Improving multi-site benefit functions via Bayesian model averaging: A new approach to benefit transfer

A benefit function transfer obtains estimates of willingness-to-pay (WTP) for the evaluation of a given policy at a site by combining existing information from different study sites. This has the advantage that more efficient estimates are obtained, but it relies on the assumption that the heterogeneity between sites is appropriately captured in the benefit transfer model. A more expensive alternative to estimate WTP is to analyze only data from the policy site in question while ignoring information from other sites. We make use of the fact that these two choices can be viewed as a model selection problem and extend the set of models to allow for the hypothesis that the benefit function is only applicable to a subset of sites. We show how Bayesian model averaging (BMA) techniques can be used to optimally combine information from all models.The Bayesian algorithm searches for the set of sites that can form the basis for estimating a benefit function and reveals whether such information can be transferred to new sites for which only a small data set is available. We illustrate the method with a sample of 42 forests from U.K. and Ireland. We find that BMA benefit function transfer produces reliable estimates and can increase about 8 times the information content of a small sample when the forest is ‘poolable’.     

Nectar secondary compounds affect self-pollen transfer: implications for female and male reproduction

Pollen movement within and among plants affects inbreeding, plant fitness, and the spatial scale of genetic differentiation. Although a number of studies have assessed how plant and floral traits influence pollen movement via changes in pollinator behavior, few have explored how nectar chemical composition affects pollen transfer. As many as 55% of plants produce secondary compounds in their nectar, which is surprising given that nectar is typically thought to attract pollinators. We tested the hypothesis that nectar with secondary compounds may benefit plants by encouraging pollinators to leave plants after visiting only a few flowers, thus reducing self-pollen transfer. We used Gelsemium sempervirens, a plant whose nectar contains the alkaloid gelsemine, which has been shown to be a deterrent to foraging bee pollinators. We found that high nectar alkaloids reduced the total and proportion of self-pollen received by one-half and one-third, respectively. However, nectar alkaloids did not affect female reproduction when we removed the potential for self-pollination (by emasculating all flowers on plants). We then tested the assumption that self-pollen in combination with outcrossed pollen depresses seed set. We found that plants were weakly self-compatible, but self-pollen with outcrossed pollen did not reduce seed set relative to solely outcrossed flowers. Finally, an exponential model of pollen carryover suggests that high nectar alkaloids could benefit plants via increased pollen export (an estimate of male function), but only when pollinators were efficient and abundant and plants had large floral displays. Results suggest that high nectar alkaloids may benefit plants via increased pollen export under a restricted set of ecological conditions, but in general, the costs of high nectar alkaloids in reducing pollination balanced or outweighed the benefits of reducing self-pollen transfer for estimates of female and male reproduction.     

Testing the Internal Consistency of Contingent Valuation Surveys

For an internal consistency check, one can examine WTP responses to different surveys. Holding an environmental problem constant, concave preferences imply WTPconcavein the success of a remedy. The same preferences imply WTPconvexin the magnitude of an environmental problem, assuming a successful remedy. Having opposite signed curvature of these two WTP patterns is general, not requiring concave preferences. For an adding-up test, an environmental commodity can be divided. Then, WTP for one part plus WTP for the second part, conditional on already having the first part, equals WTP for the whole, adjusted by an income effect.     

What can we learn from benefit transfer errors? Evidence from 20 years of research on convergent validity

We develop a nonparametric approach to meta-analysis and use it to identify modeling decisions that affect benefit transfer errors. The meta-data describe the results from 31 empirical studies testing the convergent validity of benefit transfers. They evaluated numerous methodological procedures, collectively reporting 1071 transfer errors. Our meta-regressions identify several important findings, including: (1) the median absolute error is 39%; (2) function transfers outperform value transfers; (3) transfers describing environmental quantity generate lower transfer errors than transfers describing quality changes; (4) geographic site similarity is important for value transfers; (5) contingent valuation generates lower transfer errors than other valuation methods; and (6) combining data from multiple studies tends to reduce transfer errors.     

