Functional and Floristic Dynamics of Amazonian Forests

Intact Amazonian forests are often considered to be in a steady-state, where gains in growth and recruitment are offset by losses in mortality and where net carbon accumulation is close to zero. However, permanent plot data have shown that this ecosystem removes ca. 0.4 Pg of carbon per year from the atmosphere, approximately 5% of annual anthropogenic emissions. This thesis tests several competing hypothesized drivers of detected shifts in the structure and dynamics of intact forest, by assessing changes in functional and floristic composition over the last 30 years in over 100 long-term tree monitoring plots distributed across the Amazon. I first show that the majority of species are restricted to wetter conditions, indicating that stronger and more frequent droughts could threaten many species (Chapter 3). I generated an index of water-deficit affiliation for more than 500 genera and 1800 species (Chapter 3), and demonstrated that this index can predict drought-induced mortality in several drought experiments (Chapter 4). Finally, I document how floristic and functional composition of Amazonian forests has shifted over the last 30 years: forests are increasingly dominated by large-statured taxa, and further, large trees are becoming even larger in absolute size (Chapters 5 and 6). However, relative gains in basal area were similar across size classes and canopy status. In addition, recruits are increasingly comprised of dry-affiliated genera, while the mortality of wet-affiliated genera has increased in plots where the dry season has become more intense. Communities are becoming more dry- affiliated, although these changes still lag behind the drying trend. Overall, this thesis shows the potential vulnerability of Amazonian biodiversity to an increase in aridity and supports the hypotheses that a changing climate and increased atmospheric CO2 are driving changes in Amazonian floristic and functional dynamics

Distribuição, riqueza, diversidade e estabelecimento de áreas prioritárias para a conservação de Melastomataceae Juss. no estado do Paraná

Orientador: Renato GoldenbergCoorientador: Eric Camargo SmidtMonografia (Bacharelado) - Universidade Federal do Paraná. Setor de Ciências Biológicas. Curso de Graduação em Ciências BiológicasResumo : A família Melastomataceae Juss. está distribuída nas regiões tropicais e subtropicais sendo encontrada em diversas unidades fitogeográficas. No estado do Paraná muitas coletas para a família foram realizadas ao longo dos anos e encontram-se reunidas em diversas coleções botânicas. Nos últimos anos estudos sobre a maioria dos gêneros da família presentes no estado foram realizados. A reunião dos dados gerados pelos diversos registros de coleções botânicas confirmados por estes estudos realizados possibilitaram o presente estudo que buscou encontrar e entender o padrão de distribuição, riqueza e diversidade (índice de Shannon) da família no Paraná e nas cinco Regiões Fitogeográficas propostas para o estado (Campos, Cerrado, Floresta Estacional Semidecidual, Floresta Ombrófila Densa e Floresta Ombrófila Mista) e através da análise de complementaridade revelar as regiões prioritárias para a conservação da família no estado. Utilizou-se quatro estimadores de riqueza (Chao 1, Chao 2, Jackknife 1 e Jackknife 2) para revelar a possibilidade de novas espécies. Foram confirmados 5028 registros de Melastomataceae no Paraná divididos em cinco tribos: Bertolonieae, Melastomeae, Merianieae, Miconieae e Microlicieae. A maioria das coletas foi registrada para a região leste do estado (Campos, Floresta Ombrófila Densa e Floresta Ombrófila Mista), os estimadores de riqueza apontaram essa região como a de maior possibilidade de novas espécies. As tribos Miconieae e Melastomeae foram as mais representativas com mais observações, maior riqueza e diversidade. 17 áreas prioritárias para a conservação foram apontadas pela análise de complementaridade. O maior número de unidades fitogeográficas na região leste; a localização de Curitiba, maior centro de pesquisa do estado; uma maior concentração da agricultura em larga escala nas regiões norte e oeste; as características das unidades fitogeográficas e a preferência de espécies da família por determinadas condições ambientais são os cinco fatores que podem haver influenciado o padrão de distribuição. As tribos Miconieae e Melastomeae são segundo a literatura as de maior representatividade, corroborando os resultados. Das áreas apontadas como prioritárias para a preservação apenas o município de Joaquim Távora não apresentou unidade de conservação já instaurada. Esse tipo de análise deve ser realizado para outros grupos fomentando a elaboração de políticas públicas

Fine-grained water availability drives divergent trait selection in Amazonian trees

