Fig 1.
Integration of models and analyses for land-sea planning.
Dashed squares represent the models (or broad stages of our method) used to prioritise catchment management to achieve downstream (marine) and local (terrestrial) management objectives. Black boxes depict key outputs of models, as well as derived and integrated outputs resulting from further analyses. Numbers indicate the overall sequence of modelling/analysis and arrows show how outputs are integrated in later stages. Abbreviated question numbers in parenthesis (Q1 to Q3) on the right-hand side of the diagram indicate the final outputs used to answer our three research questions.
Fig 2.
Study area: selected catchments draining into the Gulf of California, Mexico.
A) General location of study area and major rivers; B) Catchments, sub-catchments (planning units), and levels of conservation priority of adjacent marine management units; C) Estimated original extent of native vegetation types and largest catchments in the study area; and D) Current (2000) extent of vegetation types and anthropic land uses (cropland, pasture, and urban), reflecting the degree of human modification and underlying the spatial patterns of pollutant supply.
Fig 3.
Probability of change from native vegetation classes to anthropic land uses.
Maps depict the probability of change from the eight native vegetation classes to A) cropland (Pcrops), B) pasture (Ppasture), and C) urban areas (Purban) for a 20-year period (2000 to 2020), estimated using the land-use change model; also represented are the levels of conservation priority of adjacent marine management units (see legend in Fig 2B).
Fig 4.
Estimated maximum reduction in the supply of TSS and DIN from anthropic land uses after implementing hypothetical best-practice management.
Maps show the maximum reduction (kg/year), at sub-catchment level, in total suspended sediment (TSS) supply for A) cropland and B) pasture; and in dissolved inorganic nitrogen (DIN) for C) cropland and D) urban areas. Only sub-catchments including anthropic land uses were targeted for management in the prioritisation analyses based on best-practice management scenarios.
Table 1.
Nutrient event mean concentrations (EMC) and cover factor (C-factor) values (for DIN and TSS, respectively) used for catchment modelling.
The first set of values corresponds to the original EMC (mg/L) and C-factor (non-dimensional parameter) values used to model current and maximum supply scenarios. Numbers in parentheses and bold are the modified parameters used to simulate TSS and DIN reductions resulting from implementing best-practice management. We classified cropland areas based on their relative use of fertiliser, from very low to very high [81] and used this classification to progressively assign EMC values for each class (lowest to highest) using the 50th, 60th, 70th, 80th, and 90th percentiles of documented values for cropland (see S1 Text).
Fig 5.
Maximum proportional contribution (percentage) of sub-catchments to the region-wide TSS load, considering the potential conversion of native vegetation to anthropic areas.
The map integrates the individual maps depicting the potential supply of total suspended sediment (TSS) if natural vegetated areas are converted into anthropic land uses (i.e., cropland, pasture, or urban areas), each previously multiplied by the probability of change from native vegetation to the corresponding anthropic land use (Fig 3); also represented are the levels of conservation priority of adjacent marine management units (see legend in Fig 2B).
Fig 6.
Change factors of the thirty-nine catchments in our study area.
Values for change factor (CF) were allocated according to the estimated change in (A) total suspended sediment (TSS) and (B) dissolved inorganic nitrogen (DIN) loads from the ‘natural’ (Fig 2C) to the ‘current’ (Fig 2D) vegetation/land use conditions. Also represented are the levels of conservation priority of adjacent marine management units (see legend in Fig 2B).
Table 2.
Ranges of proportional change in catchment loads of pollutants (DIN and TSS) from ‘natural’ to ‘current’ land-use conditions used to calculate the change factor (CF).
We arbitrarily applied progressively larger CF values, from 0.5 (effectively reducing the importance of the catchment by half) when the estimated increase in loads of DIN or TSS was relatively low (<25%) to 1.0 (maximum importance) for catchments with very large increases in estimated loads of pollutants (>1,000% for DIN and >350% for TSS). Due to high variability in the proportional change in pollutant loads between catchments and pollutants, we assigned different intervals for CF categories for TSS and DIN, in both cases based on a geometric increase, which fitted the distribution of our data.
Fig 7.
River-plume model and priority level of catchments for coastal-marine conservation.
A) River-plume model depicting potential impacts from land-based nutrient pollution [82] used as a proxy for the maximum extent of influence of rivers. B) Marine priority (MP) factor assigned to each catchment (and subsequently to the sub-catchments within them), representing their importance for marine conservation in terms of their link to marine management units of varying conservation priority (see legend in Fig 2B).
Table 3.
Summary of parameterisation for Marxan prioritisation scenarios.
For each scenario we used the area of planning units (sub-catchments) as cost (multiplied by 100). We assigned a baseline feature penalty factor (FPF) of 10. Clumping of planning units was not required given the size of sub-catchments and the types of management actions considered; thus we set the boundary length modifier (BLM) to zero. Each scenario was run 100 times, with 1,000,000 iterations each.
Fig 8.
Priorities for catchment management and protection.
Maps show the relative value or priority of sub-catchments for achieving two sets of marine objectives (improving or maintaining end-of-river water quality) and one set of terrestrial objectives (conservation of terrestrial vertebrates). The values of planning units are represented by the selection frequency maps of Marxan. Sub-catchments selected more frequently in Marxan runs (darker colours) indicate their higher importance for achieving objectives. Also shown in the maps are the Marxan best solutions for each scenario. A) S1: Priorities for management to improve coastal-marine water quality by implementing best-practice management in anthropic land uses (cropland, pasture, and urban); B) S2: Priorities to maintain water quality through protection of remnant native vegetation to minimise the increase in sediment loads delivered to coastal-marine areas; and C) S3: Priorities for conservation of terrestrial vertebrate species; also represented is the level of conservation priority of adjacent marine management units (see legend in Fig 2B).
Fig 9.
Spatial similarities and differences between priority maps for water quality protection and protection of terrestrial biodiversity.
A) Differences between the two maps calculated by subtracting the selection frequency map of S3 (terrestrial biodiversity) from that of S2 (water quality): red areas were preferentially selected for terrestrial biodiversity, while blue areas were selected more often for protection against erosion. Areas in beige and light red or light blue had comparable selection frequencies for the two scenarios (either both low or both high, but most of these areas depict areas of low selection frequencies in both scenarios). B) Areas of coincidence, measured as the number of times (out of 100 runs) units were selected in both scenarios; also represented is the level of conservation priority of adjacent marine management units (see legend in Fig 2B).