Effects of human demand on conservation planning for biodiversity and ecosystem services

Safeguarding ecosystem services and biodiversity is critical to achieving sustainable development. To date, ecosystem services quantification has focused on the biophysical supply of services with less emphasis on human beneficiaries (i.e., demand). Only when both occur do ecosystems benefit people, but demand may shift ecosystem service priorities toward human-dominated landscapes that support less biodiversity. We quantified how accounting for demand affects the efficiency of conservation in capturing both human benefits and biodiversity by comparing conservation priorities identified with and without accounting for demand. We mapped supply and benefit for 3 ecosystem services (flood mitigation, crop pollination, and nature-based recreation) by adapting existing ecosystem service models to include and exclude factors representing human demand. We then identified conservation priorities for each with the conservation planning program Marxan. Particularly for flood mitigation and crop pollination, supply served as a poor proxy for benefit because demand changed the spatial distribution of ecosystem service provision. Including demand when jointly targeting biodiversity and ecosystem service increased the efficiency of conservation efforts targeting ecosystem services without reducing biodiversity outcomes. Our results highlight the importance of incorporating demand when quantifying ecosystem services for conservation planning.     

Testing the Reliability of the Benefit Function Transfer Approach

This article presents an experiment designed to test the reliability of the benefit function transfer approach using contingent valuation methods. The experiment uses data collected from anglers surveyed across eight contiguous Texas Gulf Coast bay regions over three distinct time periods. Results indicate that the benefit function transfer approach tends to over-estimate benefits, implying that, at least for the case of recreational saltwater fishing in Texas, the benefit function transfer approach is not reliable.     

Income inequality and willingness to pay for environmental public goods

We study how the distribution of income among members of society, and income inequality in particular, affects social willingness to pay (WTP) for environmental public goods. We find that social WTP for environmental goods decreases (increases) with income inequality if and only if environmental goods and manufactured goods are substitutes (complements). We derive adjustment factors for benefit transfer to control for differences in income distributions between a study site and a policy site. For illustration, we quantify how social WTP for environmental public goods depends on the respective income distributions for empirical case studies in Sweden and the World at large. We find that the adjustment for income inequality can be substantial.     

A longevity cost of re-mating but no benefits of polyandry in the almond moth, <Emphasis Type="Italic">Cadra cautella</Emphasis>

Species where most but not all females mate monandrously can provide insight into the potential factors both promoting and restricting polyandry. Polyandry is typically explained by direct and/or indirect benefits models; however, polyandry may also confer costs via sexually antagonistic processes. The fitness of polyandrous and monandrous females may also vary with environmental conditions, such as availability of water. For some lepidopterans, water is a vital resource that increases fecundity and may be a direct benefit of multiple mating. Male lepidopterans transfer large spermatophores that may be an important water source for females, particularly for species living in water-depauperate environments. In such species, multiple-mating females may increase their reproductive output. We examined the fitness consequences of multiple mating in the almond moth, Cadra cautella. Males transfer substantial spermatophores; these have a large chitinous process attached, which decrease female longevity. To assess the impact of female mating treatment and water availability on female fitness, females mated once or twice, either with the same or different males, with half the females in each treatment receiving water. Water-fed females had dramatically increased fecundity, but we found no fitness benefits of multiple mating. Male longevity decreased with increased mating frequency and potentially his level of reproductive investment. Water-deprived females that mated twice died sooner than once-mated females, while multiple-mating females that received water lived longer than their water-deprived counterparts. It is interesting to note that the male’s spermatophore process did not affect female fitness or longevity. Why polyandry is maintained in this species is discussed.     

Using empirical models of species colonization under multiple threatening processes to identify complementary threat‐mitigation strategies

Approaches to prioritize conservation actions are gaining popularity. However, limited empirical evidence exists on which species might benefit most from threat mitigation and on what combination of threats, if mitigated simultaneously, would result in the best outcomes for biodiversity. We devised a way to prioritize threat mitigation at a regional scale with empirical evidence based on predicted changes to population dynamics—information that is lacking in most threat‐management prioritization frameworks that rely on expert elicitation. We used dynamic occupancy models to investigate the effects of multiple threats (tree cover, grazing, and presence of an hyperaggressive competitor, the Noisy Miner (Manorina melanocephala) on bird‐population dynamics in an endangered woodland community in southeastern Australia. The 3 threatening processes had different effects on different species. We used predicted patch‐colonization probabilities to estimate the benefit to each species of removing one or more threats. We then determined the complementary set of threat‐mitigation strategies that maximized colonization of all species while ensuring that redundant actions with little benefit were avoided. The single action that resulted in the highest colonization was increasing tree cover, which increased patch colonization by 5% and 11% on average across all species and for declining species, respectively. Combining Noisy Miner control with increasing tree cover increased species colonization by 10% and 19% on average for all species and for declining species respectively, and was a higher priority than changing grazing regimes. Guidance for prioritizing threat mitigation is critical in the face of cumulative threatening processes. By incorporating population dynamics in prioritization of threat management, our approach helps ensure funding is not wasted on ineffective management programs that target the wrong threats or species.     

