Contrasting Global Trends in Marine Fishery Status Obtained from Catches and from Stock Assessments

Abstract: There are differences in perception of the status of fisheries around the world that may partly stem from how data on trends in catches over time have been used. On the basis of catch trends, it has been suggested that about 70% of all stocks are overexploited due to unsustainable harvesting and 30% of all stocks have collapsed to <10% of unfished levels. Catch trends also suggest that over time an increasing number of stocks will be overexploited and collapsed. We evaluated how use of catch data affects assessment of fisheries stock status. We analyzed simulated random catch data with no trend. We examined well‐studied stocks classified as collapsed on the basis of catch data to determine whether these stocks actually were collapsed. We also used stock assessments to compare stock status derived from catch data with status derived from biomass data. Status of stocks derived from catch trends was almost identical to what one would expect if catches were randomly generated with no trend. Most classifications of collapse assigned on the basis of catch data were due to taxonomic reclassification, regulatory changes in fisheries, and market changes. In our comparison of biomass data with catch trends, catch trends overestimated the percentage of overexploited and collapsed stocks. Although our biomass data were primarily from industrial fisheries in developed countries, the status of these stocks estimated from catch data was similar to the status of stocks in the rest of the world estimated from catch data. We conclude that at present 28–33% of all stocks are overexploited and 7–13% of all stocks are collapsed. Additionally, the proportion of fished stocks that are overexploited or collapsed has been fairly stable in recent years.     

Status of Marine Mammals in the United States

Abstract: The 1994 amendments to the U.S. Marine Mammal Protection Act required, for the first time, an assessment of the status of every marine mammal stock in the United States. We draw conclusions about the status of marine mammals from assessments of 153 stocks conducted to meet the requirements of these amendments. We found important regional differences in the status of stocks. Most stocks in the Atlantic and Pacific experience human-induced mortality (takes), primarily from gill-net fisheries. The proportion of stocks with takes was lower in the Gulf of Mexico and Hawaii, areas with few gill-net fisheries. Estimated takes exceeded removal limits for 29% of stocks in the Atlantic, 14% in the Pacific, 8% in Alaska, 7% in the Gulf of Mexico, and 0% in Hawaii. Twenty-eight stocks are listed as threatened or endangered under the U.S. Endangered Species Act. Most, but not all, baleen whale stocks are recovering after cessation of commercial harvests. Many species of pelagic cetaceans, including beaked and sperm whales, are vulnerable to mortality in pelagic drift-net fisheries. Most pinniped stocks experience takes, but none of these takes exceeds removal limits, and all pinniped stocks on mainland coasts of the United States are increasing in abundance. Quantitative data on trends in abundance are available for few cetacean stocks, emphasizing the difficulty of monitoring trends in these species. These stock assessments have greatly advanced our understanding of the status of marine mammals in the United States, but information gaps remain, particularly regarding stock structure and possible mortality in unmonitored fisheries.     

Prioritizing Pacific Salmon Stocks for Conservation

Over 300 native stocks of Pacific salmon, steelhead, and coastal cutthroat trout (Oncorhynchus spp.) are at risk of extinction in the Pacific Northwest. With only limited resources available for conservation and recovery, prioritization of these stocks may become necessary if meaningful measures are to be implemented. We propose criteria by which prioritization may be guided. First, we rank stocks for risk of extinction, either by population viability analysis or by a set of surrogate measures. Then we rank stocks for biological consequences of extinction, using sets of questions designed to establish the genetic and evolutionary consequences and the ecological consequences if a stock were to become extinct. Together, these rankings allow stocks to be prioritized for a range of possible actions, with those stocks at highest risk and bearing the greatest biological consequences of extinction receiving attention first. Application of the prioritization process to 20 Pacific anadromous salmonid stocks worked as intended, although data limitations are considerable. The process is most likely to work successfully when applied to many stocks on which data exist, when several experts carry out the prioritization, and when the results are peer reviewed.     

