Fine‐resolution conservation planning with limited climate‐change information

Climate‐change induced uncertainties in future spatial patterns of conservation‐related outcomes make it difficult to implement standard conservation‐planning paradigms. A recent study translates Markowitz's risk‐diversification strategy from finance to conservation settings, enabling conservation agents to use this diversification strategy for allocating conservation and restoration investments across space to minimize the risk associated with such uncertainty. However, this method is information intensive and requires a large number of forecasts of ecological outcomes associated with possible climate‐change scenarios for carrying out fine‐resolution conservation planning. We developed a technique for iterative, spatial portfolio analysis that can be used to allocate scarce conservation resources across a desired level of subregions in a planning landscape in the absence of a sufficient number of ecological forecasts. We applied our technique to the Prairie Pothole Region in central North America. A lack of sufficient future climate information prevented attainment of the most efficient risk‐return conservation outcomes in the Prairie Pothole Region. The difference in expected conservation returns between conservation planning with limited climate‐change information and full climate‐change information was as large as 30% for the Prairie Pothole Region even when the most efficient iterative approach was used. However, our iterative approach allowed finer resolution portfolio allocation with limited climate‐change forecasts such that the best possible risk‐return combinations were obtained. With our most efficient iterative approach, the expected loss in conservation outcomes owing to limited climate‐change information could be reduced by 17% relative to other iterative approaches.     

Thermotolerant coliform loadings to coastal areas of Santa Catarina (Brazil) evidence the effect of growing urbanisation and insufficient provision of sewerage infrastructure

Thermotolerant coliform (TC) loadings were quantified for 49 catchments draining into the North and South Bays of Santa Catarina (SC, southeastern Brazil), an area known for its tourism and aquaculture. TC loadings were calculated based on flow measurements taken in 26 rivers. TC concentrations ere quantified based on surface water samples collected at 49 catchment outlets in 2012 and 2013. Median TC loads ranged from 3.7 × 10³ to 6.8 × 10⁸ MPN s^?1. TC loadings in the catchments increased in proportion to increases in resident human population, population density and percentage of urbanised area. Catchments with more than 60% of area covered by wastewater collection and treatment systems had higher TC loads per person than catchments with less than 25%. Based on the study catchments, these results indicate that current sewerage infrastructure is ineffective in reducing contamination of faecal origin to surface waters. These findings have important implications for the management of microbiological health hazards in bathing, recreational and shellfish aquaculture waters in the North and South Bays of Santa Catarina Island.     

Drivers of Seabird Population Recovery on New Zealand Islands after Predator Eradication

Eradication of introduced mammalian predators from islands has become increasingly common, with over 800 successful projects around the world. Historically, introduced predators extirpated or reduced the size of many seabird populations, changing the dynamics of entire island ecosystems. Although the primary outcome of many eradication projects is the restoration of affected seabird populations, natural population responses are rarely documented and mechanisms are poorly understood. We used a generic model of seabird colony growth to identify key predictor variables relevant to recovery or recolonization. We used generalized linear mixed models to test the importance of these variables in driving seabird population responses after predator eradication on islands around New Zealand. The most influential variable affecting recolonization of seabirds around New Zealand was the distance to a source population, with few cases of recolonization without a source population ≤25 km away. Colony growth was most affected by metapopulation status; there was little colony growth in species with a declining status. These characteristics may facilitate the prioritization of newly predator‐free islands for active management. Although we found some evidence documenting natural recovery, generally this topic was understudied. Our results suggest that in order to guide management strategies, more effort should be allocated to monitoring wildlife response after eradication. Conductores de la Recuperación de Poblaciones de Aves Marinas en Islas de Nueva Zelanda después de la Erradicación de Depredadores     

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.     

