Effective Adaptive Management Research Articles

Leveraging deep learning and computer vision technologies to enhance management of coastal fisheries in the Pacific region

This paper presents the design and development of a coastal fisheries monitoring system that harnesses artificial intelligence technologies. Application of the system across the Pacific region promises to revolutionize coastal fisheries management. The program is built on a centralized, cloud-based monitoring system to automate data extraction and analysis processes. The system leverages YoloV4, OpenCV, and ResNet101 to extract information from images of fish and invertebrates collected as part of in-country monitoring programs overseen by national fisheries authorities. As of December 2023, the system has facilitated automated identification of over six hundred nearshore finfish species, and automated length and weight measurements of more than 80,000 specimens across the Pacific. The system integrates other key fisheries monitoring data such as catch rates, fishing locations and habitats, volumes, pricing, and market characteristics. The collection of these metrics supports much needed rapid fishery assessments. The system’s co-development with national fisheries authorities and the geographic extent of its application enables capacity development and broader local inclusion of fishing communities in fisheries management. In doing so, the system empowers fishers to work with fisheries authorities to enable data-informed decision-making for more effective adaptive fisheries management. The system overcomes historically entrenched technical and financial barriers in fisheries management in many Pacific island communities.

Learning consciousness in managing water for the environment, exemplified using Macquarie River and Marshes, Australia

Context Ongoing learning is essential for freshwater ecosystem management, but there is limited documentation of successful integration into management. Aims We aimed to increase learning-related understanding required for effective adaptive management of water for the environment, in water-stressed and contested river systems. Methods We developed a learning approach (requisite learning) for managing water for the environment, demonstrated with real-world examples from the Macquarie River and Marshes, Australia. Key results Four co-existing, interdependent learning types enable effective management of water for the environment: (1) ‘adjusting routines’, (2) ‘adaptive assessment’, (3) ‘changing practice’, and (4) ‘transforming governance’, exemplified by using management of water for the environment for the Macquarie River and Marshes. To enable and improve requisite learning, stakeholder social learning, and flexibility in governance arrangements, must develop. Conclusions Ongoing learning is essential for effective adaptive management. Understanding what requisite learning is and how capacity can be improved, will help achieve outcomes required of managing water for the environment. Implications Effective management of water for the environment is essential, transparently delivering environmental outcomes and accounting for decision-making. To do this, we need to improve explicit learning understanding by nurturing learning mandates and champions, fostering social learning, increasing flexibility in governance arrangements, and institutionalising learning.

Projected changes in extreme daily precipitation linked to changes in precipitable water and vertical velocity in CMIP6 models

Understanding the drivers of precipitation and their changes in a non-stationary climate is crucial for effective climate adaptation and water resource management, as it helps us anticipate and respond to shifting precipitation patterns and their impacts. Here, analysing simulations from the Coupled Model Intercomparison Project Phase 6 (CMIP6) we show that the conditional probability of extreme daily precipitation given joint extremes of two drivers (precipitable water and vertical velocity) will be stable in a 3 °C warmer future. Consistent with earlier work, we find that the near-global increase in precipitable water (thermodynamic influence) is the baseline for changes in extreme precipitation, which are modulated by changes in vertical velocity (dynamic influence). Thus, in regions where vertical velocity increases, the effect of the two drivers is additive and their changes contribute to an increase in extreme precipitation. The changes of the two drivers are opposite where vertical velocity decreases, resulting in only small increases in extreme precipitation or even a decrease. Furthermore, we reveal that there are moderate changes in the dependence between the drivers, which are larger over the ocean than over landmasses, but they contribute only little to the overall changes in extreme precipitation. We conclude that the use of two very simple drivers that are readily available from climate models can be of great utility for evaluating precipitation extremes in models and understanding their projected changes.

Switch from fight-flight to freeze-hide: The impacts of severe stress and brain serotonin on behavioral adaptations in flatfish.