Water availability is an important driver of plant functional biogeography. Most studies focus on the effects of precipitation, and neglect the contribution of groundwater as a source when the water table depth (WTD) is accessible to roots. Previous studies suggested that shallow water tables select for acquisitive traits. These studies have mostly contrasted shallow vs. deep water tables, without considering a more fine-grained perspective within shallow water tables or the temporal WTD behavior. Here we tested whether the degree of variation in WTD translates into divergent modes of trait selection. We expect constantly shallow WTD leading to the selection of acquisitive traits, whilst high fluctuation of WTD would lead to tree communities with more conservative traits. We used community and trait data (wood density and leaf traits) from 25 1-ha forest monitoring plots spread over 600 km in central Amazonia, covering a gradient of shallow to intermediate (0–8 m deep) WTD along the Purus-Madeira interfluve. Wood density was measured directly in trunk cores (498 trees) and leaf traits (Specific Leaf Area, Leaf Dry Mass Content, Leaf Thickness) of >6,000 individuals were estimated with FT-NIR (Fourier-Transformed Near-Infrared Spectroscopy) spectral models calibrated with cross-Amazonian data. We observed a turnover of families, genera, and species along the gradient of temporal WTD fluctuation range. This taxonomic turnover was accompanied by a change in wood traits, with higher wood density associated to higher WTD fluctuation and higher climatic water deficit. Leaf traits, however, varied in the opposite direction than initially hypothesized, i.e., trees had more acquisitive traits toward intermediate WTD with higher fluctuation. Based on those results, we propose that the effect of WTD selection should be conceptualized in a quadratic form, going from water excess in very shallow WTD (5 m, limiting condition, with conservative traits again)

Water table depth modulates productivity and biomass across Amazonian forests

AimWater availability is the major driver of tropical forest structure and dynamics. Most research has focused on the impacts of climatic water availability, whereas remarkably little is known about the influence of water table depth and excess soil water on forest processes. Nevertheless, given that plants take up water from the soil, the impacts of climatic water supply on plants are likely to be modulated by soil water conditions.LocationLowland Amazonian forests.Time period1971–2019.MethodsWe used 344 long-term inventory plots distributed across Amazonia to analyse the effects of long-term climatic and edaphic water supply on forest functioning. We modelled forest structure and dynamics as a function of climatic, soil-water and edaphic properties.ResultsWater supplied by both precipitation and groundwater affects forest structure and dynamics, but in different ways. Forests with a shallow water table (depth <5 m) had 18% less above-ground woody productivity and 23% less biomass stock than forests with a deep water table. Forests in drier climates (maximum cumulative water deficit < −160 mm) had 21% less productivity and 24% less biomass than those in wetter climates. Productivity was affected by the interaction between climatic water deficit and water table depth. On average, in drier climates the forests with a shallow water table had lower productivity than those with a deep water table, with this difference decreasing within wet climates, where lower productivity was confined to a very shallow water table.Main conclusionsWe show that the two extremes of water availability (excess and deficit) both reduce productivity in Amazon upland (terra-firme) forests. Biomass and productivity across Amazonia respond not simply to regional climate, but rather to its interaction with water table conditions, exhibiting high local differentiation. Our study disentangles the relative contribution of those factors, helping to improve understanding of the functioning of tropical ecosystems and how they are likely to respond to climate change

Variation in forest root image annotation between experts, novices and AI

Background: The manual study of root dynamics using images requires huge investments of time and resources and is prone to previously poorly quantified annotator bias. Artificial intelligence (AI) image-processing tools have been successful in overcoming limitations of manual annotation in homogeneous soils, but their efficiency and accuracy is yet to be widely tested on less homogenous, non-agricultural soil profiles, e.g., that of forests, from which data on root dynamics are key to understanding the carbon cycle. Here, we quantify variance in root length measured by human annotators with varying experience levels. We evaluate the application of a convolutional neural network (CNN) model, trained on a software accessible to researchers without a machine learning background, on a heterogeneous minirhizotron image dataset taken in a multispecies, mature, deciduous temperate forest.Results: Less experienced annotators consistently identified more root length than experienced annotators. Root length annotation also varied between experienced annotators. The CNN root length results were neither precise nor accurate, taking ~ 10% of the time but significantly overestimating root length compared to expert manual annotation (p = 0.01). The CNN net root length change results were closer to manual (p = 0.08) but there remained substantial variation.Conclusions: Manual root length annotation is contingent on the individual annotator. The only accessible CNN model cannot yet produce root data of sufficient accuracy and precision for ecological applications when applied to a complex, heterogeneous forest image dataset. A continuing evaluation and development of accessible CNNs for natural ecosystems is required

