Prediction of response of zooplankton biomass to climatic and oceanic changes

This paper examines the long-term variation in zooplankton biomass in response to climatic and oceanic changes, using a neural network as a nonlinear multivariate analysis method. Zooplankton data collected from 1951 to 1990 off the shore of northeastern Japan were analyzed. We considered patterns of the Kuroshio and the Oyashio, sea surface temperature, and meteorological parameters as environmental factors that affect zooplankton biomass. Back propagation neural networks were trained to generate mapping functions between environmental variables and zooplankton biomass. The performance of the network models was tested by varying the numbers of input and hidden units. Changes in zooplankton biomass could be predicted from environmental conditions. The neural network yielded predictions with smaller errors than those of predictions determined by linear multiple regression. The sensitivity analysis of networks was used to extract predictive knowledge. The air pressure, sea surface temperature, and some indices of atmospheric circulation were the primary factors for predictions. The patterns of the Kuroshio and the Oyashio demonstrated different effects among sea areas.     

The Importance of Being Spatial (and Reserved): Assessing Northern Spotted Owl Habitat Relationships with Hierarchical Bayesian Models

Abstract:  Regional conservation planning increasingly draws on habitat suitability models to support decisions regarding land allocation and management. Nevertheless, statistical techniques commonly used for developing such models may give misleading results because they fail to account for 3 factors common in data sets of species distribution: spatial autocorrelation, the large number of sites where the species is absent (zero inflation), and uneven survey effort. We used spatial autoregressive models fit with Bayesian Markov Chain Monte Carlo techniques to assess the relationship between older coniferous forest and the abundance of Northern Spotted Owl nest and activity sites throughout the species' range. The spatial random-effect term incorporated in the autoregressive models successfully accounted for zero inflation and reduced the effect of survey bias on estimates of species–habitat associations. Our results support the hypothesis that the relationship between owl distribution and older forest varies with latitude. A quadratic relationship between owl abundance and older forest was evident in the southern portion of the range, and a pseudothreshold relationship was evident in the northern portion of the range. Our results suggest that proposed changes to the network of owl habitat reserves would reduce the proportion of the population protected by up to one-third, and that proposed guidelines for forest management within reserves underestimate the proportion of older forest associated with maximum owl abundance and inappropriately generalize threshold relationships among subregions. Bayesian spatial models can greatly enhance the utility of habitat analysis for conservation planning because they add the statistical flexibility necessary for analyzing regional survey data while retaining the interpretability of simpler models.     

Protection of Genetic Diversity and Maintenance of Connectivity among Reef Corals within Marine Protected Areas

Abstract: High‐latitude coral reefs (HLRs) are potentially vulnerable marine ecosystems facing well‐documented threats to tropical reefs and exposure to suboptimal temperatures and insolation. In addition, because of their geographic isolation, HLRs may have poor or erratic larval connections to tropical reefs and a reduced genetic diversity and capacity to respond to environmental change. On Australia's east coast, a system of marine protected areas (MPAs) has been established with the aim of conserving HLRs in part by providing sources of colonizing larvae. To examine the effectiveness of existing MPAs as networks for dispersal, we compared genetic diversity within and among the HLRs in MPAs and between these HLRs and tropical reefs on the southern Great Barrier Reef (GBR). The 2 coral species best represented on Australian HLRs (the brooding Pocillopora damicornis and the broadcast‐spawning Goniastrea australensis) exhibited sharply contrasting patterns of diversity and connectedness. For P. damicornis, the 8‐locus genetic and genotypic diversity declined dramatically with increasing latitude (N_a= 3.6–1.2, H_e= 0.3–0.03, N_g:N = 0.87–0.06), although population structure was consistent with recruitment derived largely from sexual reproduction (G_o:G_e= 1.28–0.55). Genetic differentiation was high among the HLRs (F_ST[SD]= 0.32 [0.08], p < 0.05) and between the GBR and the HLRs (F_ST= 0.24 [0.06], p < 0.05), which indicates these temperate populations are effectively closed. In contrast for G. australensis, 9‐locus genetic diversity was more consistent across reefs (N_a= 4.2–3.9, H_e= 0.3–0.26, N_g:N = 1–0.61), and there was no differentiation among regions (F_ST= 0.00 [0.004], p > 0.05), which implies the HLRs and the southern GBR are strongly interconnected. Our results demonstrate that although the current MPAs appear to capture most of the genetic diversity present within the HLR systems for these 2 species, their sharply contrasting patterns of connectivity indicate some taxa, such as P. damicornis, will be more vulnerable than others, and this disparity will provide challenges for future management.     