Animals often experience changes in their environment that can be perceived as stressful. Previous evidence indicates that different individuals may have distinct stress responses. The role of serotonin (5-HT) in stress adaptation is well established, but its relationship with different defense strategies and the persistence of physiological and behavioral responses in different individuals during repeated acute stress remain unclear. In this study, using olive flounder (Paralichthys olivaceus) as a model, we analyzed the relationship between boldness and neurotransmitter 5-HT activity. We found that 5-HT suppression with 5-HT synthesis inhibitor p-chlorophenylalanine (pCPA) and 5-HT receptor subtype 1A (5-HT1A) antagonist WAY-100635 increased their oxygen consumption rates and the boldness of shy individuals. We determined the metabolic and behavioral changes in bold and shy individuals to repeated acute stress. The results suggest that bold individuals switch on passive "energy-saving" personality by changing their defense behavior from "fight-flight" to "freeze-hide" during a threat encounter, which manifests high behavioral plasticity. Both behavioral types decreased their spontaneous activity levels, which were also strengthened by limiting metabolic rate. Interestingly, treatment with pCPA and WAY-100635 before stress procedure attenuated stress and increased the boldness across diverse behavioral types. This study provides the initial empirical evidence of how perception of stress impacts both individual defense behavior and personality in this species. These findings can enhance our comprehension of individual variability and behavioral plasticity in animals, thereby improving our ability to develop effective adaptive management strategies.

PROBLEMS OF ADAPTIVE MANAGEMENT OF THE FORMATION OF FINANCIAL AND ECONOMIC POTENTIAL OF ENTERPRISES IN UKRAINE

Adaptive management of the formation of the financial and economic potential of enterprises in Ukraine faces numerous problems that may be related to macroeconomic conditions, internal processes at enterprises, as well as external factors. The problems of adaptive management of the formation of the financial and economic potential of enterprises in Ukraine are closely related to the processes of digitalization of business and enterprise management in the context of the formation of the "National Strategy of Industry 4.0". Today, the transition of companies to digital technologies using Industry 4.0 tools is one of the leading directions of their development and the country as a whole. Attempts to transition to digitalization in the context of Industry 4.0 made it possible to create in Ukraine the National Movement "Industry 4.0 in Ukraine", which acts as a platform for uniting business associations, participants and communities of markets, enterprise management systems, mechanical engineering, engineering, scientists and researchers for the purpose of transition of Ukrainian enterprises to digital transformation standards. For adaptive management of forming the financial and economic potential of enterprises in Ukraine, possible threats on the path to digitalization can include: cyber security and possible hacker attacks; problems in the operation of equipment, property damage and theft; state policy and activities of the regulator in the market; the COVID-19 pandemic, an economic crisis or an unstable situation in the country; actions by competitors, unfair competition; low level of financing and investment inflow, the financial component of the digital transformation process; lack of qualified workers and specialists in the direction of digital transformations. To overcome these problems, a comprehensive approach is needed, including stabilization of the macroeconomic environment, improvement of the legal framework, development of infrastructure, support for innovation, and the fight against corruption. This will create favorable conditions for effective adaptive management of the formation of the financial and economic potential of enterprises in Ukraine.

Forest fires in a changing climate: risk assessment and management in Leiria national forest, Portugal

Forest ecosystems are vital for sustainable development and human well-being globally and in Europe. Sustainably managed forests are fundamental in combating natural disasters and providing multiple important goods and services for humans and the environment. However, with increasing climate change and its associated effects, forests have become severely and regularly prone to fires. This is seriously threatening forest protection, human safety, the economy, and biodiversity. In this context, understanding future forest fire risks, susceptibility, hazards, and fire prevention is essentially needed. This study thus examines the forest fire risks and hazards in Leiria National Forest (Mata Nacional de Leiria), Portugal using the 2017 forest fire as a benchmark. With the adoption of GIS and remote sensing techniques and data, vegetation type (NDMI), human factors (roads and settlement proximity), and terrain characteristics (slope and aspect) were assessed to map fire risk. Through multi-criteria analysis, these data were integrated to generate a forest fire risk index. Results demonstrate that about 46% of the study area is within high-risk and risky zones, 50% is considered moderate-risk fire zones and 3% is classified as low and risk-free zones. Sensitivity analysis indicated that high-risk areas are mostly low moisture coniferous fuel types while risk-free areas are high moisture deciduous fuel types. Further, it was established that the observed high-risk and risky zones are attributed mostly to proximity to settlements and roads and little topographical influence. The study thus suggests an increased future forest fire risk under the prevailing conditions and a hiking potential of increased burnt areas. We thus proposed effective proactive measures and adaptive management approaches to prevent and mitigate the devastating impacts of forest fires within the study location.