Fishing quota markets

In 1986, New Zealand responded to the open-access problem by establishing the world's largest individual transferable quota (ITQ) system. Using a 15-year panel dataset from New Zealand that covers 33 species and more than 150 markets for fishing quotas, we assess trends in market activity, price dispersion, and the fundamentals determining quota prices. We find that market activity is sufficiently high in the economically important markets and that price dispersion has decreased. We also find evidence of economically rational behavior through the relationship between quota lease and sale prices and fishing output and input prices, ecological variability, and market interest rates. Controlling for these factors, our results show a greater increase in quota prices for fish stocks that faced significant reductions, consistent with increased profitability due to rationalization. Overall, this suggests that these markets are operating reasonably well, implying that ITQs can be effective instruments for efficient fisheries management.     

Carbon stocks across a chronosequence of thinned and unmanaged red pine (Pinus resinosa) stands

Forests function as a major global C sink, and forest management strategies that maximize C stocks offer one possible means of mitigating the impacts of increasing anthropogenic CO2 emissions. We studied the effects of thinning, a common management technique in many forest types, on age-related trends in C stocks using a chronosequence of thinned and unmanaged red pine (Pinus resinosa) stands ranging from 9 to 306 years old. Live tree C stocks increased with age to a maximum near the middle of the chronosequence in unmanaged stands, and increased across the entire chronosequence in thinned stands. C in live understory vegetation and C in the mineral soil each declined rapidly with age in young stands but changed relatively little in middle-aged to older stands regardless of management. Forest floor C stocks increased with age in unmanaged stands, but forest floor C decreased with age after the onset of thinning around age 40 in thinned stands. Deadwood C was highly variable, but decreased with age in thinned stands. Total ecosystem C increased with stand age until approaching an asymptote around age 150. The increase in total ecosystem C was paralleled by an age-related increase in total aboveground C, but relatively little change in total belowground C. Thinning had surprisingly little impact on total ecosystem C stocks, but it did modestly alter age-related trends in total ecosystem C allocation between aboveground and belowground pools. In addition to characterizing the subtle differences in C dynamics between thinned and unmanaged stands, these results suggest that C accrual in red pine stands continues well beyond the 60-100 year management rotations typical for this system. Management plans that incorporate longer rotations and thinning in some stands could play an important role in maximizing C stocks in red pine forests while meeting other objectives including timber extraction, biodiversity conservation, restoration, and fuel reduction goals.     

Spatial surplus production modeling of Atlantic tunas and billfish

We formulate and simulation-test a spatial surplus production model that provides a basis with which to undertake multispecies, multi-area, stock assessment. Movement between areas is parameterized using a simple gravity model that includes a "residency" parameter that determines the degree of stock mixing among areas. The model is deliberately simple in order to (1) accommodate nontarget species that typically have fewer available data and (2) minimize computational demand to enable simulation evaluation of spatial management strategies. Using this model, we demonstrate that careful consideration of spatial catch and effort data can provide the basis for simple yet reliable spatial stock assessments. If simple spatial dynamics can be assumed, tagging data are not required to reliably estimate spatial distribution and movement. When applied to eight stocks of Atlantic tuna and billfish, the model tracks regional catch data relatively well by approximating local depletions and exchange among high-abundance areas. We use these results to investigate and discuss the implications of using spatially aggregated stock assessment for fisheries in which the distribution of both the population and fishing vary over time.     

Continuous plankton records: Persistence in time-series of annual means of abundance of zooplankton

Time-series of annual means of abundance of zooplankton of the north-east Atlantic Ocean and the North Sea, for the period 1948 to 1977, show considerable associations between successive years. The seasonal dynamics of the stocks appear to be consistent with at least a proportion of this being due to inherent persistence from year-to-year. Experiments with a simple model suggest that the observed properties of the time-series cannot be reproduced as a response to simple random forcing. The extent of trends and long wavelength variations can be simulated by introducing fairly extensive persistence into the perturbations, but this underestimates the extent of shorter wavelength variability in the observed time-series. The effect of persistence is to increase the proportion of trend and long wavelength variability in time-series of annual means, but stocks can respond to short wavelength perturbations provided these have a clearly defined frequency.     