Archaeometric researches on the provenance of Mediterranean Archaic Phoenician and Punic pottery

The aim of this study is to setup a first chemical database that could represent the starting point for a reliable classification method to discriminate between Archaic Phoenician and Punic pottery on the base of their chemical data. This database up to now can discriminate between several different areas of production and provenance and can be applied also to unknown ceramic samples of comparable age and production areas. More than 100 ceramic fragments were involved in this research, coming from various archaeological sites having a crucial importance in the context of the Phoenician and Punic settlement in central and western Mediterranean: Carthage (Tunisia), Toscanos (South Andalusia, Spain), Sulci, Monte Sirai, Othoca, Tharros (Sardinia, Italy) and Pithecusa (Campania, Italy). Since long-time archaeologists hypothesised that Mediterranean Archaic Phoenician and Punic pottery had mainly a local or just a regional diffusion, with the exception of some particular class like transport amphorae. To verify the pottery provenance, statistical analyses were carried out to define the existence of different ceramic compositional groups characterised by a local origin or imported from other sites. The existing literature data are now supplemented by new archaeometric investigations both on Archaic Phoenician ceramics and clayey raw materials from Sardinia. Therefore, diffractometric analyses, optical microscopy observations and X-ray fluorescence analyses were performed to identify the mineralogical and chemical composition of Othoca ceramics and clayey raw material. The obtained results were then compared with own literature data concerning Phoenician and Punic pottery in order to find features related to the different ceramic productions and their provenance. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) were also performed on the chemical compositional data in order to discriminate ceramic groups. A very complex situation was found: imported ceramics coming from Carthage, with a large-scale distribution, were found together with a predominant local production pottery. The archaeometric results demonstrate that historical and typological approach has to be supported by scientific analyses to better understand local or Mediterranean exchanges.     

Case study of landfill leachate recirculation using small-diameter vertical wells

A case study of landfill liquids addition using small diameter (5 cm) vertical wells is reported. More than 25,000 m³ of leachate was added via 134 vertical wells installed 3 m, 12 m, and 18 m deep over five years in a landfill in Florida, US. Liquids addition performance (flow rate per unit screen length per unit liquid head) ranged from 5.6 × 10^?8 to 3.6 × 10^?6 m³ s^?1 per m screen length per m liquid head. The estimated radial hydraulic conductivity ranged from 3.5 × 10^?6 to 4.2 × 10^?4 m s^?1. The extent of lateral moisture movement ranged from 8 to 10 m based on the responses of moisture sensors installed around vertical well clusters, and surface seeps were found to limit the achievable liquids addition rates, despite the use of concrete collars under a pressurized liquids addition scenario. The average moisture content before (51 samples) and after (272 samples) the recirculation experiments were 23% (wet weight basis) and 45% (wet weight basis), respectively, and biochemical methane potential measurements of excavated waste indicated significant (p < 0.025) decomposition.     

Using a Bayesian network to clarify areas requiring research in a host–pathogen system

Bayesian network analyses can be used to interactively change the strength of effect of variables in a model to explore complex relationships in new ways. In doing so, they allow one to identify influential nodes that are not well studied empirically so that future research can be prioritized. We identified relationships in host and pathogen biology to examine disease‐driven declines of amphibians associated with amphibian chytrid fungus (Batrachochytrium dendrobatidis). We constructed a Bayesian network consisting of behavioral, genetic, physiological, and environmental variables that influence disease and used them to predict host population trends. We varied the impacts of specific variables in the model to reveal factors with the most influence on host population trend. The behavior of the nodes (the way in which the variables probabilistically responded to changes in states of the parents, which are the nodes or variables that directly influenced them in the graphical model) was consistent with published results. The frog population had a 49% probability of decline when all states were set at their original values, and this probability increased when body temperatures were cold, the immune system was not suppressing infection, and the ambient environment was conducive to growth of B. dendrobatidis. These findings suggest the construction of our model reflected the complex relationships characteristic of host–pathogen interactions. Changes to climatic variables alone did not strongly influence the probability of population decline, which suggests that climate interacts with other factors such as the capacity of the frog immune system to suppress disease. Changes to the adaptive immune system and disease reservoirs had a large effect on the population trend, but there was little empirical information available for model construction. Our model inputs can be used as a base to examine other systems, and our results show that such analyses are useful tools for reviewing existing literature, identifying links poorly supported by evidence, and understanding complexities in emerging infectious‐disease systems.