Geographical Trapping of Synchronous Extremes Amidst Increasing Variability of Indian Summer Monsoon Rainfall

AbstractConcurrent extreme rainfall events, or synchronous extremes, during Indian Summer Monsoon Rainfall (ISMR), cause significant damage, but their spatiotemporal evolution remains unclear. Using the event synchronization approach to examine the synchronicity of extreme rainfall events from 1901 to 2019, we find that Central India consistently hosts strongly connected synchronous extreme hubs with localized connections, indicating the geographical trapping of these concurrent events in the region. We observe a moderate positive correlation between network cohesiveness and El Niño Southern Oscillations (ENSO), and a negative correlation between ENSO and link lengths, suggesting localized synchronicity during El Niño dominant decades and opposite patterns in La Niña periods. Despite increasing ISMR variability and spatial nonuniformity, the persistence of hubs and network attributes could offer insights for predicting synchronous extremes, informing effective adaptation and risk management strategies during the monsoon season.

Future projections of thermal regimes and mixing characteristics in a monomictic reservoir under climate change

Stratified structures are basic physical characteristics of lakes and reservoirs, which play important roles in vertical convection and mixing processes. Global warming is expected to alter the thermal regimes and stratified characteristics of lakes and reservoirs, leading to potential environmental risks and ecological problems. In this study, we used a two-dimensional hydrodynamic model to explore the response characteristics of the thermal regime to climate change in the Xiluodu Reservoir. The study is conducted based on four representative concentration pathways (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5). The results show that the projected surface water temperature of the reservoir will significantly increase (0.13 °C/10a-0.34 °C/10a) from 2022 to 2099, and the warming rate of the water in the epilimnion and thermocline will be greater than that in the hypolimnion, leading to enhanced vertical stratification in the reservoir. The stratification stability index (SI) will increase by 14.4 % under RCP 8.5 compared to that under RCP 2.6. The main responses of the reservoir to climate change are earlier start date and later end date of stratification, which may increase the duration of hypoxia in the hypolimnion and promote the succession of planktonic algal communities. The withdrawal water temperature in the reservoir will significantly increase under the climate warming scenarios (0.03 °C/10a-0.22 °C/10a). Furthermore, our research results indicate that selective withdrawal is still needed to mitigate the adverse effects of cold water from the reservoir during the fish spawning period under the strongest warming scenario, and can also be an effective adaptive management strategy to regulate thermal stability in the reservoir. This study provides a scientific and theoretical reference for the sustainable management of similar deep monomictic reservoirs under climate change.

Interactions among rainfall, fire, forbs and non-native grasses predict occupancy dynamics for the endangered Pacific pocket mouse (Perognathus longimembris pacificus) in a Mediterranean-type ecosystem

It is important to understand species-habitat relationships to implement effective adaptive management for rare species. However, it can be challenging to assess habitat associations and their relationships to abiotic stressors in dynamic habitats without the insights that can be gained from long-term monitoring. We report results from the first six years of extensive track tube monitoring of the largest two of three remaining extant populations of federally endangered Pacific pocket mouse (Perognathus longimembris pacificus) in a coastal Mediterranean-type ecosystem on Marine Corps Base, Camp Pendleton in southern California, USA. We used dynamic occupancy and structural equation modeling to assess potential drivers of population trends that included habitat, fire history, rainfall, disturbance, and the presence of other small mammals. We found that the variables that best predicted mouse occupancy were moderate to high forb and perennial herb cover (40–80%), and moderate to high open ground (20–70%) and low non-native grass cover (<20%), Non-native grass cover (>20%) was also a strong predictor of lower PPM colonization and increased extinction probabilities, with the extent of non-native grass cover being strongly influenced by annual rainfall and recency of fire. Our study adds to the growing literature on effects of invasive annual grasses on native species in Mediterranean-type ecosystems. We suggest that habitat management could be based upon promotion of open forb and perennial herb dominated habitats with reduction of non-native grasses by prescribed fire and other methods. These types of spatial and temporal monitoring programs can support land managers by creating a monitoring and management feedback loop. They can reveal landscape and environmental variables associated with species persistence, inform habitat management goals, and help managers to assess the success of management actions on populations of conservation concern.