Assessing aboveground tropical forest biomass using Google Earth canopy images

Reducing Emissions from Deforestation and Forest Degradation (REDD) in efforts to combat climate change requires participating countries to periodically assess their forest resources on a national scale. Such a process is particularly challenging in the tropics because of technical difficulties related to large aboveground forest biomass stocks, restricted availability of affordable, appropriate remote-sensing images, and a lack of accurate forest inventory data. In this paper, we apply the Fourier-based FOTO method of canopy texture analysis to Google Earth's very-high-resolution images of the wet evergreen forests in the Western Ghats of India in order to (1) assess the predictive power of the method on aboveground biomass of tropical forests, (2) test the merits of free Google Earth images relative to their native commercial IKONOS counterparts and (3) highlight further research needs for affordable, accurate regional aboveground biomass estimations. We used the FOTO method to ordinate Fourier spectra of 1436 square canopy images (125 x 125 m) with respect to a canopy grain texture gradient (i.e., a combination of size distribution and spatial pattern of tree crowns), benchmarked against virtual canopy scenes simulated from a set of known forest structure parameters and a 3-D light interception model. We then used 15 1-ha ground plots to demonstrate that both texture gradients provided by Google Earth and IKONOS images strongly correlated with field-observed stand structure parameters such as the density of large trees, total basal area, and aboveground biomass estimated from a regional allometric model. Our results highlight the great potential of the FOTO method applied to Google Earth data for biomass retrieval because the texture-biomass relationship is only subject to 15% relative error, on average, and does not show obvious saturation trends at large biomass values. We also provide the first reliable map of tropical forest aboveground biomass predicted from free Google Earth images.     

Trends in the exploitation of South Atlantic shark populations

Approximately 25% of globally reported shark catches occur in Atlantic pelagic longline fisheries. Strong declines in shark populations have been detected in the North Atlantic, whereas in the South Atlantic the situation is less clear, although fishing effort has been increasing in this region since the late 1970s. We synthesized information on shark catch rates (based on 871,177 sharks caught on 86,492 longline sets) for the major species caught by multiple fleets in the South Atlantic between 1979 and 2011. We complied records from fishing logbooks of fishing companies, fishers, and onboard observers that were supplied to Brazilian institutions. By using exploratory data analysis and literature sources, we identified 3 phases of exploitation in these data (Supporting Information). From 1979 to 1997 (phase A), 5 fleets (40 vessels) fished mainly for tunas. From 1998 to 2008 (phase B), 20 fleets (100 vessels) fished for tunas, swordfishes, and sharks. From 2008 to 2011 (phase C), 3 fleets (30 vessels) fished for multiple species, but restrictive measures were implemented. We used generalized linear models to standardize catch rates and identify trends in each of these phases. Shark catch rates increased from 1979 to 1997, when fishing effort was low, decreased from 1998 to 2008, when fishing effort increased substantially, and remained stable or increased from 2008 to 2011, when fishing effort was again low. Our results indicate that most shark populations affected by longlines in the South Atlantic are currently depleted, but these populations may recover if fishing effort is reduced accordingly. In this context, it is problematic that comprehensive data collection, monitoring, and management of these fisheries ceased after 2012. Concurrently with the fact that Brazil is newly identified by FAO among the largest (and in fastest expansion) shark sub‐products consumer market worldwide.     

Structural equation-based latent growth curve modeling of watershed attribute-regulated stream sensitivity to reduced acidic deposition

Little information is available about the effects of watershed attributes on long-term stream chemical responses to reduced acidic deposition. Long-term chemical data (1980-2006) for 21 stream sites from the Appalachian Mountain region and data of 8 watershed attributes of the corresponding watersheds were analyzed to study such effects. Latent growth curve modeling (LGM), a structural equation modeling (SEM) approach, was conducted to quantify stream sensitivity to reduced acidic deposition in the region, and to model the initial chemical conditions (intercepts) and changing rates (slopes) in three time periods: 1980s, 1990s, and 2000s. The modeled chemical trends were generally consistent with those trends which were detected by trend analyses in a previous study. Watershed attributes including area, mean elevation, percentage of developed land, percentage of grassland, percentages of shale and sandstone, percentage of barren land, and percentage of soil type Gilpin-Upshur-Vandalia (GUV) were found to affect stream sensitivity to the reduced acidic deposition with their importance in the respective order. The stream sensitivity in turn regulated streams’ chemical initial conditions and their changing rates in the studied watersheds. This innovative application of LGM for modeling long-term chemical trends advanced our understanding of the role of watershed attributes in regulating streams’ responses to reduced acidic deposition. This approach can be applied for evaluating streams’ responses to acidic deposition in other regions and, more broadly, for studying causal effects on long-term behaviors in other ecological and environmental science subjects.     

