Implications of Macroalgal Isolation by Distance for Networks of Marine Protected Areas

The global extent of macroalgal forests is declining, greatly affecting marine biodiversity at broad scales through the effects macroalgae have on ecosystem processes, habitat provision, and food web support. Networks of marine protected areas comprise one potential tool that may safeguard gene flow among macroalgal populations in the face of increasing population fragmentation caused by pollution, habitat modification, climate change, algal harvesting, trophic cascades, and other anthropogenic stressors. Optimal design of protected area networks requires knowledge of effective dispersal distances for a range of macroalgae. We conducted a global meta‐analysis based on data in the published literature to determine the generality of relation between genetic differentiation and geographic distance among macroalgal populations. We also examined whether spatial genetic variation differed significantly with respect to higher taxon, life history, and habitat characteristics. We found clear evidence of population isolation by distance across a multitude of macroalgal species. Genetic and geographic distance were positively correlated across 49 studies; a modal distance of 50–100 km maintained F_ST < 0.2. This relation was consistent for all algal divisions, life cycles, habitats, and molecular marker classes investigated. Incorporating knowledge of the spatial scales of gene flow into the design of marine protected area networks will help moderate anthropogenic increases in population isolation and inbreeding and contribute to the resilience of macroalgal forests. Implicaciones del Aislamiento por Distancia de Macroalgas para Redes de Áreas Marinas Protegidas     

A hierarchical Bayesian model for forecasting state-level corn yield

Historically, the National Agricultural Statistics Service crop forecasts and estimates have been determined by a group of commodity experts called the Agricultural Statistics Board (ASB). The corn yield forecasts for the “speculative region,” ten states that account for approximately 85 % of corn production, are based on two sets of monthly surveys, a farmer interview survey and a field measurement survey. The members of the ASB subjectively determine a forecast on the basis of a discussion of the survey data and auxiliary information about weather, average planting dates, and crop maturity. The ASB uses an iterative procedure, where initial state estimates are adjusted so that the weighted sum of the final state estimates is equal to a previously-determined estimate for the speculative region. Deficiencies of the highly subjective ASB process are lack of reproducibility and a measure of uncertainty. This paper describes the use of Bayesian methods to model the ASB process in a way that leads to objective forecasts and estimates of the corn yield. First, we use small area estimation techniques to obtain state-level forecasts. Second, we describe a way to adjust the state forecasts so that the weighted sum of the state forecasts is equal to a previously-determined regional forecast. We use several diagnostic techniques to assess the goodness of fit of various models and their competitors. We use Markov chain Monte Carlo methods to fit the models to both historic and current data from the two monthly surveys. Our results show that our methodology can provide reasonable and objective forecasts of corn yields for states in the speculative region.     

Soil C and N models that integrate microbial diversity

Industrial agriculture is yearly responsible for the loss of 55–100 Pg of historical soil carbon and 9.9 Tg of reactive nitrogen worldwide. Therefore, management practices should be adapted to preserve ecological processes and reduce inputs and environmental impacts. In particular, the management of soil organic matter (SOM) is a key factor influencing C and N cycles. Soil microorganisms play a central role in SOM dynamics. For instance, microbial diversity may explain up to 77 % of carbon mineralisation activities. However, soil microbial diversity is actually rarely taken into account in models of C and N dynamics. Here, we review the influence of microbial diversity on C and N dynamics, and the integration of microbial diversity in soil C and N models. We found that a gain of microbial richness and evenness enhances soil C and N dynamics on the average, though the improvement of C and N dynamics depends on the composition of microbial community. We reviewed 50 models integrating soil microbial diversity. More than 90 % of models integrate microbial diversity with discrete compartments representing conceptual functional groups (64 %) or identified taxonomic groups interacting in a food web (28 %). Half of the models have not been tested against an empirical dataset while the other half mainly consider fixed parameters. This is due to the difficulty to link taxonomic and functional diversity.     

The data not collected on community forestry

Conservation and development practitioners increasingly promote community forestry as a way to conserve ecosystem services, consolidate resource rights, and reduce poverty. However, outcomes of community forestry have been mixed; many initiatives failed to achieve intended objectives. There is a rich literature on institutional arrangements of community forestry, but there has been little effort to examine the role of socioeconomic, market, and biophysical factors in shaping both land‐cover change dynamics and individual and collective livelihood outcomes. We systematically reviewed the peer‐reviewed literature on community forestry to examine and quantify existing knowledge gaps in the community‐forestry literature relative to these factors. In examining 697 cases of community forest management (CFM), extracted from 267 peer‐reviewed publications, we found 3 key trends that limit understanding of community forestry. First, we found substantial data gaps linking population dynamics, market forces, and biophysical characteristics to both environmental and livelihood outcomes. Second, most studies focused on environmental outcomes, and the majority of studies that assessed socioeconomic outcomes relied on qualitative data, making comparisons across cases difficult. Finally, there was a heavy bias toward studies on South Asian forests, indicating that the literature on community forestry may not be representative of decentralization policies and CFM globally.     