Forest Phenology under Differing Topographic Conditions: A Case Study of Changbai Mountain in Northeast China

Forest phenology is sensitive to climate change, and its responses affect many land surface processes, resulting in a feedback effect on climate change. Human activities have been the main driver of climate change’s long-term shifts in temperature and weather patterns. Forest phenology, understood as the timing of the annual cycles of plants, is extremely sensitive to changes in climate. Quantifying the responses of temperate forest phenology under an elevational range of topographic conditions that mimic climate change is essential for making effective adaptive forest ecosystem management decisions. Our study utilized the Google Earth Engine (GEE), gap filling, and the Savitzky–Golay (GF-SG) algorithm to develop a long-time series spatio-temporal remote sensing data fusion. The forest phenology characteristics on the north slope of Changbai Mountain were extracted and analyzed annually from 2013 to 2022. Our study found that the average start of the growing season (SOS) on the north slope of Changbai Mountain occurred between the 120th–150th day during the study period. The end of the growing season (EOS) occurred between the 270th–300th day, and the length of the growing season (LOS) ranged from the 110th–190th day. A transect from the northeast to southwest of the study area for a 10-year study period found that SOS was delayed by 39 d, the EOS advanced by 32 d, and the LOS was gradually shortened by 63 d. The forest phenology on the north slope of Changbai Mountain showed significant topographic differentiations. With an increase of 100 m in altitude, the mean SOS was delayed by 1.71 d (R2 = 0.93, p &lt; 0.01). There were no obvious trends in EOS variation within the study area altitude gradient. LOS decreased by 1.23 d for each 100 m increase in elevation (R2 = 0.90, p &lt; 0.01). Forests on steep slopes had an earlier SOS, a later EOS, and a longer LOS than forests on gentle slopes. For each degree increase in slope, SOS advanced by 0.12 d (R2 = 0.53, p = 0.04), EOS was delayed by 0.18 d (R2 = 0.82, p = 0.002), and the LOS increased by 0.28 d (R2 = 0.78, p = 0.004). The slope aspect had effects on the EOS and the LOS but had no effect on the SOS. The forest EOS of the south aspect was 3.15 d later than that of the north aspect, and the LOS was 6.47 d longer. Over the 10-year study period, the phenology differences between the north and south aspects showed that the LOS difference decreased by 0.85 d, the SOS difference decreased by 0.34 d, and the EOS difference decreased by 0.53 d per year. Our study illustrates the significance of the coupling mechanism between mountain topography and forest phenology, which will assist our future understanding of the response of mountain forest phenology to climate change, and provide a scientific basis for further research on temperate forest phenology.

Open water dreissenid mussel control projects: lessons learned from a retrospective analysis

Dreissenid mussels are one of the most problematic aquatic invasive species (AIS) in North America, causing substantial ecological and economic effects. To date, dreissenid mussel control efforts in open water have included physical, biological, and chemical methods. The feasibility of successful dreissenid mussel management or eradication in lakes is relatively undocumented in the freshwater management literature. This review presents information on 33 open water dreissenid mussel control projects in 23 North America lakes. We reviewed data from past dreissenid mussel control projects and identified patterns and knowledge gaps to help inform adaptive management strategies. The three key lessons learned include (1) pre- and post-treatment survey methods that are designed to meet management objectives are beneficial, e.g., by sampling for all life stages and taking into account that no survey method is completely comprehensive; (2) defining the treatment area—particularly ensuring it is sufficiently large to capture all life stages present—is critical to meeting management objectives; and (3) control projects provide an opportunity to collect water chemistry, effects on non-target organisms, and other efficacy-related data that can inform safe and effective adaptive management.

Using the Rao's Q diversity index as an indicator of protected area effectiveness in conserving biodiversity

