Incorporation of Spatial and Economic Analyses of Human‐Use Data in the Design of Marine Protected Areas

Abstract: Social, economic, and ecological criteria contribute to the successful design, implementation, and management of marine protected areas (MPAs). In the context of California's Marine Life Protection Act Initiative, we developed a set of methods for collecting, compiling, and analyzing data about the spatial extent and relative economic importance of commercial and recreational fishing. We interviewed 174 commercial fishers who represented the major fisheries in the initiative's north‐central coast region, which extends from Point Arena south to Pigeon Point. These fishers provided data that we used to map the extent of each of the fishing grounds, to weight the relative importance of areas within the grounds, to characterize the operating costs of each fishery, and to analyze the potential economic losses associated with proposed marine protected areas. A regional stakeholder group used the maps and impact analyses in conjunction with other data sets to iteratively identify economic and ecological trade‐offs in designations of different areas as MPAs at regional, port, and fishery extents. Their final proposed MPA network designated 20% of state waters as MPAs. Potential net economic loss ranged from 1.7% to 14.2% in the first round of network design and totaled 6.3% in the final round of design. This process is a case study in the application of spatial analysis to validate and integrate local stakeholder knowledge in marine planning.     

Population dynamics of the barnacle <Emphasis Type="Italic">Chthamalus montagui</Emphasis> at two spatial and temporal scales in northern Spain

This study describes spatial and temporal patterns of variability in population parameters in the barnacle Chthamalus montagui Southward in three localities of northern Spain and evaluates whether density-dependent settlement may regulate population dynamics. The sampling design considered two spatial scales, localities and sites within localities, and two temporal scales, years and six month intervals. Density, amount of free space, mortality, growth rate and magnitude of settlement (both absolute and per unit of free space) were obtained from photographs of permanent quadrats and from direct counts in the field. The number of settlers in scraped and untouched quadrats was used to estimate the importance of the presence of conspecifics in settlement. Significant variation at the two spatial and temporal scales was found for most parameters. Large spatial and temporal variations in adult mortality rate, density, and settlement were observed. Patterns of mortality were not consistent with differences in density among localities. Differences in settlement among localities were maintained through time. We suggest that magnitude of settlement is regulated by persistent features such as topography or local water circulation. We assume that early post-settlement mortality does not differ among localities. In the absence of differential mortality, settlement determines average population density within localities. Within localities, settlement was independent of density and free space. No consistent evidence was found on preferential settlement at the vicinity of conspecifics. The main conclusion is that density-dependent settlement is not relevant for the regulation of the populations of C. montagui in the northern Spain. Regulation might occur by density-dependent processes within the adult fraction of the population and/or the larval phase before settlement.     

Functional response of sport divers to lobsters with application to fisheries management.

Fishery managers must understand the dynamics of fishers and their prey to successfully predict the outcome of management actions. We measured the impact of a two-day exclusively recreational fishery on Caribbean spiny lobster in the Florida Keys, USA, over large spatial scales (>100 km) and multiple years and used a theoretical, predator-prey functional response approach to identify whether or not sport diver catch rates were density-independent (type I) or density-dependent (type II or III functional response), and if catch rates were saturated (i.e., reached an asymptote) at relatively high lobster densities. We then describe how this predator-prey framework can be applied to fisheries management for spiny lobster and other species. In the lower Keys, divers exhibited a type-I functional response, whereby they removed a constant and relatively high proportion of lobsters (0.74-0.84) across all pre-fishing-season lobster densities. Diver fishing effort increased in a linear manner with lobster prey densities, as would be expected with a type-I functional response, and was an order of magnitude lower in the upper Keys than lower Keys. There were numerous instances in the upper Keys where the density of lobsters actually increased from before to after the fishing season, suggesting some type of "spill-in effect" from surrounding diver-disturbed areas. With the exception of isolated reefs in the upper Keys, the proportion of lobsters removed by divers was density independent (type-I functional response) and never reached saturation at natural lobster densities. Thus, recreational divers have a relatively simple predatory response to spiny lobster, whereby catch rates increase linearly with lobster density such that catch is a reliable indicator of abundance. Although diver predation is extremely high (approximately 80%), diver predation pressure is not expected to increase proportionally with a decline in lobster density (i.e., a depensatory response), which could exacerbate local extinction. Furthermore, management actions that reduce diver effort should have a concomitant and desired reduction in catch. The recreational diver-lobster predator-prey construct in this study provides a useful predictive framework to apply to both recreational and commercial fisheries, and on which to build as management actions are implemented.     