Transferability of Species Distribution Models: a Functional Habitat Approach for Two Regionally Threatened Butterflies

Abstract:  Numerous models for predicting species distribution have been developed for conservation purposes. Most of them make use of environmental data (e.g., climate, topography, land use) at a coarse grid resolution (often kilometres). Such approaches are useful for conservation policy issues including reserve-network selection. The efficiency of predictive models for species distribution is usually tested on the area for which they were developed. Although highly interesting from the point of view of conservation efficiency, transferability of such models to independent areas is still under debate. We tested the transferability of habitat-based predictive distribution models for two regionally threatened butterflies, the green hairstreak ( Callophrys rubi ) and the grayling ( Hipparchia semele ), within and among three nature reserves in northeastern Belgium. We built predictive models based on spatially detailed maps of area-wide distribution and density of ecological resources. We used resources directly related to ecological functions (host plants, nectar sources, shelter, microclimate) rather than environmental surrogate variables. We obtained models that performed well with few resource variables. All models were transferable—although to different degrees—among the independent areas within the same broad geographical region. We argue that habitat models based on essential functional resources could transfer better in space than models that use indirect environmental variables. Because functional variables can easily be interpreted and even be directly affected by terrain managers, these models can be useful tools to guide species-adapted reserve management.     

Toward equality of biodiversity knowledge through technology transfer

To help stem the continuing decline of biodiversity, effective transfer of technology from resource‐rich to biodiversity‐rich countries is required. Biodiversity technology as defined by the Convention on Biological Diversity (CBD) is a complex term, encompassing a wide variety of activities and interest groups. As yet, there is no robust framework by which to monitor the extent to which technology transfer might benefit biodiversity. We devised a definition of biodiversity technology and a framework for the monitoring of technology transfer between CBD signatories. Biodiversity technology within the scope of the CBD encompasses hard and soft technologies that are relevant to the conservation and sustainable use of biodiversity, or make use of genetic resources, and that relate to all aspects of the CBD, with a particular focus on technology transfer from resource‐rich to biodiversity‐rich countries. Our proposed framework introduces technology transfer as a response indicator: technology transfer is increased to stem pressures on biodiversity. We suggest an initial approach of tracking technology flow between countries; charting this flow is likely to be a one‐to‐many relationship (i.e., the flow of a specific technology from one country to multiple countries). Future developments should then focus on integrating biodiversity technology transfer into the current pressure‐state‐response indicator framework favored by the CBD (i.e., measuring the influence of technology transfer on changes in state and pressure variables). Structured national reporting is important to obtaining metrics relevant to technology and knowledge transfer. Interim measures, that can be used to assess biodiversity technology or knowledge status while more in‐depth indicators are being developed, include the number of species inventories, threatened species lists, or national red lists; databases on publications and project funding may provide measures of international cooperation. Such a pragmatic approach, followed by rigorous testing of specific technology transfer metrics submitted by CBD signatories in a standardized manner may in turn improve the focus of future targets on technology transfer for biodiversity conservation.     

Measuring marginal willingness to pay using conjoint analysis and developing benefit transfer functions in various Asian cities

We need a consistent methodology to measure the co-benefits of climate change mitigation across Asian countries. This study chose a strategy of modifying the Japan-specific life-cycle impact assessment method based on endpoint modeling (LIME) for wider application across countries. LIME has two dimensions. First, it is an environmental science that links the cause-and-effect chain. Second, it is an environmental valuation that weighs four endpoint damages in monetary terms through a conjoint analysis that is derived from an Internet-based questionnaire survey. This article describes the modification of the methodology for application of the conjoint analysis to weigh environmental impacts. We approached the investigation as follows. First, we conducted Internet surveys to measure marginal willingness-to-pay (MWTP). We used a sample of 112 respondents in their 20 s to 40 s, divided equally between men and women, in 11 cities across China, India, and Southeast Asia. The results obtained showed clear statistical significance and were comparable across the cities. Second, we attempted to develop functions (called benefit transfer functions) to simplify the measured MWTP in order to apply it across different Asian countries. The functions were derived through a stepwise meta-analytic method, a type of multiple regression analysis whose independent variable was MWTP and dependent variables were attributes of both respondents and surveyed cities. The functions showed that coal consumption and percentage of nature reserve were dependent variables. Then, the MWTPs estimated from the functions were compared with the measured MWTP for transfer error, which is calculated by the absolute value of the difference between the estimated value and the measured value divided by the latter. The transfer error was below 50% in about 90% of the 44 results (a combination of four endpoints and 11 cities), implying that the developed functions were statistically significant.     

