Assuntos

RESUMO

Plant phenology-the timing of cyclic or recurrent biological events in plants-offers insight into the ecology, evolution, and seasonality of plant-mediated ecosystem processes. Traditionally studied phenologies are readily apparent, such as flowering events, germination timing, and season-initiating budbreak. However, a broad range of phenologies that are fundamental to the ecology and evolution of plants, and to global biogeochemical cycles and climate change predictions, have been neglected because they are "cryptic"-that is, hidden from view (e.g., root production) or difficult to distinguish and interpret based on common measurements at typical scales of examination (e.g., leaf turnover in evergreen forests). We illustrate how capturing cryptic phenology can advance scientific understanding with two case studies: wood phenology in a deciduous forest of the northeastern USA and leaf phenology in tropical evergreen forests of Amazonia. Drawing on these case studies and other literature, we argue that conceptualizing and characterizing cryptic plant phenology is needed for understanding and accurate prediction at many scales from organisms to ecosystems. We recommend avenues of empirical and modeling research to accelerate discovery of cryptic phenological patterns, to understand their causes and consequences, and to represent these processes in terrestrial biosphere models.

Assuntos

RESUMO

To predict forest response to long-term climate change with high confidence requires that dynamic global vegetation models (DGVMs) be successfully tested against ecosystem response to short-term variations in environmental drivers, including regular seasonal patterns. Here, we used an integrated dataset from four forests in the Brasil flux network, spanning a range of dry-season intensities and lengths, to determine how well four state-of-the-art models (IBIS, ED2, JULES, and CLM3.5) simulated the seasonality of carbon exchanges in Amazonian tropical forests. We found that most DGVMs poorly represented the annual cycle of gross primary productivity (GPP), of photosynthetic capacity (Pc), and of other fluxes and pools. Models simulated consistent dry-season declines in GPP in the equatorial Amazon (Manaus K34, Santarem K67, and Caxiuanã CAX); a contrast to observed GPP increases. Model simulated dry-season GPP reductions were driven by an external environmental factor, 'soil water stress' and consequently by a constant or decreasing photosynthetic infrastructure (Pc), while observed dry-season GPP resulted from a combination of internal biological (leaf-flush and abscission and increased Pc) and environmental (incoming radiation) causes. Moreover, we found models generally overestimated observed seasonal net ecosystem exchange (NEE) and respiration (Re ) at equatorial locations. In contrast, a southern Amazon forest (Jarú RJA) exhibited dry-season declines in GPP and Re consistent with most DGVMs simulations. While water limitation was represented in models and the primary driver of seasonal photosynthesis in southern Amazonia, changes in internal biophysical processes, light-harvesting adaptations (e.g., variations in leaf area index (LAI) and increasing leaf-level assimilation rate related to leaf demography), and allocation lags between leaf and wood, dominated equatorial Amazon carbon flux dynamics and were deficient or absent from current model formulations. Correctly simulating flux seasonality at tropical forests requires a greater understanding and the incorporation of internal biophysical mechanisms in future model developments.

Assuntos

RESUMO

Abstract The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.

Resumo A região semiárida do nordeste do Brasil, a Caatinga, é extremamente importante devido à sua biodiversidade e endemismo. Medidas de fisiologia vegetal são cruciais para a calibração de Modelos de Vegetação Globais Dinâmicos (DGVMs) que são atualmente usados para simular as respostas da vegetação diante das mudanças globais. Em um trabalho de campo realizado em uma área de floresta preservada na Caatinga localizada em Petrolina, Pernambuco, medidas de assimilação de carbono (em resposta à luz e ao CO2) foram realizadas em 11 indivíduos de Poincianella microphylla, uma espécie nativa que é abundante nesta região. Estes dados foram utilizados para calibrar a velocidade máxima de carboxilação (Vcmax) usada no modelo INLAND. As técnicas de calibração utilizadas foram Regressão Linear Múltipla (MLR) e técnicas de mineração de dados como Classification And Regression Tree (CART) e K-MEANS. Os resultados foram comparados com o modelo INLAND não calibrado. Verificou-se que a Produtividade Primária Bruta (PPB) simulada atingiu 72% da PPB observada ao usar os valores de Vcmax calibrado, enquanto que o modelo não calibrado obteve-se 42% da PPB observada. Assim, este trabalho mostra os benefícios de calibrar DGVMs usando medidas ecofisiológicas de campo, especialmente em áreas onde os dados de campo são escassos ou inexistentes, como na Caatinga.