Use of a seagrass residency index to apportion commercial fishery landing values and recreation fisheries expenditure to seagrass habitat service

Where they dominate coastlines, seagrass beds are thought to have a fundamental role in maintaining populations of exploited species. Thus, Mediterranean seagrass beds are afforded protection, yet no attempt to determine the contribution of these areas to both commercial fisheries landings and recreational fisheries expenditure has been made. There is evidence that seagrass extent continues to decline, but there is little understanding of the potential impacts of this decline. We used a seagrass residency index, that was trait and evidence based, to estimate the proportion of Mediterranean commercial fishery landings values and recreation fisheries total expenditure that can be attributed to seagrass during different life stages. The index was calculated as a weighted sum of the averages of the estimated residence time in seagrass (compared with other habitats) at each life stage of the fishery species found in seagrass. Seagrass‐associated species were estimated to contribute 30%–40% to the value of commercial fisheries landings and approximately 29% to recreational fisheries expenditure. These species predominantly rely on seagrass to survive juvenile stages. Seagrass beds had an estimated direct annual contribution during residency of €58–91 million (4% of commercial landing values) and €112 million (6% of recreation expenditure) to commercial and recreational fisheries, respectively, despite covering <2% of the area. These results suggest there is a clear cost of seagrass degradation associated with ineffective management of seagrass beds and that policy to manage both fisheries and seagrass beds should take into account the socioeconomic implications of seagrass loss to recreational and commercial fisheries.     

Spatial and temporal variability modify density dependence in populations of large herbivores

A central challenge in ecology is to understand the interplay of internal and external controls on the growth of populations. We examined the effects of temporal variation in weather and spatial variation in vegetation on the strength of density dependence in populations of large herbivores. We fit three subsets of the model ln(Nt) = a + (1 + b) x ln(N(t-1)) + c x ln(N(t-2)) to five time series of estimates (Nt) of abundance of ungulates in the Rocky Mountains, USA. The strength of density dependence was estimated by the magnitude of the coefficient b. We regressed the estimates of b on indices of temporal heterogeneity in weather and spatial heterogeneity in resources. The 95% posterior intervals of the slopes of these regressions showed that temporal heterogeneity strengthened density-dependent feedbacks to population growth, whereas spatial heterogeneity weakened them. This finding offers the first empirical evidence that density dependence responds in different ways to spatial heterogeneity and temporal heterogeneity.     

Assessing and testing prediction uncertainty for single tree-based models: A case study applied to northern hardwood stands in southern Québec, Canada

Estimating prediction uncertainty for a single tree-based model is hindered by the complex structure of these models. In this paper, we addressed this issue with a case study applied to northern hardwood stands in Québec, Canada. SaMARE is a stochastic single tree-based model that was designed for these types of stands. Using a Monte Carlo approach, the model can provide a mean predicted value and its confidence limits for some plot-level attributes.The mean predicted values were compared to observed values in terms of bias and accuracy. In addition to these common statistics, we compared nominal coverage of Monte Carlo-simulated confidence intervals with real (observed) coverage to verify the adequacy of the simulated uncertainty. A comparison was made using several plot-level attributes, which exhibited an increasing discriminative complexity. This complexity ranges from coarse attributes, such as all-species basal area, up to more complex ones, such as basal area for stems of a particular species and with sawlog potential.The results showed that in terms of absolute value, biases were small, but could be relatively high with respect to the average observed value when the discriminative complexity of the attribute increased. The comparison between nominal and real coverage of confidence intervals gave satisfactory results for all-species plot-level attributes. However, for some species-specific attributes, the Monte Carlo-simulated confidence intervals overestimated the real coverage.     

Predicting arsenic concentrations in groundwater of San Luis Valley,Colorado: implications for individual-level lifetime exposure assessment

Consumption of inorganic arsenic in drinking water at high levels has been associated with chronic diseases. Risk is less clear at lower levels of arsenic, in part due to difficulties in estimating exposure. Herein we characterize spatial and temporal variability of arsenic concentrations and develop models for predicting aquifer arsenic concentrations in the San Luis Valley, Colorado, an area of moderately elevated arsenic in groundwater. This study included historical water samples with total arsenic concentrations from 595 unique well locations. A longitudinal analysis established temporal stability in arsenic levels in individual wells. The mean arsenic levels for a random sample of 535 wells were incorporated into five kriging models to predict groundwater arsenic concentrations at any point in time. A separate validation dataset (n = 60 wells) was used to identify the model with strongest predictability. Findings indicate that arsenic concentrations are temporally stable (r = 0.88; 95 % CI 0.83–0.92 for samples collected from the same well 15–25 years apart) and the spatial model created using ordinary kriging best predicted arsenic concentrations (ρ = 0.72 between predicted and observed validation data). These findings illustrate the value of geostatistical modeling of arsenic and suggest the San Luis Valley is a good region for conducting epidemiologic studies of groundwater metals because of the ability to accurately predict variation in groundwater arsenic concentrations.     

