A decision-support tool to facilitate discussion of no-take boundaries for Marine Protected Areas during stakeholder consultation processes

Plant species diversity assessment and monitoring in catchment areas of River Chenab, Punjab, Pakistan

Background

Biodiversity data is crucial for sustainable development and making decisions regarding natural resources and its conservation. The study goal was to use quantitative ecological approaches to determine the species richness and diversity of wild flora and the ultimate impact of environmental factors on vegetation dynamics.

Methods

Quadrats having sizes of 1×1 for herbs, 5×5 for shrubs, and 10×10 m² for trees were used. Various phytosociological characteristics were investigated in association with a wide variety of environmental variables. Soil analysis based on texture, moisture, pH, electrical conductivity (EC), organic matter (OM), available potassium (K), and phosphorus (P) were examined. The existing state of vegetation along the River Chenab was assessed using SWOT analysis and a future conservation strategy was devised.

Results

One hundred twenty different plant speies were divided into 51 families including 92 dicots, 17 monocots, 6 pteridophytes and 1 bryophyte species. Herbs accounted for 89 followed by shrubs (16 species) and trees (15 species). Correlation analysis revealed a highly positive correlation between relative density and relative frequency (0.956**). Shannon and Simpson’s diversity indices elaborated that site 3 and 7 with clay loamy soil had non-significant alpha diversity and varies from site to site. Diversity analysis showed that site 10 was most diverse (22.25) in terms of species richness. The principal coordinate analysis expressed that different environmental variables including OM, soil pH, P, K, and EC affect vegetation significantly, therefore, loamy soil showed presence and dispersal of more vegetation as compared to loam, sandy and sandy loam soils. Further, 170 ppm of available potassium had significant affect on plant diversity and distribution.

Conclusion

Asteraceae family was found dominant as dicot while poaceae among monocot. Adhatoda vasica was one of the unique species and found in Head Maralla site. For evenness, site 3 had maximum value 0.971. Most of the soil represented loamy soil texture where site 2 and 4 possess high soil moisture content. SWOT analysis revealed strengths as people prefered plants for medicine, food and economic purposes. In weakness, agricultural practices, soil erosion and flooding affected the vegetation. In opportunities, Forest and Irrigation Departments were planting plants for the restoration of ecosystem. Threats include anthropogenic activities overgrazing, urbanization and road infrastructure at Head Maralla, habitat fragmentation at Head Khanki, and extensive fish farming at Head Qadirabad. Future conservation efforts should be concentrated on SWOT analysis outcome in terms of stopping illegal consumption of natural resources, restoration of plant biodiversity through reforestation, designating protected areas and multiplying rare species locally.

Protected area effectiveness against land development in Spain

Land use-land cover (LULC) changes towards artificial covers are one of the main global threats to biodiversity conservation. In this comprehensive study, we tested a number of methodological and research hypotheses, and a new covariate control technique in order to address common protected area (PA) assessment issues and accurately assess whether different PA networks have had an effect at preventing development of artificial LULCs in Spain, a highly biodiverse country that has experienced massive socioeconomic transformations in the past two decades. We used digital census data for four PA networks designated between 1990 and 2000: Nature Reserves (NRs), Nature Parks (NPs), Sites of Community Importance (SCIs) and Special Protection Areas (SPAs). We analysed the effect of explanatory variables on the ecological effectiveness of protected polygons (PPs): Legislation stringency, cummulative legal designations, management, size, age and bio-physical characteristics. A multiple Before-After-Control-Impact (BACI) semi-experimental research design was used whereby artificial land cover increase (ALCI) and proportional artificial land cover increase (PALCI) results were compared inside and outside PAs, using 1 km and 5 km buffer areas surrounding PAs as controls. LULC data were retrieved from Corine Land Cover (CLC) 1990 and 2006 data. Results from three spatial-statistical models using progressively restrictive criteria to select control areas increasingly more accurate and similar to the assessed PPs were compared. PAs were a generally effective territorial policy to prevent land development in Spain. NRs were the most effective PA category, with no new artificial land covers in the assessed period, although exact causality could not be attributed due to legal overlaps. SPAs were the least effective category, with worse ALCI data than their control areas. Legal protection was effective against land development, which was influenced by most bio-physical variables. However, cumulative legal designations and PA management did not seem to influence land development. The spatial-statistical technique used to make cases and control environmentally similar did not produce consistent outcomes and should be refined.

Gbm.auto: A software tool to simplify spatial modelling and Marine Protected Area planning

Boosted Regression Trees. Excellent for data-poor spatial management but hard to use

Marine resource managers and scientists often advocate spatial approaches to manage data-poor species. Existing spatial prediction and management techniques are either insufficiently robust, struggle with sparse input data, or make suboptimal use of multiple explanatory variables. Boosted Regression Trees feature excellent performance and are well suited to modelling the distribution of data-limited species, but are extremely complicated and time-consuming to learn and use, hindering access for a wide potential user base and therefore limiting uptake and usage.

BRTs automated and simplified for accessible general use with rich feature set

We have built a software suite in R which integrates pre-existing functions with new tailor-made functions to automate the processing and predictive mapping of species abundance data: by automating and greatly simplifying Boosted Regression Tree spatial modelling, the gbm.auto R package suite makes this powerful statistical modelling technique more accessible to potential users in the ecological and modelling communities. The package and its documentation allow the user to generate maps of predicted abundance, visualise the representativeness of those abundance maps and to plot the relative influence of explanatory variables and their relationship to the response variables. Databases of the processed model objects and a report explaining all the steps taken within the model are also generated. The package includes a previously unavailable Decision Support Tool which combines estimated escapement biomass (the percentage of an exploited population which must be retained each year to conserve it) with the predicted abundance maps to generate maps showing the location and size of habitat that should be protected to conserve the target stocks (candidate MPAs), based on stakeholder priorities, such as the minimisation of fishing effort displacement.

Gbm.auto for management in various settings

By bridging the gap between advanced statistical methods for species distribution modelling and conservation science, management and policy, these tools can allow improved spatial abundance predictions, and therefore better management, decision-making, and conservation. Although this package was built to support spatial management of a data-limited marine elasmobranch fishery, it should be equally applicable to spatial abundance modelling, area protection, and stakeholder engagement in various scenarios.

Quantifying the squeezing or stretching of fisheries as they adapt to displacement by marine reserves

The designation of no-take marine reserves involves social and economic concerns due to the resulting displacement of fishing effort, when fishing rights are removed from those who traditionally fished within an area. Displacement can influence the functioning of the fishery and success of the reserve, yet levels of displacement are seldom quantified after reserve implementation and very rarely before that. We devised a simple analytical framework based on set theory to facilitate reserve placement. Implementation of the framework requires maps of fishing grounds, fishing effort, or catch per unit effort for at least 2 years. The framework quantifies the level of conflict that a reserve designation might cause in the fishing sector due to displacement and the opportunities to offset the conflict through fisher spatial mobility (i.e., ability of fishers to fish elsewhere). We also considered how the outputs of the framework can be used to identify targeted management interventions for each fishery. We applied the method in Honduras, where the largest marine protected area in Central America is being placed, for which spatial data on fishing effort were available for 6 fisheries over 3 years. The proposed closure had a greater negative impact on the shrimp and lobster scuba fisheries, which concentrated respectively 28% and 18% of their effort inside the reserve. These fisheries could not accommodate the displacement within existing fishing grounds. Both would be forced to stretch into new fishing grounds, which are available but are of unknown quality. These stakeholders will likely require compensation to offset costly exploratory fishing or to travel to fishing grounds farther away from port.