Advancing Land-Sea Conservation Planning: Integrating Modelling of Catchments, Land-Use Change, and River Plumes to Prioritise Catchment Management and Protection

Human-induced changes to river loads of nutrients and sediments pose a significant threat to marine ecosystems. Ongoing land-use change can further increase these loads, and amplify the impacts of land-based threats on vulnerable marine ecosystems. Consequently, there is a need to assess these threats and prioritise actions to mitigate their impacts. A key question regarding prioritisation is whether actions in catchments to maintain coastal-marine water quality can be spatially congruent with actions for other management objectives, such as conserving terrestrial biodiversity. In selected catchments draining into the Gulf of California, Mexico, we employed Land Change Modeller to assess the vulnerability of areas with native vegetation to conversion into crops, pasture, and urban areas. We then used SedNet, a catchment modelling tool, to map the sources and estimate pollutant loads delivered to the Gulf by these catchments. Following these analyses, we used modelled river plumes to identify marine areas likely influenced by land-based pollutants. Finally, we prioritised areas for catchment management based on objectives for conservation of terrestrial biodiversity and objectives for water quality that recognised links between pollutant sources and affected marine areas. Our objectives for coastal-marine water quality were to reduce sediment and nutrient discharges from anthropic areas, and minimise future increases in coastal sedimentation and eutrophication. Our objectives for protection of terrestrial biodiversity covered species of vertebrates. We used Marxan, a conservation planning tool, to prioritise interventions and explore spatial differences in priorities for both objectives. Notable differences in the distributions of land values for terrestrial biodiversity and coastal-marine water quality indicated the likely need for trade-offs between catchment management objectives. However, there were priority areas that contributed to both sets of objectives. Our study demonstrates a practical approach to integrating models of catchments, land-use change, and river plumes with conservation planning software to inform prioritisation of catchment management.


Pasture (modified/induced grasslands):
We used the median value of vegetation identified as modified grasslands of the full dataset, except in cases when data was not available for a particular constituent or when values between native and modified/human-induced grasslands were minimal or inverted (i.e., higher for native vegetation conditions). Classes in the USA include pasture/hay, and for Australia, it also includes dairy cow pasture grazing and intensive grazing areas.
Forests: Due to difficulty in matching/cross-walking our land cover/vegetation classes to diverse forest classes reported in EMC studies and to high similarity and inconsistencies in the variation in EMC values between different types of forests for different constituents we decided to aggregate EMC values for all types of forests, including dry scrub/shrub and xeric vegetation. We thus assign gradually lower values, starting from temperate forests, down to xeric/desert environments, based on calculated percentiles (i.e., 90 th to 25 th ). Higher values correspond to woodlands (temperate coniferous and mixed forest), followed by rainforests (humid tropical forests), dry forests (thorn forests and bushland), and scrub/shrub (xeric desert and semidesert vegetation). We assigned the values of "rain forest" to "palm forest" due to its proximity and due to the minimal area that it occupies within the study region. Until we have more information specific for Mexico, this could be considered a working hypothesis to be tested using local experimental/observational data.
Although we would expect that erosion in areas with poor vegetation cover (e.g, scrub-shrub, xeric vegetation), would be important and therefore particulate nutrient concentrations would be higher, studies indicate that arid and xeric environments tend to export significantly less nutrients than temperate and rain forests (Perakis and Hedin 2002;Smith et al., 2003). Smith et al. (2003) estimated natural background concentrations for vegetation types that correspond fairly well with vegetation classes in our study region and showed that those corresponding to arid environments are significantly lower than those corresponding to temperate forests.

Other land cover/vegetation classes:
Information for EMC values for other land cover/vegetation classes (e.g., wetlands, coastal dunes) was very scarce and values very contrasting. Considering they represent a minor portion of the region and that expected nutrient runoff from these areas is very low, either because runoff is very low or because they act like reservoirs for nutrients from upstream sources, we set values to zero for these classes. In contrast, values for bare/exposed land (i.e., cleared areas, mostly for urban or agricultural areas) were set high based on available studies, mostly only form North American studies. Red numbers indicate the criteria to assign values were adjusted, e.g., when percentile values were the same for different classes.

References
Blue numbers indicate concentrations used to construct best-practice management scenarios.   Jennings (1996). Characterization of nonpoint sources and loadings to Corpus Christi Bay National Estuary Program Study Area. Austin, Texas, Texas Natural Resource Conservation Commission. Bartley & Spears (2010;; EMC median values (>90% indicated land use); Reference: Bartley R, Speirs W, Ellis T, Waters DK. 2011. A review of sediment and nutrient concentration data from Australia for use in catchment water quality models. Marine Pollution Bulletin .