Developing innovative monitoring systems for biodiversity outcomes in protected areas (PAs) are important to enable effective adaptive management. Here we show how to quantitatively detect and monitor temporal and spatial patterns in environmental heterogeneity, an important indicator of ecological integrity and biodiversity patterns. We used a 28-year time series (1991–2018) generated from freely available Landsat satellite imagery and Rao's quadratic index to calculate relative heterogeneity and trends in heterogeneity for 41 PAs across South Africa. We selected PAs where mega-herbivore assemblages were similar and where management objectives were broadly aligned with South African legislation to protect biodiversity. There was a three-fold difference in heterogeneity among PAs, mainly linked to variation in topography and rainfall. Heterogeneity decreased in 12 (29%), increased in three (7%) and remained stable in 26 (64%) PA's. These trends were not explained by overall heterogeneity, PA size, or rainfall – i.e., PAs that were smaller, drier, and more homogenous were not more likely to show decreasing trends than PAs that were larger, wetter, and generally more heterogenous. Rather trends in heterogeneity are likely the result of interactions between regional process (e.g., rainfall) and local factors such fire regimes, megaherbivore densities, and park management. The framework presented here can be extended to include every PA nationally, or even globally, and the data product fully automated. This presents an opportunity for conservation management to incorporate this important biodiversity indicator in PA monitoring programs as well as other large-scale ecological monitoring initiatives.

Human and Physical Dilemmas in Small-Scale Irrigation Development: Evidence from Climate-Sensitive Northern Ghana

Irrigation is widely accepted as an effective adaptive management strategy for small-scale farmers of traditional rain-fed crops in areas experiencing climate change. These changes include timing of onset of the rainy season and the amount and intensity of rainfall—both issues of concern to rural farmers in northern Ghana. Despite obvious benefits, it is not apparent if farmers in this area perceive irrigation as a viable or attainable option to deal with potential food insecurity related to climate change. In this article, we examine three main issues: (1) dry spells during the early parts of the farming season, (2) impacts of these dry spells on farming systems, and (3) farmer adaptation through different agricultural water management practices. A geographic information system and remote sensing analysis using Landsat 8 and field Global Positioning System data supports findings from seventy-two in-depth interviews with smallholder farmers. Results from this integrated assessment reveal sociocultural and physical barriers to manual irrigation, including labor needs, water source proximity, and size of operational land holding. Increasing climate variability in this region will necessitate adaptation by local farmers; however, we argue that irrigation projects should not be initiated without detailed understanding of local sociocultural contexts. Effectively moving water from source to crop will require integrative knowledge of local hydrogeology and sociocultural norms.

Adaptive Management of Business Entities in the Context of Digitalization of the Economy

The advanced development of the world’s economies requires a detailed study of the adaptive management of business entities in the economy digitalization. It includes ensuring the economic growth of countries and promoting the use of information and communication technologies in the digital economy. Digitalization of the world’s economies is secured by implementing relevant legislation and making political decisions to implement state policy and strategy in the digital economy sector for effective adaptive management of business entities. The aim of the research is to establish the regularity of the impact of digitalization in the world’s economies on the adaptive management of business entities. This can be achieved by conducting a regression analysis to reflect the dependence of the influence of factors on the level of digitalization in 25 economies of the world according to the IMD digital competitiveness ranking. This is aimed at checking the features of the use of various digital technologies at the enterprise level for 2022. Research methods: method of synthesis of information; regression analysis; systematization, generalization. Results. It is established that as a result of regression analysis, the value of the coefficient of determination indicates that the regression model explains 78 % of the relationship between the future readiness of countries to implement digital technologies and the use of information and communication technologies, but there are still a small number of other factors that are not included in the regression model.

Artificial intelligence and automated monitoring for assisting conservation of marine ecosystems: A perspective

Conservation of marine ecosystems has been highlighted as a priority to ensure a sustainable future. Effective management requires data collection over large spatio-temporal scales, readily accessible and integrated information from monitoring, and tools to support decision-making. However, there are many roadblocks to achieving adequate and timely information on both the effectiveness, and long-term success of conservation efforts, including limited funding, inadequate sampling, and data processing bottlenecks. These factors can result in ineffective, or even detrimental, management decisions in already impacted ecosystems. An automated approach facilitated by artificial intelligence (AI) provides conservation managers with a toolkit that can help alleviate a number of these issues by reducing the monitoring bottlenecks and long-term costs of monitoring. Automating the collection, transfer, and processing of data provides managers access to greater information, thereby facilitating timely and effective management. Incorporating automation and big data availability into a decision support system with a user-friendly interface also enables effective adaptive management. We summarise the current state of artificial intelligence and automation techniques used in marine science and use examples in other disciplines to identify existing and potentially transferable methods that can enable automated monitoring and improve predictive modelling capabilities to support decision making. We also discuss emerging technologies that are likely to be useful as research in computer science and associated technologies continues to develop and become more accessible. Our perspective highlights the potential of AI and big data analytics for supporting decision-making, but also points to important knowledge gaps in multiple areas of the automation processes. These current challenges should be prioritised in conservation research to move toward implementing AI and automation in conservation management for a more informed understanding of impacted ecosystems to result in successful outcomes for conservation managers. We conclude that the current research and emphasis on automated and AI assisted tools in several scientific disciplines may mean the future of monitoring and management in marine science is facilitated and improved by the implementation of automation.

