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opencc-by-4.0Jan 2024View details →

Figure 3. Critical stop lines for a sequential count plan for T. urticae. For a in Spatial distribution and sampling plan for Tetranychus urticae (Acari: Tetranychidae) in bean crops

Figure 3. Critical stop lines for a sequential count plan for T. urticae. For a precision level of 10 and 25%.

opencc-by-4.0Jan 2024View details →

Figure 2 in Spatial distribution and sampling plan for Tetranychus urticae (Acari: Tetranychidae) in bean crops

Figure 2. Sample sizes required to achieve a given precision level of 10 and 25% at different mean densities of T. urticae per leaf.

opencc-by-4.0Jan 2024View details →

Figure 1 in Spatial distribution and sampling plan for Tetranychus urticae (Acari: Tetranychidae) in bean crops

Figure 1. Relationship between variance and mean density (all stages combined per leaf) of T. urticae samples collected from bean fields near Varamin vicinity, Tehran province, Iran. The red lines are the best-fitting lines of Taylor's power law.

opencc-by-4.0Nov 2024View details →

Introduction to ecosystem-based marine spatial planning

The video introduces the ecosystem-based marine spatial planning concept. How this approach to management, considers human activities while ensuring marine conservation and protection.

opencc-by-4.0Nov 2024View details →

Ecosystem-based marine spatial planning assessment tool video tutorial

Aiming at promoting the capacity building of competent authorities, scientists and consultants, a novel tool is proposed for assessing the alignment of marine spatial planning processes with ecosystem-based approach principles and to guide its operationalization. The EB-MSP assessment tool is developed with the ambition of providing a fit-for-purpose tool to overcome the ecosystem-based marine spatial planning implementation barriers reported by experts and managers. The EB-MSP assessment tool is designed to apply to any spatial plan, regardless of its stage of development: assessment of existing plans, plans in progress; or different national plans in a transboundary region. The EB-MSP tool can be accessed at https://aztidata.es/EB-MSP The EB-MSP assessment tool leads users through a step-by-step procedure for evaluating a specific plan. Upon first access, the user must register. This way, the users can conduct multiple assessments in a single session or different sessions, and can retrieve the information from previous sessions. The evaluation can be conducted by documenting the actions adopted during the planning process stages, or by examining how ecosystem-based marine spatial planning cross-cutting topics, have been incorporated into the plan. The user has to provide information about 130 statements that reflect the actions or tasks adopted during the planning process. For each action, six complementary fields of information have to be provided by the user: <ol> <li>           the degree of implementation of the action; </li> <li>           the relevance of the action for the assessed planning site; </li> <li>          the main source of knowledge base supporting the action; </li> <li>          the respondent's confidence; </li> <li>          approaches, tools and methods implemented; and </li> <li>          justification of the responses</li> </ol> Once the assessment is performed, the results are displayed as dynamic graphs that can be downloaded. The responses are also stored in a table, which can be downloaded as an Excel spreadsheet.

opencc-by-4.0May 2024View details →

Urban Agriculture and Health: Insights from Anonymized Expert Interviews on Spatial Planning in Greater Lomé, Togo

Transcripts of anonymized interviews with 11 urban planning experts in Greater Lomé on the subject of urban agriculture, health and spatial planning.

Fig. 2 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 2: Flowchart of steps and methods followed (AHP: Analytic Hierarchy Process, FM: Fuzzy Membership).

Fig. 7 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 7: Spatial representation of the Fishing pressure index from the small scale coastal fishery (FPc).

opencc-zeroDec 2015View details →

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Data from: Abiotic proxies for predictive mapping of near-shore benthic assemblages: implications for marine spatial planning

Marine spatial planning (MSP) should assist managers in guiding human activities towards sustainable practices and in minimizing user-conflicts in our oceans. A necessary first step is to quantify spatial patterns of marine assemblages in order to understand the ecosystem's structure, function, and services. However, the large spatial scale, high economic value, and density of human activities in near-shore habitats often makes quantifying this component of marine ecosystems especially daunting. To address this challenge, we developed an assessment method that employs abiotic proxies to rapidly characterize marine assemblages in near-shore benthic environments with relatively high resolution. We evaluated this assessment method along 300 km of the State of Maine's coastal shelf (< 100m depth)—a zone where high densities of buoyed lobster traps typically preclude extensive surveys by towed sampling gear (i.e., otter trawls). During the summer months of 2010-2013, we implemented a stratified-random survey using a small remotely operated vehicle that allowed us to work around lobster buoys and to quantify all benthic megafauna to species. Stratifying by substrate, depth, and coastal water masses, we found that abiotic variables explained a significant portion of variance (37- 59%) in benthic species composition, diversity, biomass and economic value. Generally, the density, diversity, and biomass of assemblages significantly increased with the substrate complexity (i.e., from sand-mud to ledge). The diversity, biomass and economic value of assemblages also decreased significantly with increasing depth. Lastly demersal fish densities, sessile invertebrate densities, species diversity, and assemblage biomass increased from east to west, while the abundance of mobile invertebrates and economic value decreased, corresponding mainly to the contrasting water-mass characteristics of the Maine Coastal Current system (i.e., summertime current direction, speed, and temperature). Integrating modeled predictions with existing GIS layers for abiotic conditions allowed us to scale up important assemblage attributes to define key foundational ecological principles of MSP and to find priority regions where some bottom-disturbing activities would have minimal impact to benthic assemblages. We conclude that abiotic proxies can be strong forcing functions for the assembly of marine communities and therefore useful tools for spatial extrapolations of marine assemblages in congested (heavily used) near-shore habitats.

opencc-by-4.0Dec 2015View details →

Data for the paper "Addressing Uncertainties in Modelling Cumulative Impacts within Maritime Spatial Planning in the Adriatic and Ionian Region"

Data for the paper "Addressing Uncertainties in Modelling Cumulative Impacts within Maritime Spatial Planning in the Adriatic and Ionian Region."

Fig. 1 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 1: Map of the study area.

Fig. 6 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 6: Scatterplot matrices for Large (A) and Linear (B) FM functions.

Fig. 8 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 8: High values of the FP c index as estimated by Local Moran's I test.

Fig. 5 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 5: Spatial representation of coastal vessels activity indexes (Ac).

Fig. 4 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 4: Spatial representation of the Coastal fishery suitability index (Sc).

Fig. 3 in Multi-Criteria Decision Analysis as a tool to extract fishing footprints: application to small scale fisheries and implications for management in the context of the Maritime Spatial Planning Directive

Fig. 3: Spatial representation of the criteria ranking taken into account in MCDA.