Small-scale fisheries (SSF) account for much of the global fish catch, but data to assess them often do not exist, impeding assessments of their historical dynamics and status. Here, we propose an approach to assess ‘data-less’ SSF using local knowledge to produce data, life history theory to describe their historical multispecies dynamics, and length-based reference points to evaluate stock status. We demonstrate use of this approach in three data-less SSFs of the Congo Basin. Fishers’ recalls of past fishing events indicated fish catch declined by 65–80% over the last half-century. Declines in and depletion of many historically important species reduced the diversity of exploited species, making the species composition of the catch more homogenous in recent years. Length-at-catch of 11 of the 12 most important species were below their respective lengths-at-maturity and optimal lengths (obtained from Fishbase) in recent years, indicating overfishing. The most overfished species were large-bodied and found in the Congo mainstem. These results show the approach can suitably assess data-less SSF. Fishers’ knowledge produced data at a fraction of the cost and effort of collecting fisheries landings data. Historical and current data on fish catch, length-at-catch, and species diversity can inform management and restoration efforts to curb shifting baselines of these fisheries. Classification of stock status allows prioritizing management efforts. The approach is easy to apply and generates intuitive results, having potential to complement the toolkits of researchers and managers working in SSF and engage stakeholders in decision-making processes.
Tag: data analysis
Manual on general management principles for breeding seed orchards (BSOs). Part II: data analysis
This guide provides a manual for analysis of data from breeding seed orchards in the statistical program R mainly based on BreedR (Muñoz and Sanchez 2020) aiming at the highest possible genetic gains while considering genetic diversity and providing knowledge on provenances as well.
Climatic drivers and ecological implications of variation in the time interval between leaf-out and flowering
Leaf-out and flowering in any given species have evolved to occur in a predetermined sequence, with the inter-stage time interval optimized to maximize plant fitness. Although warming-induced advances of both leaf-out and flowering are well documented, it remains unclear whether shifts in these phenological phases differ in magnitudes and whether changes have occurred in the length of the inter-stage intervals. Here, we present an extensive synthesis of warming effects on flower-leaf time intervals, using long-term (1963–2014) and in situ data consisting of 11,858 leaf-out and flowering records for 183 species across China. We found that the timing of both spring phenological events was generally advanced, indicating a dominant impact of forcing conditions compared with chilling. Stable time intervals between leaf-out and flowering prevailed for most of the time series despite increasing temperatures; however, some of the investigated cases featured significant changes in the time intervals. The latter could be explained by differences in the temperature sensitivity (ST) between leaf and flower phenology. Greater ST for flowering than for leaf-out caused flowering times to advance faster than leaf emergence. This shortened the inter-stage intervals in leaf-first species and lengthened them in flower-first species. Variation in the time intervals between leaf-out and flowering events may have far-reaching ecological and evolutionary consequences, with implications for species fitness, intra/inter-species interactions, and ecosystem structure, function, and stability.
Farmers’ perceptions and matching climate records jointly explain adaptation responses in four communities around Lake Tana, Ethiopia
Farmers’ climate perceptions are responsible for shaping their adaptive responses and are thus essential to consider for the design of strategies to reduce vulnerability and increase resilience. In this study, we collected social data in four communities in the central Ethiopian Highlands on farmers’ climate perceptions and adaptations using group discussions and PRA tools. We related these to climate data spanning 30 years (1981 to 2010), consisting of daily minimum temperature, maximum temperature and precipitation, modelled for the four communities using global databases and regional meteorological data. We found that farmers’ climate perceptions showed considerable spatial and gender differences. Perceptions matched well with records describing climate variability, particularly in terms of the shortening and the increased variability of the rainy season, as well as the occurrence of extreme drought in recent years. Climate change, described by long-term average increases in temperature and decreases in precipitation, was perceived, but with subordinate priority. Perceived climate impacts included reduced crop yield, increased occurrence of pests and diseases and increased crop damage by extreme events and poverty. Adaptations were mainly land based and included agronomic measures, land management and ecosystem restoration. Furthermore, important gender differences in adaptation could be traced back to typical gender roles. Results highlight the risk of broadcast adaptation programs, such as the government-propagated combination of mineral fertilizers and early maturing crop varieties. Most importantly, they point to the need to consider climate variability, site- and gender-specific perceptions and priorities.