Farmers’ perceptions of climate change, long-term variability and trends in rainfall in Apac district, northern Uganda

BackgroundClimate change poses severe threats to smallholder farmers' agricultural livelihoods and food security in Sub Saharan Africa. Understanding long-term rainfall trends of variability and extremes at local scales and perceptions regarding long-term changes in climate variables is important in planning appropriate adaptation measures to climate change. This paper examines the perception of farmers in Apac district regarding long-term changes in climate variables and analyzes the trend of occurrence in seasonal and annual rainfall in Apac district, northern Uganda. A cross-sectional survey design was employed to collect data on farmers' perceptions regarding long-term changes in climate from 260 randomly selected smallholder farmers’ households across two sub-counties in Apac district by administering semi-structured questionnaires in February 2018. Monthly rainfall data sets from the Uganda Meteorological Authority (UMA) for the period 1980 to 2019 for Apac district were also used to analyze trends of occurrences in seasonal and annual rainfall in the study area. The non-parametric Sequential Mann–Kendall (SMK) tests were employed at a 5% significance level to detect mean seasonal rainfall trends and abrupt change points.ResultsThe majority of the respondents (87%) perceived a decrease in precipitation over the past 39 years. The plot of forward regression u(ti) values and backward regression u’(ti) values showed interactions indicating rainfall trends, rainfall lower and upper limits and abrupt change points in the different cropping seasons. Analysis of historical series of mean monthly and annual rainfall showed an abrupt change in rainfall in March, April, May (MAM) season in 1982. Although the September, October and November (SON) season did not show a significant abrupt change, there was a significant (p < 0.05) increase in rainfall above the upper limit from 1994 to date.ConclusionThe mean seasonal rainfall for MAM and SON cropping seasons in Apac district were highly variable from different time points within the past 39 years (1980–2019), while June, July, and August (JJA) did not realize a significant change in rainfall within the same study period that the two cropping seasons (MAM and SON) in the district experienced remarkable variations in rainfall. This, therefore, provides a basis for the government to strengthen the provision of an effective climate tailored agricultural advisory service to aid farmers’ adaptation planning at the local level and to assist smallholder farmers and land-use managers in developing effective adaptation management strategies to the effects of climate change.

Rural social-ecological systems vulnerability evolution and spatial-temporal heterogeneity in arid environmental change region: A case study of Minqin Oasis, northwestern China

Theoretical and methodological approaches of social-ecological system's vulnerability provide a new perspective for the sustainable development of arid areas. However, the spatial distribution heterogeneity of internal elements of vulnerability has always been ignored in current academic research and policy practice. Especially in low-exposure but high-vulnerability areas, it is more urgent to explore the vulnerability-causing factors and construct the response capacity. Therefore, according to the concept of vulnerability and its internal element relationship, this study proposes an analysis method for the spatial-temporal heterogeneity of exposure risk and vulnerability. With the aid of GIS, while assessing the spatial-temporal evolution of the overall vulnerability of the Minqin Oasis since the comprehensive management of Shiyang River Basin, we pay more attention to the spatial-temporal heterogeneity of the internal elements of vulnerability. Then applying the stepwise regression and geographically weighted regression, the article further analyzed the intrinsic reasons for spatial-temporal heterogeneity of SES's vulnerability and exposure risk. The results are as follows. 1) The vulnerability in Minqin County from 2008 to 2016 was relatively low and showed an evolutionary trend of increased and then decreased. In addition, the vulnerability area was high in the Quanshan and Dam Districts and low in the Lake District; 2) the spatial-temporal heterogeneity of system exposure risk and vulnerability was significant and gradually reduced; and 3) the five main factors affecting spatial-temporal heterogeneity of the vulnerability of SES in Minqin Oasis include: per capita cultivated area, disaster-affected area, domestic water consumption, financial savings, and afforestation area. Finally, we proposed targeted recommendations for effective adaptive management and sustainable development for exposure-vulnerability heterogeneous regions.