Changes in the spatial distribution of spawning activity by north-east Atlantic mackerel in warming seas: 1977–2010

Migratory species with a broad geographic range, such as north-east Atlantic mackerel, may be amongst the fauna most able to respond to warming seas, typically with a poleward shift in range. Habitat heterogeneity could, however, produce more complex patterns than a simple polewards translation in distribution. We tested for changes in the central location and spatial spread of mackerel spawning over a 33-year period. Spatial statistics [centre of gravity (CoG) of egg production, spatial variance, and degree of anisotropy] were used to summarise interannual changes in the spawning locations of the western spawning stock of north-east Atlantic mackerel (NEA-WSC) using data from the ICES triennial egg survey. A northwards shift in CoG of egg production estimates was observed, related to both an expansion in the distribution in survey effort and warming waters of the north-east Atlantic. Sea surface temperature (SST) had a significant positive association with the observed northward movement of NEA-WSC mackerel, equivalent to a displacement of 37.7 km °C^?1 (based on spring mean SST for the region). The spatial distribution of spawning around the CoG also changed significantly with SST, with a less elongated spatial spread in warm years. An increase in the proportion of spawning over the Porcupine Bank demonstrated how habitat interacts with positional shifts to affect how north-east Atlantic mackerel are distributed around the centre of their spawning range.     

International Environmental Agreements: Design of Optimal Transfers Under Heterogeneity

International environmental agreements (IEAs) can coordinate abatement of transboundary pollutants. This paper investigates how heterogeneous countries facing a stock pollutant might structure such an agreement. In particular, we examine how an IEA might be implemented with a set of monetary transfers. The focus is on transfers that are time invariant, linear in emissions, and consistent with budget balance. There is a range of such schemes that would induce efficient emissions. We provide a simple and intuitive characterization of these penalties and describe how specific proposals might be chosen in order to facilitate compliance and implementation. Our proposals are illustrated with a simple example. We show that heterogeneity reduces the scope for penalty schemes to jointly satisfy desirable properties.     

Photochemical Reactivities of Paraffinic Hydrocarbon-Nitrogen Oxide Mixtures Upon Addition of Propylene or Toluene

Effects associated with photochemical air pollution were measured during irradiation of n-butane-nitrogen oxide or n-butane-ethane-nitrogen oxide mixtures, with small amounts of propylene or toluene added. The effects measured including nitrogen dioxide and oxidant dosages, yields of formaldehyde and peroxy-acetyl nitrate, and eye irritation response. The results obtained clearly show that beneficial effects result from selective changes in hydrocarbon composition as well as from reduction of total hydrocarbon concenfration. Exclusion of olefins and alkylbenzenes was highly effective in reducing oxidant dosage, formaldehyde and peroxyacetyl nitrate concentrations, and eye irritation response. The only penalty was a modest increase in nitrogen dioxide dosage. A large reduction in nitrogen oxide concentration reduced nitrogen dioxide dosage and eye irritation response, but with the penalty of a large increase in oxidant dosage. The desirability of preferentially reducing olefins and alkylbenzenes rather than paraffinic hydrocarbons, acetylene, and benzene is strongly supported by this study. Research and development efforts should be directed toward preferential hydrocarbon control by mechanical or catalytic control     

Isoprene emissions and climate

Biogenic volatile organic compounds (BVOCs) play an important role in atmospheric chemistry and the carbon cycle. Isoprene is quantitatively the most important of the non-methane BVOCs (NMBVOCs), with an annual emission of about 400–600 TgC; about 90% of this is emitted by terrestrial plants. Incorporating a mechanistic treatment of isoprene emissions within land-surface schemes has recently become a focus for the modelling community, the aim being to quantify the potential magnitude of associated climate feedbacks. However, these efforts are hampered by major uncertainties about why plants emit isoprene and the relative importance of different environmental controls on isoprene emission. The availability and reliability of observations of isoprene fluxes from different types of vegetation is limited, and this also imposes constraints on model development. Nevertheless, progress is being made towards the development of mechanistic models of isoprene emission which, in conjunction with atmospheric chemistry models, will ultimately allow improved quantification of the feedbacks between the terrestrial biosphere and climate under past and future climate states.