Identifying interactions among salmon populations from observed dynamics

A simple direct correlation analysis of individual counts between different populations often fails to characterize the true nature of population interactions; however, the most common data type available for population studies is count data, and one of the most important objectives in population and community ecology is to identify interactions among populations. Here, I examine the dynamics of the spawning abundance of fall-run chinook salmon spawning within the California Central Valley and the Klamath Basin, California, and the Columbia River Basin, Oregon. I analyzed multiple time series from each watershed using a multivariate time-series technique called maximum autocorrelation factor analysis. This technique was used for finding common underlying trends in escapement abundance within each watershed. These trends were further investigated to identify potential resource-mediated interactions among the three groups of salmon. Each group is affected by multiple trends that are likely to be affected by environmental factors. In addition, some of the trends are coherent with each other, and the differences in population dynamics originate from variations in the relative importance of these trends among the three watershed groups.     

Estimating species occurrence, abundance, and detection probability using zero-inflated distributions

Researchers have developed methods to account for imperfect detection of species with either occupancy (presence absence) or count data using replicated sampling. We show how these approaches can be combined to simultaneously estimate occurrence, abundance, and detection probability by specifying a zero-inflated distribution for abundance. This approach may be particularly appropriate when patterns of occurrence and abundance arise from distinct processes operating at differing spatial or temporal scales. We apply the model to two data sets: (1) previously published data for a species of duck, Anas platyrhynchos, and (2) data for a stream fish species, Etheostoma scotti. We show that in these cases, an incomplete-detection zero-inflated modeling approach yields a superior fit to the data than other models. We propose that zero-inflated abundance models accounting for incomplete detection be considered when replicate count data are available.     

Using ecosystem-services assessments to determine trade-offs in ecosystem-based management of marine mammals

The goal of ecosystem-based management (EBM) is to support a sustainable and holistic multisectored management approach, and is recognized in a number of international policy frameworks. However, it remains unknown how these goals should be linked to assessments and management plans for marine fauna, such as mammals and fish stocks. It appears particularly challenging to carry out trade-off analyses of various ocean uses without a framework that integrates knowledge of environmental, social, and economic benefits derived from nonstationary marine fauna. We argue this gap can be filled by applying a version of the ecosystem-service approach at the population level of marine fauna. To advance this idea, we used marine mammals as a case study to demonstrate what indicators could operationalize relevant assessments and deliver an evidence base for the presence of ecosystem services and disservices derived from marine mammals. We found indicators covering common ecosystem service categories feasible to apply; examples of indicator data are already available in the literature for several populations. We encourage further exploration of this approach for application to marina fauna and biodiversity management, with the caveat that conceptual tensions related to the use of the ecosystem service concept itself needs to be addressed to ensure acceptance by relevant stakeholders.     

Identifying fishing trip behaviour and estimating fishing effort from VMS data using Bayesian Hidden Markov Models

Recent advances in technologies have lead to a vast influx of data on movements, based on discrete recorded position of animals or fishing boats, opening new horizons for future analyses. However, most of the potential interest of tracking data depends on the ability to develop suitable modelling strategies to analyze trajectories from discrete recorded positions. A serious modelling challenge is to infer the evolution of the true position and the associated spatio-temporal distribution of behavioural states using discrete, error-prone and incomplete observations. In this paper, a Bayesian Hierarchical Model (HBM) using Hidden Markov Process (HMP) is proposed as a template for analyzing fishing boats trajectories based on data available from satellite-based vessel monitoring systems (VMS). The analysis seeks to enhance the definition of the fishing pressure exerted on fish stocks, by discriminating between the different behavioural states of a fishing trip, and also by quantifying the relative importance of each of these states during a fishing trip. The HBM approach is tested to analyse the behaviour of pelagic trawlers in the Bay of Biscay. A hidden Markov chain with a regular discrete time step is used to model transitions between successive behavioural states (e.g., fishing, steaming, stopping (at Port or at sea)) of each vessel. The parameters of the movement process (speed and turning angles) are defined conditionally upon the behavioural states. Bayesian methods are used to integrate the available data (typically VMS position recorded at discrete time) and to draw inferences on any unknown parameters of the model. The model is first tested on simulated data with different parameters structures. Results provide insights on the potential of HBM with HMP to analyze VMS data. They show that if VMS positions are recorded synchronously with the instants at which the process switch from one behavioural state to another, the estimation method provides unbiased and precise inferences on behavioural states and on associated movement parameters. However, if the observations are not gathered with a sufficiently high frequency, the performance of the estimation method could be drastically impacted when the discrete observations are not synchronous with the switching instants. The model is then applied to real pathways to estimate variables of interest such as the number of operations per trip, time and distance spent fishing or travelling.     