Bayesian Decision‐Network Modeling of Multiple Stakeholders for Reef Ecosystem Restoration in the Coral Triangle

Proposals for marine conservation measures have proliferated in the last 2 decades due to increased reports of fishery declines and interest in conservation. Fishers and fisheries managers have often disagreed strongly when discussing controls on fisheries. In such situations, ecosystem‐based models and fisheries‐stock assessment models can help resolve disagreements by highlighting the trade‐offs that would be made under alternative management scenarios. We extended the analytical framework for modeling such trade‐offs by including additional stakeholders whose livelihoods and the value they place on conservation depend on the condition of the marine ecosystem. To do so, we used Bayesian decision‐network models (BDNs) in a case study of an Indonesian coral reef fishery. Our model included interests of the fishers and fishery managers; individuals in the tourism industry; conservation interests of the state, nongovernmental organizations, and the local public; and uncertainties in ecosystem status, projections of fisheries revenues, tourism growth, and levels of interest in conservation. We calculated the total utility (i.e., value) of a range of restoration scenarios. Restricting net fisheries and live‐fish fisheries appeared to be the best compromise solutions under several combinations of settings of modeled variables. Results of our case study highlight the implications of alternate formulations for coral reef stakeholder utility functions and discount rates for the calculation of the net benefits of alternative fisheries management options. This case study may also serve as a useful example for other decision analyses with multiple stakeholders. Modelo de Red de Decisión Bayesiana de Múltiples Actores Interesados en la Restauración de Ecosistemas de Arrecife en el Triángulo de Coral     

Modeling spawning dates of Hucho taimen in Mongolia to establish fishery management zones.

The ecological impacts of recreational fisheries are of growing concern and pose a number of unique management challenges. Here we report on our efforts to provide guidance for managing a recreational fishery for taimen, the giant Eurasian trout (Hucho taimen) in Mongolia. This species has declined dramatically across its range of Siberia and Central Asia, and is currently listed as endangered in Mongolia. Strong populations persist in remote regions of Mongolia because of limited anthropogenic impacts and harvest, though interest in the fishery is expanding rapidly. Current fishing regulations list the spring "opening date" for taimen fishing as 15 June, although regulations have not been consistently enforced, partially because taimen spawn much earlier than 15 June in much of the country. Through a combination of statistical models, climate data, knowledge of taimen biology, and geographic information systems (GIS), we model taimen spawning dates for potential habitat in Mongolia. A parametric bootstrap procedure was used to simulate variability in spawning date derived from inter-annual climate variability and model error, from which we estimated the date in which taimen spawning is predicted to occur with 90% confidence. We recommend the designation of three fisheries management zones, with corresponding opening dates of 20 May, 1 June, and 15 June. Our fishery opening date recommendations are less restrictive than existing regulations. Provided there is little or no catch-and-release fishing mortality, this approach serves both environmental and human needs by protecting taimen during the reproductive period, while still allowing a post-spawning catch-and-release fishery that benefits local economies and generates revenue (through fishing concession fees) for local conservation efforts.     

Evaluation of variance approximation techniques for non-linear photosynthesis—irradiance models

Parameters derived from photosynthesis-irradiance (P-I) models, although often empirical in nature, are useful indicators of the photoadaptive state of phytoplankton in culture and in situ. However objective criteria for determining significant changes in P-I curves are rarely provided, because confidence intervals for parameters of non-linear models are not estimated easily. Examination of least-squares residuals in parameter space and Monte Carlo approaches have been used to estimate confidence regions around parameter values, but the computationally intensive nature of these methods has prevented their routine application. We present an alternative method of estimating confidence intervals for parameters of P-I curves that runs quickly on a microcomputer and is easily combined with common parameter-estimation routines. This algorithm was tested using a 3-parameter P-I model and curves describing a wide range of photoadaptive states, with different numbers of observations and different amounts of inherent variability. The method produced results comparable to the Monte Carlo technique. This analysis makes it possible to specify the sample size required to define parameters with acceptable confidence as a function of data variance and photoadaptive state. In most reasonable situations, 25 observations are sufficient.     

