The limited importance of size-asymmetric light competition and growth of pioneer species in early secondary forest succession in Vietnam

It is generally believed that asymmetric competition for light plays a predominant role in determining the course of succession by increasing size inequalities between plants. Size-related growth is the product of size-related light capture and light-use efficiency (LUE). We have used a canopy model to calculate light capture and photosynthetic rates of pioneer species in sequential vegetation stages of a young secondary forest stand. Growth of the same saplings was followed in time as succession proceeded. Photosynthetic rate per unit plant mass (Pmass: mol C g−1 day−1), a proxy for plant growth, was calculated as the product of light capture efficiency [Φmass: mol photosynthetic photon flux density (PPFD) g−1 day−1] and LUE (mol C mol PPFD−1). Species showed different morphologies and photosynthetic characteristics, but their light-capturing and light-use efficiencies, and thus Pmass, did not differ much. This was also observed in the field: plant growth was not size-asymmetric. The size hierarchy that was present from the very early beginning of succession remained for at least the first 5 years. We conclude, therefore, that in slow-growing regenerating vegetation stands, the importance of asymmetric competition for light and growth can be much less than is often assumed

The cocoa yield gap in Ghana: a quantification and an analysis of factors that could narrow the gap

Open Access Article; Published online: 28 Jul 2022CONTEXT Global cocoa production is largely concentrated in West Africa where over 70% of cocoa is produced. Here, cocoa farming is largely a rain-fed, low-input system with low average yields, which are expected to decline with climate change. With increasing demand, there is a need to evaluate opportunities to increase production whilst avoiding deforestation and expansion to croplands. Thus, it is important to know how much additional cocoa can be produced on existing farmland, and what factors determine this potential for increased yield. OBJECTIVE The objective was to quantify the cocoa yield gap in Ghana and identify the factors that can contribute to narrowing the gap. METHODS We calculated the cocoa yield gap as the difference between potential yield (i. water-limited potential(Yw) quantified using a crop model, ii. attainable yield in high-input systems(YE), iii. attainable yield in low-input systems(YF)) and actual farmer yield. Both absolute and relative yield gaps were calculated. We then related each yield gap (absolute & relative) as a function of environment and management variables using mixed-effects models. RESULTS AND CONCLUSIONS There were considerable yield gaps on all cocoa farms. Maximum water-limited yield gaps (YGW) were very large with a mean absolute gap of 4577 kg/ha representing 86% of Yw. Attainable yield gap in high-input (YGE) was lower with mean absolute gap of 1930 kg/ha representing 73% of YE. The yield gap in low-input (YGF) was even lower with mean absolute gap of 469 kg/ha representing 42% of YF. Mixed-effects models showed that, absolute YGW were larger at sites with higher precipitation in the minor wet and minimum temperature in the minor dry season explaining 22% of the variability in YGW. These same factors and cocoa planting density explained 28% of variability in absolute YGE. Regardless of climate, absolute YGF and relative YGW, YGE and YGF were reduced by increasing cocoa planting density and application of fungicide against black pod. The models explained 25% of the variability in absolute YGF, and 33%, 33% and 25% in relative YGW, YGE and YGF respectively. In conclusion, climate determined absolute YGW in Ghana whilst absolute YGE were determined by both climate and management. In contrast, absolute YGF and relative YGW, YGE and YGF can be reduced by agronomic management practices. SIGNIFICANCE Our study is one of the first to quantify cocoa yield gaps in West Africa and shows that these can be closed by improved agronomic practices

Development of pancreatic diseases during long-term follow-up after acute pancreatitis:a post-hoc analysis of a prospective multicenter cohort

