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opencc-zeroNov 2023View details →

Ecological forecasts for marine resource management during climate extremes

Forecasting weather has become commonplace, but as society faces novel and uncertain environmental conditions there is a critical need to forecast ecology. Forewarning of ecosystem conditions during climate extremes can support proactive decision-making, yet applications of ecological forecasts are still limited. We showcase the capacity for existing marine management tools to transition to a forecasting configuration and provide skilful ecological forecasts up to 12 months in advance. The management tools use ocean temperature anomalies to help mitigate whale entanglements and sea turtle bycatch, and we show that forecasts can forewarn of human-wildlife interactions caused by unprecedented climate extremes. We further show that regionally downscaled forecasts are not a necessity for ecological forecasting and can be less skilful than global forecasts if they have fewer ensemble members. Our results highlight capacity for ecological forecasts to be explored for regions without the infrastructure or capacity to regionally downscale, ultimately helping to improve marine resource management and climate adaptation globally.

opencc-zeroMar 2024View details →

Data from: Seasonal bee communities vary in their responses to resources at local and landscape scales: Implication for land managers

Context: There is great interest in land management practices for pollinators; however, a quantitative comparison of landscape and local effects on bee communities is necessary to determine if adding small habitat patches can increase bee abundance or species richness. The value of increasing floral abundance at a site is undoubtedly influenced by the phenology and magnitude of floral resources in the landscape, but due to the complexity of measuring landscape-scale resources, these factors have been understudied. Objectives: To address this knowledge gap, we quantified the relative importance of local versus landscape scale resources for bee communities, identified the most important metrics of local and landscape quality, and evaluated how these relationships vary with season. Methods: We studied season-specific relationships between local and landscape quality and wild-bee communities at 33 sites in the Finger Lakes region of New York, USA. We paired site surveys of wild bees, plants, and soil characteristics with a multi-dimensional assessment of landscape composition, configuration, insecticide toxic load, and a spatio-temporal evaluation of floral resources at local and landscape scales. Results: We found that the most relevant spatial scale and landscape factor varied by season. Early-season bee communities responded primarily to landscape resources, including the presence of flowering trees and wetland habitats. In contrast, mid to late-season bee communities were more influenced by local conditions, though bee diversity was negatively impacted when sites were embedded in highly agricultural landscapes. Soil composition had complex impacts on bee communities, and likely reflects effects on plant community flowering. Conclusions: Early-season bees can be supported by adding flowering trees and wetlands, while mid to late-season bees can be supported by local addition of summer and fall flowering plants. Sites embedded in landscapes with a greater proportion of natural areas will host a greater bee species diversity.

Generation of a network slicing dataset: the foundations for AI-based B5G resource management

This paper introduces a comprehensive network slicing dataset designed to empower artificial intelligence (AI), and other data-based resource management and network performance prediction applications, in 5G and beyond (B5G) networks. The dataset, generated through a packet-level simulator, captures the complexities of network slicing considering the three main network slice types defined by 3GPP: Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Internet of Things (mIoT). It includes a wide range of network scenarios with varying topologies, slice instances, and traffic flows. The included scenarios consist of transport networks, excluding the RAN infrastructure. Each sample consists of pairs of (network scenario, performance metrics). The network configuration includes network topology, traffic characteristics, routing configurations, while the performance metrics are the delay, jitter, and loss for each flow. The dataset is generated with a custom network slicing admission control module, enabling the simulation of realistic scenarios without violating SLAs. This network slicing dataset is a valuable asset for the research community, unlocking opportunities for innovations in 5G and B5G networks.

opencc-by-4.0Nov 2021View details →

Database Search Results for Resource Management in Converged Optical and MillimeterWave Radio Networks Review