Photographic mark-recapture analysis of clustered mammal-eating killer whales around the Aleutian Islands and Gulf of Alaska

We used photographic mark-recapture methods to estimate the number of mammal-eating “transient” killer whales using the coastal waters from the central Gulf of Alaska to the central Aleutian Islands, around breeding rookeries of endangered Steller sea lions. We identified 154 individual killer whales from 6,489 photographs collected between July 2001 and August 2003. A Bayesian mixture model estimated seven distinct clusters (95% probability interval = 7–10) of individuals that were differentially covered by 14 boat-based surveys exhibiting varying degrees of association in space and time. Markov Chain Monte Carlo methods were used to sample identification probabilities across the distribution of clusters to estimate a total of 345 identified and undetected whales (95% probability interval = 255–487). Estimates of covariance between surveys, in terms of their coverage of these clusters, indicated spatial population structure and seasonal movements from these near-shore waters, suggesting spatial and temporal variation in the predation pressure on coastal marine mammals.     

Single and multispecies systems: The the Eastern Tropical Atlantic

The commercial tuna fishery in the Eastern Tropical Atlantic is based on harvests from three species of tuna: Yellowfin, Skipjack, and Bigeye. Two models are developed to examine the bioeconomics of this fishery. In the first model, species are presumed ecologically independent and selectively harvested. In the second model, two of the species (Yellowfin and Skipjack) are assumed to be interspecific competitors and jointly harvested independent of the third species (Bigeye). Data on landings, effort, cost, and revenue are assembled allowing estimates of the bioeconomic parameters for the model where all species are independent and selectively harvested. Open access and bioeconomic equilibria are identified for a combination of cost and discount rates. The pristine, open access, and bioeconomic equilibria are also determined for the multispecies model based on parameters from the single species (independent) models and plausible values for interaction and joint production coefficients. Equilibria are compared, and management policies from a single- and multispecies perspective are explored.     

3种氯酚化合物对大型溞的联合毒性

氯酚类化合物是我国水体中广泛存在的一类优先控制污染物,以大型溞(Daphnia magna)为试验生物,测定了2,4-二氯酚、2,4,6-三氯酚和五氯酚对大型溞的48 h致死的单一毒性和联合毒性.基于单一氯酚化合物的浓度-效应曲线,运用浓度加和(CA)与独立作用(IA)2个模型对2种等毒性浓度比的混合物(Mix-LC5...     

Modeling the effect of photosynthetic vegetation properties on the NDVI--LAI relationship

Developing a relationship between the normalized difference vegetation index (NDVI) and the leaf area index (LAI) is essential to describe the pattern of spatial or temporal variation in LAI that controls carbon, water, and energy exchange in many ecosystem process models. Photosynthetic vegetation (PV) properties can affect the estimation of LAI, but no models integrate the effects of multiple species. We developed four alternative NDVI-LAI models, three of which integrate PV effects: no PV effects, leaf-level effects, canopy-level effects, and effects at both levels. The models were fit to data across the natural range of variation in NDVI for a widespread High Arctic ecosystem. The weight of evidence supported the canopy-level model (Akaike weight, wr = 0.98), which includes species-specific canopy coefficients that primarily scale fractional PV cover to LAI by accounting for the area of unexposed PV. Modeling the canopy-level effects improved prediction of LAI (R2 = 0.82) over the model with no PV effect (R2 = 0.71) across the natural range of variation in NDVI but did not affect the site-level estimate of LAI. Satellite-based methods to estimate species composition, a variable in the model, will need to be developed. We expect that including the effects of PV properties in NDVI-LAI models will improve prediction of LAI where species composition varies across space or changes over time.     

Striking a Balance between Biodiversity Conservation and Socioeconomic Viability in the Design of Marine Protected Areas

Abstract: The establishment of marine protected areas is often viewed as a conflict between conservation and fishing. We considered consumptive and nonconsumptive interests of multiple stakeholders (i.e., fishers, scuba divers, conservationists, managers, scientists) in the systematic design of a network of marine protected areas along California's central coast in the context of the Marine Life Protection Act Initiative. With advice from managers, administrators, and scientists, a representative group of stakeholders defined biodiversity conservation and socioeconomic goals that accommodated social needs and conserved marine ecosystems, consistent with legal requirements. To satisfy biodiversity goals, we targeted 11 marine habitats across 5 depth zones, areas of high species diversity, and areas containing species of special status. We minimized adverse socioeconomic impacts by minimizing negative effects on fishers. We included fine‐scale fishing data from the recreational and commercial fishing sectors across 24 fisheries. Protected areas designed with consideration of commercial and recreational fisheries reduced potential impact to the fisheries approximately 21% more than protected areas designed without consideration of fishing effort and resulted in a small increase in the total area protected (approximately 3.4%). We incorporated confidential fishing data without revealing the identity of specific fisheries or individual fishing grounds. We sited a portion of the protected areas near land parks, marine laboratories, and scientific monitoring sites to address nonconsumptive socioeconomic goals. Our results show that a stakeholder‐driven design process can use systematic conservation‐planning methods to successfully produce options for network design that satisfy multiple conservation and socioeconomic objectives. Marine protected areas that incorporate multiple stakeholder interests without compromising biodiversity conservation goals are more likely to protect marine ecosystems.     