Background and Aim: More insight into the incidence of and factors associated with progression following a first episode of acute pancreatitis (AP) would offer opportunities for improvements in disease management and patient counseling. Methods: A long-term post hoc analysis of a prospective cohort of patients with AP (2008–2015) was performed. Primary endpoints were recurrent acute pancreatitis (RAP), chronic pancreatitis (CP), and pancreatic cancer. Cumulative incidence calculations and risk analyses were performed. Results: Overall, 1184 patients with a median follow-up of 9 years (IQR: 7–11) were included. RAP and CP occurred in 301 patients (25%) and 72 patients (6%), with the highest incidences observed for alcoholic pancreatitis (40% and 22%). Pancreatic cancer was diagnosed in 14 patients (1%). Predictive factors for RAP were alcoholic and idiopathic pancreatitis (OR 2.70, 95% CI 1.51–4.82 and OR 2.06, 95% CI 1.40–3.02), and no pancreatic interventions (OR 1.82, 95% CI 1.10–3.01). Non-biliary etiology (alcohol: OR 5.24, 95% CI 1.94–14.16, idiopathic: OR 4.57, 95% CI 2.05–10.16, and other: OR 2.97, 95% CI 1.11–7.94), RAP (OR 4.93, 95% CI 2.84–8.58), prior pancreatic interventions (OR 3.10, 95% CI 1.20–8.02), smoking (OR 2.33, 95% CI 1.14–4.78), and male sex (OR 2.06, 95% CI 1.05–4.05) were independently associated with CP. Conclusion: Disease progression was observed in a quarter of pancreatitis patients. We identified several risk factors that may be helpful to devise personalized strategies with the intention to reduce the impact of disease progression in patients with AP.</p

Development of pancreatic diseases during long-term follow-up after acute pancreatitis:a post-hoc analysis of a prospective multicenter cohort

Background and Aim: More insight into the incidence of and factors associated with progression following a first episode of acute pancreatitis (AP) would offer opportunities for improvements in disease management and patient counseling. Methods: A long-term post hoc analysis of a prospective cohort of patients with AP (2008–2015) was performed. Primary endpoints were recurrent acute pancreatitis (RAP), chronic pancreatitis (CP), and pancreatic cancer. Cumulative incidence calculations and risk analyses were performed. Results: Overall, 1184 patients with a median follow-up of 9 years (IQR: 7–11) were included. RAP and CP occurred in 301 patients (25%) and 72 patients (6%), with the highest incidences observed for alcoholic pancreatitis (40% and 22%). Pancreatic cancer was diagnosed in 14 patients (1%). Predictive factors for RAP were alcoholic and idiopathic pancreatitis (OR 2.70, 95% CI 1.51–4.82 and OR 2.06, 95% CI 1.40–3.02), and no pancreatic interventions (OR 1.82, 95% CI 1.10–3.01). Non-biliary etiology (alcohol: OR 5.24, 95% CI 1.94–14.16, idiopathic: OR 4.57, 95% CI 2.05–10.16, and other: OR 2.97, 95% CI 1.11–7.94), RAP (OR 4.93, 95% CI 2.84–8.58), prior pancreatic interventions (OR 3.10, 95% CI 1.20–8.02), smoking (OR 2.33, 95% CI 1.14–4.78), and male sex (OR 2.06, 95% CI 1.05–4.05) were independently associated with CP. Conclusion: Disease progression was observed in a quarter of pancreatitis patients. We identified several risk factors that may be helpful to devise personalized strategies with the intention to reduce the impact of disease progression in patients with AP.</p

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database - enhanced coverage and open access

This article has 730 authors, of which I have only listed the lead author and myself as a representative of University of HelsinkiPlant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.Peer reviewe

TRY plant trait database - enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Inferring plant–plant interactions using remote sensing

Rapid technological advancements and increasing data availability have improved the capacity to monitor and evaluate Earth's ecology via remote sensing. However, remote sensing is notoriously ‘blind’ to fine-scale ecological processes such as interactions among plants, which encompass a central topic in ecology. Here, we discuss how remote sensing technologies can help infer plant–plant interactions and their roles in shaping plant-based systems at individual, community and landscape levels. At each of these levels, we outline the key attributes of ecosystems that emerge as a product of plant–plant interactions and could possibly be detected by remote sensing data. We review the theoretical bases, approaches and prospects of how inference of plant–plant interactions can be assessed remotely. At the individual level, we illustrate how close-range remote sensing tools can help to infer plant–plant interactions, especially in experimental settings. At the community level, we use forests to illustrate how remotely sensed community structure can be used to infer dominant interactions as a fundamental force in shaping plant communities. At the landscape level, we highlight how remotely sensed attributes of vegetation states and spatial vegetation patterns can be used to assess the role of local plant–plant interactions in shaping landscape ecological systems. Synthesis. Remote sensing extends the domain of plant ecology to broader and finer spatial scales, assisting to scale ecological patterns and search for generic rules. Robust remote sensing approaches are likely to extend our understanding of how plant–plant interactions shape ecological processes across scales—from individuals to landscapes. Combining these approaches with theories, models, experiments, data-driven approaches and data analysis algorithms will firmly embed remote sensing techniques into ecological context and open new pathways to better understand biotic interactions