Decision dashboard
An information note to describe what decision dashboards are and how they can be used to assist in evidence based decision making and planning.
Harnessing online digital data in biodiversity monitoring
Online digital data from media platforms have the potential to complement biodiversity monitoring efforts. We propose a strategy for integrating these data into current biodiversity datasets in light of the Kunming-Montreal Global Biodiversity Framework.
Data and information in a political forest: The case of REDD+
Data and information are central to policy processes, as they frame the policy problem, the design and the implementation of policy, and evaluation of policy impacts. Better data and information infrastructure is expected to lead to better policies and outcomes, for example, by enabling transparent decision making and enhancing capacity and accountability. However, the collection, selection, representation, framing and application of data are not merely technical and apolitical procedures, but are dependent on the interests represented in the policy processes they aim to inform. Social scientists have pointed to the “politics of numbers” and their effects on forests and trees and on the people relying on them, as well as on those involved in their measurements. We use the case of the Reducing Emissions from Deforestation and Forest Degradation (REDD+) international initiative and focus on the central aspect of understanding drivers of deforestation and measures of REDD+ performance to unpack the politics of policy processes. Data and information are socially constructed, and their interpretations are shaped by the contexts in which they emerge. Dominant beliefs in the transformative power of new data and technologies cannot explain why, often, new information does not translate into policy change and action to halt deforestation. Technological advances in making new and ever larger amounts of data available for analysis are a necessary yet insufficient condition for changing the business as usual in deforestation. Through openness, reflexivity and the tackling of silences in data and information related to the global political economy of deforestation the scientific community can make a key contribution to more equitable policy change.
Improved uncertainties analysis of Indonesia’s Forest Reference Emission Level 2016
Under its Nationally Determined Contribution (NDC), Indonesia has continued to reduce greenhouse gas (GHG) emissions. Reducing emissions from deforestation and forest degradation, the sustainable management of forests, and the conservation and enhancement of forest carbon stocks (REDD+) is a key component of the NDC target from the land sector.
Indonesia submitted its 1st FREL in January 2016 (FREL–2016) covered emissions from deforestation, forest degradation, and peat decomposition. As part of the FREL–2016 improvement, the next FREL should consider land-use change dynamics and emissions/removal in biomass and soil. This improvement needs to reduce the uncertainty of emission factors (EFs) and activity data (AD) including non-CO2 GHGs and reinforce sink monitoring.
Analysis in this working paper aims to improve and re-run the uncertainty in Indonesia’s FREL–2016 by using Monte Carlo simulation (MCS). This working paper is expected to provide recommendations for Indonesia and other wetland-rich countries to meet their conditional land use emission reduction targets through improving the accuracy in their wetlands GHG accounting.
Application Module: Social Network Analysis and Qualitative Data Analysis
The first training series focused on research methodologies and techniques based on the realization that researchers have important knowledge and skills to create robust research products in the topic of sustainable development, which is always complex, and had become even more so in the midst of the pandemic. Material from the training has now been compiled into book form to improve accessibility to this knowledge product.
Small-scale fisheries (SSF) account for much of the global fish catch, but data to assess them often do not exist, impeding assessments of their historical dynamics and status. Here, we propose an approach to assess ‘data-less’ SSF using local knowledge to produce data, life history theory to describe their historical multispecies dynamics, and length-based reference points to evaluate stock status. We demonstrate use of this approach in three data-less SSFs of the Congo Basin. Fishers’ recalls of past fishing events indicated fish catch declined by 65–80% over the last half-century. Declines in and depletion of many historically important species reduced the diversity of exploited species, making the species composition of the catch more homogenous in recent years. Length-at-catch of 11 of the 12 most important species were below their respective lengths-at-maturity and optimal lengths (obtained from Fishbase) in recent years, indicating overfishing. The most overfished species were large-bodied and found in the Congo mainstem. These results show the approach can suitably assess data-less SSF. Fishers’ knowledge produced data at a fraction of the cost and effort of collecting fisheries landings data. Historical and current data on fish catch, length-at-catch, and species diversity can inform management and restoration efforts to curb shifting baselines of these fisheries. Classification of stock status allows prioritizing management efforts. The approach is easy to apply and generates intuitive results, having potential to complement the toolkits of researchers and managers working in SSF and engage stakeholders in decision-making processes.