Promising the moon? Evaluation of indigenous and lunar fishing calendars using semiparametric generalized mixed models of recreational catch data

Previous studies that have investigated relationships between the lunar cycle and recreational fishing success have all suffered from various problems—most notably, the failure to account for potential confounders in a statistically rigorous manner. We propose methods to account for season, fisher identity, fishing effort, day, and variation in biomass, all of which have previously either been omitted or handled in an ad hoc way. These are applied to two sets of data on recreational fishing of the snapper Pagrus auratus in New Zealand. In addition to estimating effects due to lunar phase, we also implement these methods to analyse the performance of a lunar-based indigenous M ${{\bar{\mathrm{a}}}}$ ori fishing calendar. Recreational fishers in New Zealand often make use of such calendars in order to predict fishing success on specific days, however little is known about the performance of such predictions or whether they hold any practical use to the everyday angler. A relationship between lunar phase and fishing success is identified, as well as support for some aspects of the M ${{\bar{\mathrm{a}}}}$ ori fishing calendar predictions. The magnitudes of these effects are small, however, casting doubt on the practical relevance of lunar based fishing predictions. In addition to the known seasonal trend associated with annual migration, an unexpected second trend is detected, and postulated to be associated with intense local fishing pressure over the summer vacation period.     

Database-driven models of the world's Large Marine Ecosystems

We present a new methodology for database-driven ecosystem model generation and apply the methodology to the world's 66 currently defined Large Marine Ecosystems. The method relies on a large number of spatial and temporal databases, including FishBase, SeaLifeBase, as well as several other databases developed notably as part of the Sea Around Us project. The models are formulated using the freely available Ecopath with Ecosim (EwE) modeling approach and software. We tune the models by fitting to available time series data, but recognize that the models represent only a first-generation of database-driven ecosystem models. We use the models to obtain a first estimate of fish biomass in the world's LMEs. The biggest hurdles at present to further model development and validation are insufficient time series trend information, and data on spatial fishing effort.     

Comanagement Practices Enhance Fisheries in Marine Protected Areas

Abstract:  Fishing activities worldwide have dramatically affected marine fish stocks and ecosystems. Marine protected areas (MPAs) with no-take zones may enhance fisheries, but empirical evidence of this is scant. We conducted a 4-year survey of fish catches around and within an MPA that was previously fully closed to fishing and then partially reopened under regulated comanaged fishing. In collaboration with the fishers and the MPA authority, we set the fishing effort and selected the gear to limit fishing impact on key fish predators, juvenile fish stage, and benthic communities and habitats. Within an adaptive comanagement framework, fishers agreed to reduce fishing effort if symptoms of overfishing were detected. We analyzed the temporal trends of catch per unit of effort (CPUE) of the whole species assemblages and CPUE of the four most valuable and frequent species observed inside the opened buffer zone and outside the MPA investigated. After the comanaged opening, CPUE first declined and then stabilized at levels more than twice that of catches obtained outside the MPA. Our results suggest that working closely with fishers can result in greater fisheries catches. Partial protection of coastal areas together with adaptive comanagement involving fishers, scientists, and managers can effectively achieve conservation and fishery management goals and benefit fishing communities and alleviate overfishing.     

Detecting and Modeling Spatial and Temporal Dependence in Conservation Biology

Abstract: Due to the structuring forces and large-scale physical processes that shape our biosphere, we often find that environmental and ecological data are either spatially or temporally—or both spatially and temporally—dependent. When these data are analyzed, statistical techniques and models are frequently applied that were developed for independent data. We describe some of the detrimental consequences, such as inefficient parameter estimators, biased hypothesis test results, and inaccurate predictions, of ignoring spatial and temporal data dependencies, and we cite an example of adverse statistical results occurring when spatial dependencies were disregarded. We also discuss and recommend available techniques used to detect and model spatial and temporal dependence, including variograms, covariograms, autocorrelation and partial autocorrelation plots, geostatistical techniques, Gaussian autoregressive models, K functions, and ARIMA models, in environmental and ecological research to avoid the aforementioned difficulties.