Paper Selection Procedure In order to conduct the survey titled "Resource Management in Converged Optical and MillimeterWave Radio Networks: A Review", the authors reviewed works published in the literature with a focus on those that cover most of the identified optimization requirements for converged optical fronthaul and mmWave wireless access networks. The research method is based on the research steps given in "The PRISMA 2020 statement"[1]. The selection procedure is also illustrated in "Database Search Flow Chart.png" figure. The first step was the selection of the papers. We completed this step by making database searches in the ACM, Elsevier (Science Direct), IEEE, IET,  MDPI, Optical Society (OSA), Springer, Taylor & Francis, and Wiley online library databases with keywords ``resource allocation AND converged mmWave fiber wireless (FiWi)'', ``resource management AND converged mmWave fiber wireless (FiWi)'', and ``resource allocation AND converged fiber wireless (FiWi)''. The searches in all databases were completed in May 2021. The resulting collection was screened, to exclude non-scientific texts, book chapters, out of context papers, and survey papers. The remaining 189 papers found in our database search are provided in the excel file titled "FiWi Resource Allocation Database Search.xlsx". Among these papers, our selection criteria was created to present the works that are most relevant to the target network architecture, providing novel implementation solutions to the requirements of the optimization objective. The criteria selected for our eligibility step can be summarized as follows: <ul> <li>The study provided a sound research approach and published after a scholarly review process;</li> <li>The study had a resource management optimization objective for mmWave networks;</li> <li>The study explained the system model and proposed a well-defined optimization algorithm;</li> <li>The effects of the algorithm on a performance metric was reported and the different aspects of the performance metric was analyzed with different evaluation criteria.</li> </ul> This review is limited to the focus scope on converged optical and mmWave radio network solutions and by the databases taken into consideration. The prioritization of the works that address a well-defined optimization algorithm led to the omission of relevant papers. We did not include works that do not clearly define a resource management objective, i.e., a study that focuses on the the hardware implementation aspects of optical and mmWave radio networks with no resource management perspective. We manually excluded all studies that do not match these criteria with a simple scoring system, in which a point is deducted from an eligible paper for each missing criterion. The initial screening process and the data collection steps were carried out by the first author and the final inclusion decision was made by all the reviewers for the studies with the highest scores. After this screening process, we identified 37 papers that focused on at least one of the resource management objectives of throughput maximization, delay minimization, energy-efficiency, and virtualized resource allocation. The papers that have joint objectives are classified under their main optimization focus of that paper. The list of the selected papers are provided in "FiWi Resource Allocation Papers Selected for Review.xlsx" file. Our target in this review is to understand the recent optimization techniques used in resource allocation for converged optical fronthaul and radio mmWave access network implementations, therefore we focused our search to the works completed in the last five years (between 2016 and 2021), and approximately 95% of the selected papers fit under this category. Overview of the data collected from selected papers In this section, we provide answers to the three following questions with the data collected from the eligible studies: <ul> <li>Question 1: Which algorithms are used more often in performance optimization in converged mmWave networks?</li> <li>Question 2: Which performance metrics are determined to show that the optimization method achieves the objective?</li> <li>Question 3: Which criteria are used to evaluate the solution method?</li> </ul> Regarding the first question, the figure titled "Distribution of Optimization Algorithms in Selected Papers" shows the distribution of the optimization algorithms used by the selected papers. The distribution of the main performance metrics according to the resource optimization objectives is given in Table 1 (Distribution of Evaluation Criteria) and the evaluation criteria to test the performances of the selected papers are grouped in Table 2 (Distribution of Main Performance Metrics Depending on Optimization Objectives), which shows how many times each criterion is used together with how many of the resource management objectives use these criterion. References:  [1] Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.;Brennan, S.E.;  Chou, R.;  Glanville, J.;  Grimshaw, J.M.;  Hróbjartsson, A.;  Lalu, M.M.;  Li, T.;  Loder, E.W.;  Mayo-Wilson, E.;McDonald, S.; McGuinness, L.A.; Stewart, L.A.; Thomas, J.; Tricco, A.C.; Welch, V.A.; Whiting, P.; Moher, D. The PRISMA 2020statement: an updated guideline for reporting systematic reviews.Systematic Reviews2021,10.  doi:10.1186/s13643-021-01626-4.

opencc-by-4.0Jun 2024View details →

Datasets for input and output of INFORM Severity-based SMAA study of resource allocation in humanitarian aid and disaster management under climatic losses and damages

The landscape of climate change and extreme events will remain a wicked problem for equitable and forward-looking resource prioritisation. The question of how to couple climate and multi-risk information remains. IPCC has considered that multi-criteria decision analysis (MCDA) can help. We use stochastic multi-attribute analysis (SMAA), a variant of MCDA, to compute prioritisations of climatic losses & damages (l&d) for fragile countries with a humanitarian response plan. SMAA is combined with the INFORM Severity index, measuring the status of crises and disasters, and preferences gathered from stakeholders (e.g., United Nations, European Union, World Bank, the research and public sector, civil society). <ul> <li>Dataset S1. XLS-file with all the input data compiled from sources, concurrent data manipulation, and descriptions of steps taken until ready for the SMAA.</li> <li>Dataset S2. XLS-file with results of the SMAA for all weight schemes and concurrent analysis, such as sensitivity heat mapping, correlations, regressions, and Tukey mean-difference plot.</li> </ul>

Figure 4 in Herbicide-resistance management: a common pool resource problem?

Figure 4. Attributes of resource users associated with cooperative behavior and self-governance. Adapted from Schlager (2004, 152).

Figure 3 in Herbicide-resistance management: a common pool resource problem?

Figure 3. Attributes of common pool resources associated with cooperative behavior and self-governance. Adapted from Schlager (2004, 151–152).

Figure 1 in Herbicide-resistance management: a common pool resource problem?