Evaluating and expanding the European Union's protected-area network toward potential post-2020 coverage targets

The Convention on Biological Diversity's (CBD) strategic plan will expire in 2020, but biodiversity loss is ongoing. Scientists call for more ambitious targets in the next agreement. The nature-needs-half movement, for example, has advocated conserving half of Earth to solve the biodiversity crisis, which has been translated to protecting 50% of each ecoregion. We evaluated current protection levels of ecoregions in the territory of one of the CBD's signatories, the European Union (EU). We also explored the possible enlargement of the Natura 2000 network to implement 30% or 50% ecoregion coverage in the EU member states’ protected area (PA) network. Based on the most recent land-use data, we examined whether ecoregions have enough natural area left to reach such high coverage targets. We used a spatially explicit mixed integer programing model to estimate the least-cost expansion of the PA network based on 3 scenarios that put different emphasis on total conservation cost, ecological representation of ecosystems, or emphasize an equal share of the burden among member states. To realize 30% and 50% ecoregion coverage, the EU would need to add 6.6% and 24.2%, respectively, of its terrestrial area to its PA network. For all 3 scenarios, the EU would need to designate most recommended new PAs in seminatural forests and other semi- or natural ecosystems. Because 15 ecoregions did not have enough natural area left to implement the ecoregion-coverage targets, some member states would also need to establish new PAs on productive land, allocating the largest share to arable land. Thirty percent ecoregion coverage was met by protecting remaining natural areas in all ecoregions except 3, where productive land would also need to be included. Our results support discussions of higher ecoregions protection targets for post-2020 biodiversity frameworks.     

Identifying important species that amplify or mitigate the interactive effects of human impacts on marine food webs

Some species may have a larger role than others in the transfer of complex effects of multiple human stressors, such as changes in biomass, through marine food webs. We devised a novel approach to identify such species. We constructed annual interaction-effect networks (IENs) of the simulated changes in biomass between species of the southeastern Australian marine system. Each annual IEN was composed of the species linked by either an additive (sum of the individual stressor response), synergistic (lower biomass compared with additive effects), or antagonistic (greater biomass compared with additive effects) response to the interaction effect of ocean warming, ocean acidification, and fisheries. Structurally, over the simulation period, the number of species and links in the synergistic IENs increased and the network structure became more stable. The stability of the antagonistic IENs decreased and became more vulnerable to the loss of species. In contrast, there was no change in the structural attributes of species linked by an additive response. Using indices common in food-web and network theory, we identified the species in each IEN for which a change in biomass from stressor effects would disproportionately affect the biomass of other species via direct and indirect local, intermediate, and global predator–prey feeding interactions. Knowing the species that transfer the most synergistic or antagonistic responses in a food-web may inform conservation under increasing multiple-stressor impacts.     

Estimating freshwater turtle mortality rates and population declines following hook ingestion

Freshwater turtle populations are susceptible to declines following small increases in the mortality of adults, making it essential to identify and understand potential threats. Freshwater turtles ingest fish hooks associated with recreational angling, and this is likely a problem because hook ingestion is a source of additive mortality for sea turtles. We used a Bayesian‐modeling framework, observed rates of hook ingestion by freshwater turtles, and mortality of sea turtles from hook ingestion to examine the probability that a freshwater turtle in a given population ingests a hook and subsequently dies from it. We used the results of these analyses and previously published life‐history data to simulate the effects of hook ingestion on population growth for 3 species of freshwater turtle. In our simulation, the probability that an individual turtle ingests a hook and dies as a result was 1.2–11%. Our simulation results suggest that this rate of mortality from hook ingestion is sufficient to cause population declines. We believe we have identified fish‐hook ingestion as a serious yet generally overlooked threat to the viability of freshwater turtle populations.     

