct tree species composition compared to those in the tropical region. In addition, floristic similarity of subtropical plots is between 13 to 16% while similarity between tropical forest plots is between 3% to 9%. Overall, plots ~ 0.5-ha or larger may be preferred for describing patterns at regional scales in order to avoid plot size effects. We highlight the need to promote collaboration and capacity building among researchers in the Andean region (i.e., South-South cooperation) in order to generate and synthesize information at regional scale.Fil: Malizia, Agustina. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Blundo, Cecilia Mabel. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Carilla, Julieta. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Osinaga Acosta, Oriana. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Cuesta, Francisco. Universidad de Las Américas; Ecuador. Consorcio para el Desarrollo Sostenible de la Ecorregión Andina; EcuadorFil: Duque, Alvaro. Universidad Nacional de Colombia. Sede Medellín; ColombiaFil: Aguirre, Nikolay. Universidad Nacional de Loja. Centro de Investigaciones Tropicales del Ambiente y la Biodiversidad; EcuadorFil: Aguirre, Zhofre. Universidad Nacional de Loja. Centro de Investigaciones Tropicales del Ambiente y la Biodiversidad; EcuadorFil: Ataroff, Michele. Universidad de Los Andes; VenezuelaFil: Baez, Selene. Escuela Politécnica Nacional; EcuadorFil: Calderón Loor, Marco. Universidad de Las Américas; Ecuador. Deakin University; AustraliaFil: Cayola, Leslie. Herbario Nacional de Bolivia; Bolivia. Missouri Botanical Garden; Estados UnidosFil: Cayuela, Luis. Universidad Rey Juan Carlos; EspañaFil: Ceballos, Sergio Javier. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Cedillo, Hugo. Universidad de Cuenca; EcuadorFil: Farfán Ríos, William. Universidad Nacional de San Antonio Abad del Cusco. Herbario Vargas; PerúFil: Feeley, Kenneth J.. University of Miami; Estados UnidosFil: Fuentes, Alfredo Fernando. Herbario Nacional de Bolivia; Bolivia. Missouri Botanical Garden; Estados UnidosFil: Gámez Álvarez, Luis E.. Universidad de Los Andes; VenezuelaFil: Grau, Hector Ricardo. Universidad Nacional de Tucumán. Instituto de Ecología Regional. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Instituto de Ecología Regional; ArgentinaFil: Homeier, Juergen. Universität Göttingen; AlemaniaFil: Jadan, Oswaldo. Universidad de Cuenca; EcuadorFil: Llambi, Luis Daniel. Escuela Politécnica Nacional; EcuadorFil: Loza Rivera, María Isabel. University of Missouri; Estados Unidos. Herbario Nacional de Bolivia; Bolivia. Missouri Botanical Garden; Estados UnidosFil: Macía, Manuel J.. Universidad Autónoma de Madrid; EspañaFil: Malhi, Yadvinder. University of Oxford; Reino UnidoFil: Malizia, Lucio Ricardo. Universidad Nacional de Jujuy. Facultad de Ciencias Agrarias; ArgentinaFil: Peralvo, Manuel. Consorcio para el Desarrollo Sostenible de la Ecorregión Andina; EcuadorFil: Pinto, Esteban. Consorcio para el Desarrollo Sostenible de la Ecorregión Andina; EcuadorFil: Tello, Sebastián. Missouri Botanical Garden; Estados UnidosFil: Silman, Miles. Center for Energy, Environment and Sustainability; Estados UnidosFil: Young, Kenneth R.. University of Texas at Austin; Estados Unido

Bauhinia tuichiensis (Fabaceae, Cercideae), una Especie Nueva del Bosque Seco de la Regi\uf3n Madidi, Bolivia

Volume: 22Start Page: 148End Page: 15

Passiflora madidiana, a New Species of Passifloraceae from Northern Bolivia

Volume: 20Start Page: 285End Page: 28

Comparisons of the strength and shape of elevational gradients between scales and between observed β-diversity and β-deviations.

<p>β-deviations were calculated using the random SAD (r-SAD) and fixed SAD (f-SAD) null models (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121458#sec002" target="_blank">Methods</a>). A) Strength of the gradients measured using adjusted R² values (_adj.R²) from cubic regressions between diversity and elevation. Black circles represent original _adj.R² estimates. Grey regions show the distribution of values based on 1,999 bootstrapped regressions. Black lines represent 99% confidence intervals. B) Shape of the gradients measured using standardized regression coefficients. Only the coefficients for elevation (b₁) and elevation squared (b₂) are presented. Other coefficients lead to similar conclusions. Black symbols represent original estimates. Black arrows show the change in coefficients between observed β-diversity and β-deviations at a given spatial scale. Black lines represent 99% data ellipses which define confidence regions. Other symbols show the distribution of values based on bootstrapped regressions.</p

Variation in the overall magnitude of β-deviations between small and large spatial scales.

<p>β-deviations were calculated using the random SAD (r-SAD) and fixed SAD (f-SAD) null models (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121458#sec002" target="_blank">Methods</a>). The horizontal grey line marks the value of no difference from null model expectations (i.e. β-deviation of zero). β-deviations above the line indicate higher β-diversity than expected by random sampling of individuals from observed species pools. Note that β-deviations are higher at large scales than at small scales (linear mixed-effects model: <i>t</i>₂₇₆ < 38.97; <i>p</i> < 0.001). In addition, mean β-deviations are statistically different from zero for all combinations of spatial scale and null model (one sample t-tests: |<i>t</i>| > 4.77; p < 0.001).</p

Distributions of 2,668 woody plant species along the elevational gradient.

<p>Each vertical line represents the elevational range of a species in the Madidi region. Ranges are defined as the interval between the lowest and highest elevations at which a species was found within the full network of plots. The horizontal dashed line marks the elevation at which there seems to be a break in the continuous turnover in forest composition along the elevational gradient. Above 3,725 m, forests are composed only of 3 woody plant species: <i>Gynoxys asterotricha</i>, <i>G</i>. <i>compressissima</i> and <i>Polylepis pepei</i>.</p