Figure 1. Diagram of pesticide resistance as common property resource based on Miranowski and Carlson (1986). In this conceptualization, the common property resource is pest susceptibility, which is composed of a stock variable and a flow variable. Pest resistance is initially a renewable resource but becomes depleted over time through repeated use of chemicals. Thus, the actions of certain individuals may deplete the resource stock for others.

Figure 2 in Herbicide-resistance management: a common pool resource problem?

Figure 2. This diagram conceptualizes herbicide resistance as a common pool resource problem. Importantly, two conjoined common pool resources—herbicides and the weed gene pool—make up this resource system. Following common pool resource theory, this diagram illustrates the interconnectedness of four stock variables: (1) supply of a herbicide; (2) supply of a weed gene pool susceptible to a herbicide; (3) supply of a weed gene pool resistant to a herbicide; and (4) supply of herbicide efficacy on a weed gene pool. We have also diagramed corresponding flow variables or resource units (RU). In a generalized way, the use of a herbicide application (F1) influences the weed gene pool. However, the weed gene pool (S2 and S3) also acts independently of herbicide use and is influenced by both biological dynamics and social dynamics. Importantly, dynamics involving the weed gene pool are complex and include spatial and temporal variability in both the plant population and weed seedbank. The characteristics of the weed gene pool (S2 and S3) then affect the efficacy of the herbicide (S4) and whether its effectiveness is renewable or whether it becomes a finite stock resource. The quality of the herbicide (S4) may ultimately affect the supply of the herbicide (S1), if declining efficacy takes away from the herbicide's economic and chemical utility. In particular, the quality of these two common pool resources and not simply the quantity makes it a very complex resource arrangement. Factors adding complexity include that the weed gene pool is simultaneously both a pest and a resource. Furthermore, when the weed gene pool is characterized as a resource (its susceptibility to herbicides), the quality of this resource depends primarily upon provisioning practices of the common pool resource that keep the quality intact.In other words, following resource practices that do not allow internal or external resistance into the gene pool is key to maintaining its quality. The lack of quality from underprovisioning may result in a finite stock supply of the resource (i.e., weed gene pool susceptible to herbicides).Overappropriation (i.e., quantity or overharvesting of the resource) is a concern,in that it can be connected to poor provisioning practices.Aside from using a resource unit of herbicide in an application, the resource user does not directly appropriate or harvest from the system. This schematic only covers a generalized scenario, and more finescale analysis is needed to tease apart the complex relationships existing among herbicides and the weed gene pool.

opencc-by-4.0Dec 2015View details →

Fig. 1 in Reducing mowing frequency increases floral resource and butterfly (Lepidoptera: Hesperioidea and Papilionoidea) abundance in managed roadside margins

Fig. 1. Effects of mowing treatment (no mowing, mowing every 6 wk, and mowing every 3 wk) on butterfly abundance and mortality. (a) Live butterflies were counted every other week and summed for the section replicates of each mowing treatment. (b) Dead butterflies were counted weekly and summed for the section replicates of each mowing treatment. (c) The relative butterfly mortalities were calculated every 3 wk as ΣDead / (ΣDead + ΣLive) for the section replicates of each mowing treatment. The gray box on each x-axis indicates when the interrupted 6 wk treatment (6* wk) was added due a mowing error in the 6 wk treatment sections in Site 2. The 6* wk treatment was split from the 6 wk treatment for the whole time period in all sites for longitudinal reasons, i.e., to avoid an unnatural drop in the 6 wk treatment afer the mowing error. The 6* wk treatment was split afer the mowing error in Site 2 in the statistical analysis. Black vertical lines represent the knots that separated the data into spline sections for the statistical analyses.

opencc-by-4.0Jan 2018View details →

Database of Pines from the Forests paper: "Intraspecific Variation in Pines from the Trans-Mexican Volcanic Belt Grown Under Two Watering Regimes: Implications for Management of Genetic Resources"

Raw data from the Forests paper: "Intraspecific Variation in Pines from the Trans-Mexican Volcanic Belt Grown under Two Watering Regimes: Implications for Management of Genetic Resources" Forests 2018 9(2), 71. doi:<a href="http://dx.doi.org/10.3390/f9020071">10.3390/f9020071. </a> The database correspond to seedlings of four Mexican pines: P. oocarpa, P. patula and P. pseudostrobus, that were submitted to two watering treatments: Field Capacity (FC) and Drought-Stress (DS), during 90 days. Growth and biomass, survival and ontogenetic score were measured.

opencc-by-4.0Oct 2022View details →

Developing circularity, renewability and efficiency indicators for sustainable resource management : propanol production as a showcase

The data used for the exergy calculations in the associated article.

publicMar 2024View details →

Data from: Seasonal bee communities vary in their responses to resources at local and landscape scales: Implication for land managers

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