Impact of bias in predicted height on tree volume estimation: A case-study of intrinsic nonlinearity

Bias originating from intrinsic nonlinearity in nonlinear models is caused by excess curvature in the solution locus of parameter estimates derived from least squares procedures. Bias due to intrinsic nonlinearity varies according to sample size as well as model specification. This paper analyses consequences of fractionising data into smaller sub-samples. Based on measurements of stem diameter and total tree height from the first Danish national forest inventory, it is demonstrated how data splitting at random may cause the intrinsic nonlinear curvature to exceed the critical F-value. Application of a Taylor-series expansion shows that, for all practical purposes, the bias in predictions of individual tree volume (based on stem diameter and tree height) is negligible. To minimize residual variance, intrinsic curvature and, in turn, prediction bias, it is recommended that data be stratified according to site conditions, stand characteristics or other relevant criteria. Finally, the preferred model should exhibit close-to-linear behaviour.     

Do microbial processes regulate the stability of a coral atoll's enclosed pelagic ecosystem?

Complex marine ecosystems contain multiple feedback cycles that can cause unexpected responses to perturbations. To better predict these responses, complicated models are increasingly being developed to enable the study of feedback cycles. However, the sparseness of ecological data often limits the direct empirical parameterization of all model parameters. Here we use a Bayesian inverse analysis approach to synthesize empirical data and ecological theory derived from published studies of a coral atoll's enclosed pelagic ecosystem (Takapoto Atoll, French Polynesia). We then use the estimates of flux magnitudes to parameterize probabilistic compartment models with two forms of heterotrophic consumption: (1) “bottom-up” donor-controlled heterotrophic consumption and (2) “top-down” mass-action heterotrophic consumption. We explore how the flux magnitudes affect the ecosystem's stability properties of resilience, reactivity, and resistance under both assumptions for heterotrophic consumption. The models suggest that the microbial uptake of dissolved organic carbon (DOC) regulates the long term rate of return to steady state following a temporary or pulse perturbation (resilience), and the cycling of carbon between abiotic pools and heterotrophic compartments regulates the short-term response (reactivity). In the bottom-up process model, the sensitivity of steady state masses following a sustained or press perturbation (resistance) is highest for the DOC pool following a sustained change to the microbial uptake rate of DOC. Further, a change in the microbial uptake of DOC propagates through the ecosystem and affects the steady state values of zooplankton. The analysis suggests that the food web is highly dependent on the recycling between the abiotic and biotic carbon pools, particularly as mediated by the microbial consumption of DOC, and this recycling determines how the ecosystem responds to perturbations.     

Genetic diversity and population structure of the Chinese mitten crab <Emphasis Type="Italic">Eriocheir sinensis</Emphasis> in its native range

The Chinese mitten crab Eriocheir sinensis is an indigenous and economically important species in China, but can also be found as invasive species in Europe and America. Mitten crabs have been exploited extensively as a food resource since the 1990s. Despite its ecological and economic importance, the genetic structure of native mitten crab populations is not well understood. In this paper, we investigated the genetic structure of mitten crab populations in China by screening samples from ten locations covering six river systems at six microsatellite loci. Our results provide further evidence that mitten crabs from the River Nanliujiang in Southern China are a genetically differentiated population within the native range of Eriocheir, and should be recognized as a separate taxonomic unit. In contrast, extremely low levels of genetic differentiation and no significant geographic population structure were found among the samples located north of the River Nanliujiang. Based on the reproductive biology of mitten crabs and the geography of their habitat we argue that both natural and human-mediated gene flow are unlikely to fully account for the similar allele frequency distributions at microsatellite loci. Large population sizes of mitten crabs suggest instead that a virtual absence of genetic drift and significant homoplasy of microsatellite alleles have contributed to the observed pattern. Furthermore, a coalescent-based maximum likelihood method indicated a more than two-fold lower effective population size of the Southern population compared to the Northern Group and low but significant levels of gene flow between both areas.     

Thermal dependency of burrowing in three species within the bivalve genus <Emphasis Type="Italic">Laternula</Emphasis>: a latitudinal comparison

The upper thermal limits for burrowing and survival were compared with micro-habitat temperature for anomalodesmatan clams: Laternula elliptica (Antarctica, 67°S); Laternula recta, (temperate Australia, 38°S) and Laternula truncata (tropical Singapore, 1°N). Lethal limits (LT₅₀) were higher than burrowing limits (BT₅₀) in L. elliptica (7.5–9.0 and 2.2°C) and L. recta (winter, 32.8–36.8 and 31.1–32.8°C) but the same range for L. truncata (33.0–35.0 and 33.4–34.9°C). L. elliptica and L. truncata had a BT₅₀ 0.4 and 2.4–3.9°C, respectively, above their maximum experienced temperature. L. recta, which experience solar heating during midday low tides, had a BT₅₀ 0.7–2.4°C below and a range for LT₅₀ that spanned their predicted environmental maximum (33.5°C). L. recta showed no seasonal difference in LT₅₀ or BT₅₀. Our single genus comparisons contrast with macrophysiological studies showing that temperate species cope better with elevated temperatures. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Conservation planning for successional landscapes