Towards adoption of mobile data collection for effective adaptation and climate risk management in Africa

AbstractThe collection and use of data on climate change and its impacts are crucial for effective climate adaptation and climate risk management. The revolution in internet access, technology and costs has led to a shift from using traditional paper‐based data collection to the use of Mobile Data Collection using Personal Digital Assistants (PDA) such as smartphones and tablets. In this paper, we report our experiences using both approaches for a household and business survey during a climate adaptation study in two Nigerian cities—Makurdi and Calabar. The focus of this paper is to evaluate and compare the effectiveness of using traditional paper‐based data collection and PDAs as data collection tools for climate change study in African societies. In Calabar, data were collected using paper questionnaires, while in Makurdi the questionnaires were developed on Open Data Kit (ODK) and administered using PDAs. Results show that data collection using PDA was faster, cheaper, more accurate and resulted in fewer omissions than paper‐based data collection. There was a time saving of four (4) minutes per questionnaire and a 24% cost saving when using PDA. PDA provides additional benefits where platforms can collect images, videos and coordinates. This significantly improved the credibility of the data collection process and provided further data that allowed for the mapping of environmental phenomena by linking survey research with geo‐referenced data in a geographic information systems platform to provide spatial representations of social and environmental system convergence. PDA offers a tool for collecting data that will make necessary socio‐environmental data available in a faster, reliable and cheaper manner; future research can build on this study by discovering other possible but less highlighted benefits of PDA. Although, with great benefits, there are lessons to be learnt and issues to consider when deploying PDA in large‐scale household surveys.

Stakeholder participation, indicators, assessment, and decision-making: applying adaptive management at the watershed scale

Climate change, population growth, and declining federal budgets are threatening the health of ecosystems, and the services they provide. Under these changing conditions, managing landscapes and resources assumes new and unprecedented challenges. Adaptive management has been identified as a natural resource management approach that allows practitioners to incorporate change and uncertainty into decision-making through an iterative process that involves long-term monitoring and continued review and adjustment of management actions. However, the success of these efforts in watershed health relies on the collective and sustained monitoring of indicators, which is seldom studied. The purpose of this analysis is to examine (1) the practical challenge of choosing a list of indicators for long-term monitoring, (2) the negotiation process among stakeholders around the selection and interpretation of indicators, and (3) the communication tools that can be used to convey the assessment’s results and findings. To do this, we analyze our ongoing work in the Cienega Watershed in southern Arizona. Our analysis shows that the selective use of indicators, regular assessment and review, and establishment of partnerships among stakeholders are all important elements in establishing effective adaptive management efforts. The selection of indicators and data sources is a moving target that requires regular consensus and review among stakeholders. The assessment itself is also a powerful engagement tool with the public at large, providing legitimacy and support to land management decision-making. Here, we outline some lessons learned that can be transferred to other cases and identify potential barriers for engagement, decision-making, and project success.

Use of an Adaptive Management System to Minimize Impacts of Deep-Sea Nodule Collection

Abstract In the nascent deep-sea mining industry, there is currently a high degree of uncertainty about what impacts prolonged metal extraction will have on the receiving environment. There is also concern regarding the transparency and monitoring of operations since the target environment is extremely remote and inaccessible. Polymetallic nodule collection is being pursued, which is distinct from other forms of deep-sea mining in that the resource is distributed in a thin layer atop the seabed, unlike cobalt-rich crusts or massive sulfides, which are concentrated in specific areas. The second distribution of nodules provides opportunities for dynamic mine planning not available for other mineral sources as many constraints normally affecting mining operations like waste stripping or underground development are absent. Also, the highly mobile ship-based collection system that utilizes robotic collectors is easily relocated to other areas in response to emerging data on environmental constraints such as proximity to fragile habitats, sensitive species, or high cumulative impacts. An adaptive management system has been identified as a vital strategy to address scientific uncertainty of ecological impacts of deep-sea mining. The design features dynamic mine planning, scenario modeling, and impact forecasting. Also, operating data will be transparently viewable in a publicly available dashboard. This paper describes an implementation of a threshold-based framework for an effective adaptive management system designed to leverage the unique characteristics inherent to the resource.