Variation in forest root image annotation by experts, novices, and AI

Background: The manual study of root dynamics using images requires huge investments of time and resources and is prone to previously poorly quantified annotator bias. Artificial intelligence (AI) image-processing tools have been successful in overcoming limitations of manual annotation in homogeneous soils, but their efficiency and accuracy is yet to be widely tested on less homogenous, non-agricultural soil profiles, e.g., that of forests, from which data on root dynamics are key to understanding the carbon cycle. Here, we quantify variance in root length measured by human annotators with varying experience levels. We evaluate the application of a convolutional neural network (CNN) model, trained on a software accessible to researchers without a machine learning background, on a heterogeneous minirhizotron image dataset taken in a multispecies, mature, deciduous temperate forest. Results: Less experienced annotators consistently identified more root length than experienced annotators. Root length annotation also varied between experienced annotators. The CNN root length results were neither precise nor accurate, taking ~ 10% of the time but significantly overestimating root length compared to expert manual annotation (p = 0.01). The CNN net root length change results were closer to manual (p = 0.08) but there remained substantial variation. Conclusions: Manual root length annotation is contingent on the individual annotator. The only accessible CNN model cannot yet produce root data of sufficient accuracy and precision for ecological applications when applied to a complex, heterogeneous forest image dataset. A continuing evaluation and development of accessible CNNs for natural ecosystems is required

The pace of life for forest trees

Tree growth and longevity trade-offs fundamentally shape the terrestrial carbon balance. Yet, we lack a unified understanding of how such trade-offs vary across the world's forests. By mapping life history traits for a wide range of species across the Americas, we reveal considerable variation in life expectancies from 10 centimeters in diameter (ranging from 1.3 to 3195 years) and show that the pace of life for trees can be accurately classified into four demographic functional types. We found emergent patterns in the strength of trade-offs between growth and longevity across a temperature gradient. Furthermore, we show that the diversity of life history traits varies predictably across forest biomes, giving rise to a positive relationship between trait diversity and productivity. Our pan-latitudinal assessment provides new insights into the demographic mechanisms that govern the carbon turnover rate across forest biomes

Standardized drought indices in ecological research: why one size does not fit all

Defining and quantifying drought is essential when studying ecosystem responses to such events. Yet, many studies lack either a clear definition of drought, and/or erroneously assume drought under conditions within the range of “normal climatic variability” (c.f. Slette et al., 2019). To improve the general characterization of drought conditions in ecological studies, Slette et al. (2019) propose that drought studies should consistently relate to the local climatic context, assessing whether reported drought periods actually constitute extremes in water availability

Compositional response of Amazon forests to climate change

Most of the planet's diversity is concentrated in the tropics, which includes many regions undergoing rapid climate change. Yet, while climate‐induced biodiversity changes are widely documented elsewhere, few studies have addressed this issue for lowland tropical ecosystems. Here we investigate whether the floristic and func- tional composition of intact lowland Amazonian forests have been changing by eval- uating records from 106 long‐term inventory plots spanning 30 years. We analyse three traits that have been hypothesized to respond to different environmental dri- vers (increase in moisture stress and atmospheric CO2 concentrations): maximum tree size, biogeographic water‐deficit affiliation and wood density. Tree communities have become increasingly dominated by large‐statured taxa, but to date there has been no detectable change in mean wood density or water deficit affiliation at the community level, despite most forest plots having experienced an intensification of the dry season. However, among newly recruited trees, dry‐affiliated genera have become more abundant, while the mortality of wet‐affiliated genera has increased in those plots where the dry season has intensified most. Thus, a slow shift to a more dry‐affiliated Amazonia is underway, with changes in compositional dynamics (recruits and mortality) consistent with climate‐change drivers, but yet to signifi- cantly impact whole‐community composition. The Amazon observational record sug- gests that the increase in atmospheric CO2 is driving a shift within tree communities to large‐statured species and that climate changes to date will impact forest composition, but long generation times of tropical trees mean that biodiver- sity change is lagging behind climate change