The systematic conservation planning literature invariably assumes that the biodiversity features being preserved in sites do not change through time. We develop a conservation planning framework for ecosystems where disturbance events and succession drive vegetation dynamics. The framework incorporates three key attributes of disturbance theory: heterogeneity in disturbance rates, spatial correlation between disturbance events and different impacts of disturbance. In our conservation problem we wish to maximise the chance that we represent a certain number of successional types given a cap on the number of sites we can conserve. Correlation between disturbance events dramatically complicates the problem of choosing the optimal suite of sites. However, in our problem we discover that spatial correlation in disturbances affects the optimal reserve network very little. The reason is twofold: (i) through our probabilistic framework we focus on the long-term effectiveness of reserve networks and (ii) in the dynamics considered in our model the state of a site is not only affected by the most recent (correlated) disturbance event but also by the site's long-term stochastic history which blurs the impact of spatial correlation. If successional states are the conservation target rather than individual species then, conserving a site can only contribute to meeting one target. However, given that correlation of disturbance events may be ignored, we show that if the number of candidate reserves is sufficiently large the statistical dependence of different conservation targets may be ignored, too. We conclude that the computational complexity of reserve selection methods for dynamic ecosystems can be much simpler than they first appear.     

Using single- and multi-target regression trees and ensembles to model a compound index of vegetation condition

An important consideration in conservation and biodiversity planning is an appreciation of the condition or integrity of ecosystems. In this study, we have applied various machine learning methods to the problem of predicting the condition or quality of the remnant indigenous vegetation across an extensive area of south-eastern Australia—the state of Victoria. The field data were obtained using the ‘habitat hectares’ approach. This rapid assessment technique produces multiple scores that describe the condition of various attributes of the vegetation at a given site. Multiple sites were assessed and subsequently circumscribed with GIS and remote-sensed data.     

Predictability of marine nematode biodiversity

In this paper, we investigated: (1) the predictability of different aspects of biodiversity, (2) the effect of spatial autocorrelation on the predictability and (3) the environmental variables affecting the biodiversity of free-living marine nematodes on the Belgian Continental Shelf. An extensive historical database of free-living marine nematodes was employed to model different aspects of biodiversity: species richness, evenness, and taxonomic diversity. Artificial neural networks (ANNs), often considered as “black boxes”, were applied as a modeling tool. Three methods were used to reveal these “black boxes” and to identify the contributions of each environmental variable to the diversity indices. Since spatial autocorrelation is known to introduce bias in spatial analyses, Moran's I was used to test the spatial dependency of the diversity indices and the residuals of the model. The best predictions were made for evenness. Although species richness was quite accurately predicted as well, the residuals indicated a lack of performance of the model. Pure taxonomic diversity shows high spatial variability and is difficult to model. The biodiversity indices show a strong spatial dependency, opposed to the residuals of the models, indicating that the environmental variables explain the spatial variability of the diversity indices adequately. The most important environmental variables structuring evenness are clay and sand fraction, and the minimum annual total suspended matter. Species richness is also affected by the intensity of sand extraction and the amount of gravel of the sea bed.     

地球系统工程与区域环境问题调控的原则

论述了地球系统科学和工程研究的发展历史、研究对象和方法,及其在解决全球性环境危机中的作用;阐述了提高共识、言行一致、保护全球公共资源、实现可持续性目标等地球系统工程的基本原则. 论述了区域性与地球系统环境问题间的关系,提出区域环境问题调控的4条基本原则:系统性调控、区际公平、多方合作、综合手段和共同约束. 指出应注重应用地球系统科学与工程的理论与方法解决区域环境与生态问题;应深入探讨地球系统及区域环境与生态压力在中国现阶段的特征,统筹全局与长远调控区域环境问题;应开展长期连续观测和信息共享,创新调控全球与区域环境与生态的技术、方法.      

Vehicle-related ammonia emissions in Hong Kong

Weekly passive sampling of ambient ammonia has been performed at ground or rooftop levels at 12 locations in Hong Kong representing different land use categories. The highest weekly average roadside level of ammonia in Hong Kong was 7.1 μg m⁻³ at a street canyon site. Ambient ammonia concentrations for a rooftop urban-residential site with nearby light traffic were <1 μg m⁻³, being less than at an adjacent road and indoor car-park. Rooftop levels of ammonia showed a significant correlation with nitrogen monoxide concentrations. Honeycomb denuder samplings were also carried out at the rooftop site and the results were consistent with passive sampling. It is concluded that the 0.54 M vehicles in Hong Kong make a considerable contribution to ambient ammonia concentrations, which are otherwise low.     

Challenges of simulating complex environmental systems at the landscape scale: A controversial dialogue between two cups of espresso

With the advancement of computational systems and the development of model integration concepts, complexity of environmental model systems increased. In contrast to that, theory and knowledge about > environmental systems as well as the capability for environmental systems analyses remained, to a large extent, unchanged. As a consequence, model conceptualization, data gathering, and validation, have faced new challenges that hardly can be tackled by modellers alone. In this discourse-like review, we argue that modelling with reliable simulations of human-environmental interactions necessitate linking modelling and simulation research much stronger to science fields such as landscape ecology, community ecology, eco-hydrology, etc. It thus becomes more and more important to identify the adequate degree of complexity in environmental models (which is not only a technical or methodological question), to ensure data availability, and to test model performance. Even equally important, providing problem specific answers to environmental problems using simulation tools requires addressing end-user and stakeholder requirements during early stages of problem development. In doing so, we avoid modelling and simulation as an end of its own.