The Importance of Analysis Method for Breeding Bird Survey Population Trend Estimates

Population trends from the Breeding Bird Survey are widely used to focus conservation efforts on species thought to be in decline and to test preliminary hypotheses regarding the causes of these declines. A number of statistical methods have been used to estimate population trends, but there is no consensus as to which is the most reliable. We quantified differences in trend estimates or different analysis methods applied to the same subset of Breeding Bird Survey data. We estimated trends for 115 species in British Columbia using three analysis methods: U.S. National Biological Service route regression, Canadian Wildlife Service route regression, and nonparametric rank-trends analysis. Overall, the number of species estimated to be declining was similar among the three methods, but the number of statistically significant declines was not similar (15, 8, and 29 respectively). In addition, many differences existed among methods in the trend estimates assigned to individual species. Comparing the two route regression methods, Canadian Wildlife Service estimates had a greater absolute magnitude on average than those of the U.S. National Biological Service method. U.S. National Biological Service estimates were on average more positive than the Canadian Wildlife Service estimates when the respective agency's data selection criteria were applied separately. These results imply that our ability to detect population declines and to prioritize species of conservation concern depend strongly upon the analysis method used. This highlights the need for further research to determine how best to accurately estimate trends from the data. We suggest a method for evaluating the performance of the analysis methods by using simulated Breeding Bird Survey data.     

Simulation of annual productivity and long-term population trends of white-winged doves in the Tamaulipan Biotic Province

We present a model that synthesizes decades of field data on white-winged doves (Zenaida asiatica asiatica; WWDO) in the Tamaulipan Biotic Province. The model is represented as a discrete-time, deterministic compartment model based on difference equations with a one-week time step designed to simulate annual productivity and long-term trends in abundance. We evaluated the model by comparing simulated annual productivity and long-term population trends to field data. Based on simulation results, we identified apparent inconsistencies in the database; we could not generate the observed annual production index with the model parameterized based on field nest success and survivorship data, nor could we generate a stable long-term population trend with the model parameterized based on suggested sustainable harvest rates and empirically-based estimates of migratory return rates. Simulation results suggest that nest success might be closer to 22% (rather than 35%). A similar trend resulted when simulated hunting pressure was increased by 25% (to 31%), or return rates of migrating juveniles and adults were decreased by 5.5 and 5.0%, to 69 and 77%, respectively, with all other values at the baseline level. For these reasons, until better estimates of nest success and migratory return rates are available, model predictions must be viewed with caution.     

Identification of Areas in Brazil that Optimize Conservation of Forest Carbon,Jaguars, and Biodiversity

A major question in global environmental policy is whether schemes to reduce carbon pollution through forest management, such as Reducing Emissions from Deforestation and Degradation (REDD+), can also benefit biodiversity conservation in tropical countries. We identified municipalities in Brazil that are priorities for reducing rates of deforestation and thus preserving carbon stocks that are also conservation targets for the endangered jaguar (Panthera onca) and biodiversity in general. Preliminary statistical analysis showed that municipalities with high biodiversity were positively associated with high forest carbon stocks. We used a multicriteria decision analysis to identify municipalities that offered the best opportunities for the conservation of forest carbon stocks and biodiversity conservation under a range of scenarios with different rates of deforestation and carbon values. We further categorized these areas by their representativeness of the entire country (through measures such as percent forest cover) and an indirect measure of cost (number of municipalities). The municipalities that offered optimal co‐benefits for forest carbon stocks and conservation were termed REDDspots (n = 159), and their spatial distribution was compared with the distribution of current and proposed REDD projects (n = 135). We defined REDDspots as the municipalities that offer the best opportunities for co‐benefits between the conservation of forest carbon stocks, jaguars, and other wildlife. These areas coincided in 25% (n = 40) of municipalities. We identified a further 95 municipalities that may have the greatest potential to develop additional REDD+ projects while also targeting biodiversity conservation. We concluded that REDD+ strategies could be an efficient tool for biodiversity conservation in key locations, especially in Amazonian and Atlantic Forest biomes. Identificación de Áreas en Brasil que Optimizan la Conservación del Carbono del Bosque, Jaguares y la Biodiversidad.     