Applying artificial neural network methodology to ocean color remote sensing

Artificial neural networks (ANN) are widely used as continuous models to fit non-linear transfer functions. In this study we used ANN to retrieve chlorophyll pigments in the near-surface of oceans from Ocean Color measurements. This bio-optical inversion is established by analyzing concomitant sun-light spectral reflectances over the ocean surface and pigment concentration. The relationships are complex, non-linear, and their biological nature implies a significant variability. Moreover, the sun-light reflectances are usually measured by satellite radiometers flying at 800 km over the ocean surface, which affect the data by adding radiometric noise and atmospheric correction errors. By comparison with the polynomial fit usually employed to treat this problem, we show the advantages of neural function approximation like the association of non-linear complexity and noise filtering.     

典型云南高原湖泊叶绿素a与影响因子的定量关系及对比分析

湖泊富营养化的影响因子涉及水文、物化、生物等多方面,具有复合性和非线性特征,定量化其与影响因素间的相关关系有助于识别影响湖泊营养状态的关键因子,可以用较低的成本、较短的时间达到理想的控制效果.云南高原湖泊具有易发生富营养化的自然和气候特征,对其富营养化发生条件及影响因子的分析可为科学的控制决策提供参考.本文选取云南滇池、程海、抚仙湖和异龙湖4个高原湖泊,比较湖泊自然特征与流域社会经济条件的异同;构建包括绝对主成分多元线性回归分析(APCS-MLR)、结构方程模型(SEM)及人工神经网络模型(ANN)的综合分析方法,重点关注并确定浮游初级生产力的代表指标(叶绿素a,Chla)与相关影响因子间的定量相关关系.研究发现:14个湖泊中,对Chla浓度变化影响最大的均为理化因子,但在各湖中该影响的正、负性及不同理化因子的贡献权重有较大差异;2流域污染源构成的不同在一定程度上影响了入湖的氮、磷负荷,使4个湖泊表现出不同的营养盐限制性特征;3流域面积、湖泊形态及湖体水动力条件影响着营养盐在湖体中的迁移转换,造成4个湖泊富营养化的差异性特征;4对Chla与影响因子间因果关联的识别须结合深入的机理过程分析.     

A Comparison of Bayesian Methods for Uncertainty Analysis in Hydraulic and Hydrodynamic Modeling

We evaluate and compare the performance of Bayesian Monte Carlo (BMC), Markov chain Monte Carlo (MCMC), and the Generalized Likelihood Uncertainty Estimation (GLUE) for uncertainty analysis in hydraulic and hydrodynamic modeling (HHM) studies. The methods are evaluated in a synthetic 1D wave routing exercise based on the diffusion wave model, and in a multidimensional hydrodynamic study based on the Environmental Fluid Dynamics Code to simulate estuarine circulation processes in Weeks Bay, Alabama. Results show that BMC and MCMC provide similar estimates of uncertainty. The posterior parameter densities computed by both methods are highly consistent, as well as the calibrated parameter estimates and uncertainty bounds. Although some studies suggest that MCMC is more efficient than BMC, our results did not show a clear difference between the performance of the two methods. This seems to be due to the low number of model parameters typically involved in HHM studies, and the use of the same likelihood function. In fact, for these studies, the implementation of BMC results simpler and provides similar results to MCMC. The results of GLUE are, on the other hand, less consistent to the results of BMC and MCMC in both applications. The posterior probability densities tend to be flat and similar to the uniform priors, which can result in calibrated parameter estimates centered in the parametric space.