Assuntos

RESUMO

Abstract The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.(AU)

Resumo A região semiárida do nordeste do Brasil, a Caatinga, é extremamente importante devido à sua biodiversidade e endemismo. Medidas de fisiologia vegetal são cruciais para a calibração de Modelos de Vegetação Globais Dinâmicos (DGVMs) que são atualmente usados para simular as respostas da vegetação diante das mudanças globais. Em um trabalho de campo realizado em uma área de floresta preservada na Caatinga localizada em Petrolina, Pernambuco, medidas de assimilação de carbono (em resposta à luz e ao CO2) foram realizadas em 11 indivíduos de Poincianella microphylla, uma espécie nativa que é abundante nesta região. Estes dados foram utilizados para calibrar a velocidade máxima de carboxilação (Vcmax) usada no modelo INLAND. As técnicas de calibração utilizadas foram Regressão Linear Múltipla (MLR) e técnicas de mineração de dados como Classification And Regression Tree (CART) e K-MEANS. Os resultados foram comparados com o modelo INLAND não calibrado. Verificou-se que a Produtividade Primária Bruta (PPB) simulada atingiu 72% da PPB observada ao usar os valores de Vcmax calibrado, enquanto que o modelo não calibrado obteve-se 42% da PPB observada. Assim, este trabalho mostra os benefícios de calibrar DGVMs usando medidas ecofisiológicas de campo, especialmente em áreas onde os dados de campo são escassos ou inexistentes, como na Caatinga.(AU)

Assuntos

RESUMO

Abstract The semiarid region of northeastern Brazil, the Caatinga, is extremely important due to its biodiversity and endemism. Measurements of plant physiology are crucial to the calibration of Dynamic Global Vegetation Models (DGVMs) that are currently used to simulate the responses of vegetation in face of global changes. In a field work realized in an area of preserved Caatinga forest located in Petrolina, Pernambuco, measurements of carbon assimilation (in response to light and CO2) were performed on 11 individuals of Poincianella microphylla, a native species that is abundant in this region. These data were used to calibrate the maximum carboxylation velocity (Vcmax) used in the INLAND model. The calibration techniques used were Multiple Linear Regression (MLR), and data mining techniques as the Classification And Regression Tree (CART) and K-MEANS. The results were compared to the UNCALIBRATED model. It was found that simulated Gross Primary Productivity (GPP) reached 72% of observed GPP when using the calibrated Vcmax values, whereas the UNCALIBRATED approach accounted for 42% of observed GPP. Thus, this work shows the benefits of calibrating DGVMs using field ecophysiological measurements, especially in areas where field data is scarce or non-existent, such as in the Caatinga.

Resumo A região semiárida do nordeste do Brasil, a Caatinga, é extremamente importante devido à sua biodiversidade e endemismo. Medidas de fisiologia vegetal são cruciais para a calibração de Modelos de Vegetação Globais Dinâmicos (DGVMs) que são atualmente usados para simular as respostas da vegetação diante das mudanças globais. Em um trabalho de campo realizado em uma área de floresta preservada na Caatinga localizada em Petrolina, Pernambuco, medidas de assimilação de carbono (em resposta à luz e ao CO2) foram realizadas em 11 indivíduos de Poincianella microphylla, uma espécie nativa que é abundante nesta região. Estes dados foram utilizados para calibrar a velocidade máxima de carboxilação (Vcmax) usada no modelo INLAND. As técnicas de calibração utilizadas foram Regressão Linear Múltipla (MLR) e técnicas de mineração de dados como Classification And Regression Tree (CART) e K-MEANS. Os resultados foram comparados com o modelo INLAND não calibrado. Verificou-se que a Produtividade Primária Bruta (PPB) simulada atingiu 72% da PPB observada ao usar os valores de Vcmax calibrado, enquanto que o modelo não calibrado obteve-se 42% da PPB observada. Assim, este trabalho mostra os benefícios de calibrar DGVMs usando medidas ecofisiológicas de campo, especialmente em áreas onde os dados de campo são escassos ou inexistentes, como na Caatinga.