At home and abroad: An empirical analysis of innovation and diffusion in energy technologies

This paper contributes to the induced innovation literature by extending the analysis of supply and demand determinants of innovation in energy technologies to account for international knowledge flows and spillovers. We select a sample of 38 innovating countries and study how knowledge related to energy-efficient and environmentally friendly technologies flows across geographical and technological space. We demonstrate that higher geographical and technological distances are associated with lower probabilities of knowledge flow. Next, we use previous estimates to construct internal and external knowledge stocks for a panel of 17 countries. We then present an econometric analysis of the supply and demand determinants of innovation accounting for international knowledge spillovers. Our results confirm the role of demand-pull effects, proxied by energy prices, and of technological opportunity, proxied by the knowledge stocks. Our results show that spillovers between countries have a significant positive impact on further innovation in energy-efficient and environmentally friendly technologies.     

Assessing the effects of pseudo-absences on predictive distribution model performance

Modelling species distributions with presence data from atlases, museum collections and databases is challenging. In this paper, we compare seven procedures to generate pseudo-absence data, which in turn are used to generate GLM-logistic regressed models when reliable absence data are not available. We use pseudo-absences selected randomly or by means of presence-only methods (ENFA and MDE) to model the distribution of a threatened endemic Iberian moth species (Graellsia isabelae). The results show that the pseudo-absence selection method greatly influences the percentage of explained variability, the scores of the accuracy measures and, most importantly, the degree of constraint in the distribution estimated. As we extract pseudo-absences from environmental regions further from the optimum established by presence data, the models generated obtain better accuracy scores, and over-prediction increases. When variables other than environmental ones influence the distribution of the species (i.e., non-equilibrium state) and precise information on absences is non-existent, the random selection of pseudo-absences or their selection from environmental localities similar to those of species presence data generates the most constrained predictive distribution maps, because pseudo-absences can be located within environmentally suitable areas. This study shows that if we do not have reliable absence data, the method of pseudo-absence selection strongly conditions the obtained model, generating different model predictions in the gradient between potential and realized distributions.     

燕山典型流域两种造林树种生态系统碳储量及固碳潜力研究

选择燕山典型流域6个林龄序列的小叶杨（Populus simonii）和5个林龄序列的山杏（Prunus sibirica）主要造林树种为研究对象,利用时间替代空间样地测量法量化退牧还林后生物量碳储量、凋落物碳储量和土壤碳储量及生态系统碳储量的变化规律,同时以各组成碳库-林龄序列中的最大碳储量之和作为生态系统饱和碳储量,以未退牧的天然草地生态系统碳储量作为初始植被类型的碳储量,分析总结了退牧还林对生态系统碳储量和碳循环的影响。结果表明,退牧还林后生态系统的生物量碳储量、凋落物碳储量基本随退牧年限的增加而增加,土壤碳储量随退牧年限的增加呈现先减小后增加的趋势。在没有人为干扰的情况下,9、15、18、22及29 a生小叶杨林的生态系统碳储量分别为7147.45、7461.67、7509.895、8468.375及8247.85 g·m^-2,9、15、18、22及26 a生山杏林的生态系统碳储量分别为6695.44、6700.82、8011.86、8001.92及7981.92 g·m^-2;9、15、18、22、29及36 a生小叶杨林的生态系统固碳潜力分别为757.08、1071.3、1119.53、2078.01、1857.48及1312.21 g·m^-2,9、15、18、22及26 a生山杏林的生态系统固碳潜力分别为310.45、1621.49、1611.55、1591.55及757.08 g·m^-2。长期来看,研究区退牧还林对提高生态系统碳汇能力是可观的、积极的。研究结果对提高造林对碳汇影响的估测能力提供数据支持,也为政府参与国际全球气候变化的谈判提供一个很好的案例研究和科学根据。