Scaling-up spatially-explicit ecological models using graphics processors

How the properties of ecosystems relate to spatial scale is a prominent topic in current ecosystem research. Despite this, spatially explicit models typically include only a limited range of spatial scales, mostly because of computing limitations. Here, we describe the use of graphics processors to efficiently solve spatially explicit ecological models at large spatial scale using the CUDA language extension. We explain this technique by implementing three classical models of spatial self-organization in ecology: a spiral-wave forming predator-prey model, a model of pattern formation in arid vegetation, and a model of disturbance in mussel beds on rocky shores. Using these models, we show that the solutions of models on large spatial grids can be obtained on graphics processors with up to two orders of magnitude reduction in simulation time relative to normal pc processors. This allows for efficient simulation of very large spatial grids, which is crucial for, for instance, the study of the effect of spatial heterogeneity on the formation of self-organized spatial patterns, thereby facilitating the comparison between theoretical results and empirical data. Finally, we show that large-scale spatial simulations are preferable over repetitions at smaller spatial scales in identifying the presence of scaling relations in spatially self-organized ecosystems. Hence, the study of scaling laws in ecology may benefit significantly from implementation of ecological models on graphics processors.     

Sensitivity of ecological models to their climate drivers: statistical ensembles for forcing.

Global and regional numerical models for terrestrial ecosystem dynamics require fine spatial resolution and temporally complete historical climate fields as input variables. However, because climate observations are unevenly spaced and have incomplete records, such fields need to be estimated. In addition, uncertainty in these fields associated with their estimation are rarely assessed. Ecological models are usually driven with a geostatistical model's mean estimate (kriging) of these fields without accounting for this uncertainty, much less evaluating such errors in terms of their propagation in ecological simulations. We introduce a Bayesian statistical framework to model climate observations to create spatially uniform and temporally complete fields, taking into account correlation in time and space, spatial heterogeneity, lack of normality, and uncertainty about all these factors. A key benefit of the Bayesian model is that it generates uncertainty measures for the generated fields. To demonstrate this method, we reconstruct historical monthly precipitation fields (a driver for ecological models) on a fine resolution grid for a climatically heterogeneous region in the western United States. The main goal of this work is to evaluate the sensitivity of ecological models to the uncertainty associated with prediction of their climate drivers. To assess their numerical sensitivity to predicted input variables, we generate a set of ecological model simulations run using an ensemble of different versions of the reconstructed fields. We construct such an ensemble by sampling from the posterior predictive distribution of the climate field. We demonstrate that the estimated prediction error of the climate field can be very high. We evaluate the importance of such errors in ecological model experiments using an ensemble of historical precipitation time series in simulations of grassland biogeochemical dynamics with an ecological numerical model, Century. We show how uncertainty in predicted precipitation fields is propagated into ecological model results and that this propagation had different modes. Depending on output variable, the response of model dynamics to uncertainty in inputs ranged from uncertainty in outputs that matched that of inputs to those that were muted or that were biased, as well as uncertainty that was persistent in time after input errors dropped.     

Lead in New York City community garden chicken eggs: influential factors and health implications

Raising chickens for eggs in urban areas is becoming increasingly common. Urban chickens may be exposed to lead, a common urban soil contaminant. We measured lead concentrations in chicken eggs from New York City (NYC) community gardens and collected information on factors that might affect those concentrations. Lead was detected between 10 and 167 μg/kg in 48 % of NYC eggs. Measures of lead in eggs from a henhouse were significantly associated (p < 0.005) with lead concentrations in soil. The association between soil and egg lead has been evaluated only once before, by a study of a rural region in Belgium. In our study, the apparent lead soil-to-egg transfer efficiency was considerably lower than that found in Belgium, suggesting that there may be important geographic differences in this transfer. We developed models that suggested that, for sites like ours, lead concentrations in >50 % of eggs from a henhouse would exceed store-bought egg concentrations (<7–13 μg/kg; 3 % above detection limit) at soil lead concentrations >120 mg/kg and that the concentration in one of six eggs from a henhouse would exceed a 100 μg/kg guidance value at soil lead concentrations >410 mg/kg. Our models also suggested that the availability of dietary calcium supplements was another influential factor that reduced egg lead concentrations. Estimates of health risk from consuming eggs with the lead concentrations we measured generally were not significant. However, soil lead concentrations in this study were <600 mg/kg, and considerably higher concentrations are not uncommon. Efforts to reduce lead transfer to chicken eggs and associated exposure are recommended for urban chicken keepers.     

Turbulence-Based Models for Gas Transfer Analysis with Channel Shape Factor Influence

Gas transfer through surface water of streams is an effective process for the environmental quality of the aquatic ecosystem. Several theoretical approaches have been proposed to estimate gas transfer rate. This paper is devoted to present a turbulence-based model and to compare it with other 3 turbulence-based modeling frameworks that provide an estimation of gas-transfer coefficient K_L at the air-water interface. These models were derived for the reaeration process. In this paper, they have been verified both for reaeration and volatilization using experimental data collected in a laboratory rectangular flume and in a circular sewer reach. These data refer to oxygen absorption and cyclohexane volatilization, respectively. Comparison of results for oxygen shows that the tested models exhibit an average absolute difference between their results and the experimental data ranging from 12.5% and 25.6%. Also, the scaling analysis of the experimental data support both small-eddy based models and the model proposed by the authors. Moreover, volatilization results show that the process is also affected by a channel shape factor, which was, finally, quantified.     

The business case for SDGs: an analysis of inclusive business models in emerging economies

In September 2015, the Sustainable Development Goals (SDGs) were endorsed by the United Nations and adopted by all 193 Member States. The SDGs integrate the 5P’s: People, Planet, Prosperity, Peace, and Partnership and clearly stress the need for all stakeholders to collaborate to create a sustainable world. Most importantly, the SDGs appeal to the central and diverse role that the business sector can play to deliver on the SDGs. This paper provides an analysis of inclusive business (IB) models as market-based solutions to contribute to the achievement of the SDGs and benefit those at the Base of the Pyramid (BoP). We investigate the IB models and their social impact in 20 organizations from emerging economies across five different sectors. The findings should help increase the uptake and scale of quality IB models and practices among the private sector, development communities, and governments to promote inclusive economic growth and social impact.     

Genetic relatedness and evolution of insect sociality

Summary Genetic relatedness is a vital concept in the study of the evolution of social behaviour because it can be used to predict gene transmission into future generations. Any models using genetic relatedness to predict the course of social evolution have to take into account that this relatedness, when defined as genotypic regression, is affected by selection and that the evolution also depends on specific assumptions concerning the cost/benefit ratios. In this paper these problems are shown to exist in recent studies examining the evolution of worker behaviour in insects; these studies are reanalysed using allele frequency models, and the results compared with those from other models.It is shown that cyclic inbreeding (alternating outbred and inbred generations) can favour worker evolution in diploid populations but is a necessary condition only within a very limited range of the cost/benefit ratio.Male-haploid models show that both female and male workers evolve more easily if they can manipulate the brood produced by the queen by biassing the brood sex ratio in favour of females (the optimal proportion of females is ca. 0.75). The threshold for female workers to evolve is also lowered if they are allowed to produce some of the colony's male offspring (which arise from unfertilized eggs), in which case the optimal brood sex ratio becomes closer to 1:1. It is apparent that the two factors — sex-ratio bias and worker-produced males — are also important in the comparison of worker evolution in male-haploid and diploid insects.     

Implications of simultaneity in a physical damage function

A modeler must often rely on highly simplified representations of complex physical systems when analyzing associated economic issues. Herein, we consider a management problem in which a bioeconomic system exhibits simultaneity in processes governing productivity and damage. In this case, it may benefit the producer to sacrifice productivity to reduce the costs associated with increased damage. We specify empirically a structural damage relationship that explains the biological process by which an invasive species damages a host and estimate the structural model and its reduced form with an exceptional dataset on infestation of olives by the olive fruit fly. We contrast the results of these models with the approach typically taken in the economic literature, which expresses damage as a function of pest density. The population-based approach introduces significantly greater bias into the individual grower's choice of damage-control inputs than estimates based on the structural model.     

Resources and offspring provisioning: a test of the Trexler-DeAngelis model for matrotrophy evolution

Theoretical models of the evolution of matrotrophy from a lecithotrophic ancestor suggest that resource availability plays a major role in selective scenarios favoring a change in offspring provisioning. We examined effects of feeding level on embryo provisioning in the livebearing fish Gambusia geiseri, a species with dual provisioning of embryos via both yolk sequestered in large eggs and post-fertilization mother-to-embryo nutrient transfer. Females were fed either once per day or once every three days for three months. Females fed daily had marginally larger brood size, significantly larger embryos, and a higher rate of nutrient transfer (assayed directly by injection of radiolabeled nutrients) than females fed every third day. There was no difference in the size of unfertilized eggs between the feeding treatments. Resource effects on matrotrophic provisioning in G. geiseri suggest that matrotrophy plays an important role in provisioning and allows females to adjust offspring size in response to resource availability.