Incorporating microclimate into species distribution models

Species distribution models (SDMs) have rapidly evolved into one of the most widely used tools to answer a broad range of ecological questions, from the effects of climate change to challenges for species management. Current SDMs and their predictions under anthropogenic climate change are, however, often based on free‐air or synoptic temperature conditions with a coarse resolution, and thus fail to capture apparent temperature (cf. microclimate) experienced by living organisms within their habitats. Yet microclimate operates as soon as a habitat can be characterized by a vertical component (e.g. forests, mountains, or cities) or by horizontal variation in surface cover. The mismatch between how we usually express climate (cf. coarse‐grained free‐air conditions) and the apparent microclimatic conditions that living organisms experience has only recently been acknowledged in SDMs, yet several studies have already made considerable progress in tackling this problem from different angles. In this review, we summarize the currently available methods to obtain meaningful microclimatic data for use in distribution modelling. We discuss the issue of extent and resolution, and propose an integrated framework using a selection of appropriately‐placed sensors in combination with both the detailed measurements of the habitat 3D structure, for example derived from digital elevation models or airborne laser scanning, and the long‐term records of free‐air conditions from weather stations. As such, we can obtain microclimatic data with a relevant spatiotemporal resolution and extent to dynamically model current and future species distributions.     

Arbuscular mycorrhizal fungi as (agro)ecosystem engineers

Symbiotic interactions have been shown to facilitate shifts in the structure and function of host plant communities. For example, parasitic plants can induce changes in plant diversity through the suppression of competitive community dominants. Arbuscular mycorrhizal (AM) fungi have also be shown to induce shifts in host communities by increasing host plant nutrient uptake and growth while suppressing non-mycorrhizal species. AM fungi can therefore function as ecosystem engineers facilitating shifts in host plant communities though the presumed physiological suppression of non-contributing or non-mycorrhizal plant species. This dichotomy in plant response to AM fungi has been suggested as a tool to suppress weed species (many of which are non-mycorrhizal) in agro-ecosystems where mycorrhizal crop species are cultivated. Rinaudo et al. (2010), this issue, have demonstrated that AM fungi can suppress pernicious non-mycorrhizal weed species including Chenopodium album (fat hen) while benefiting the crop plant Helianthus annuus (sunflower). These findings now suggest a future for harnessing AM fungi as agro-ecosystem engineers representing potential alternatives to costly and environmentally damaging herbicides.     

Incorporating infectious duration-dependent transmission into Bayesian epidemic models

Compartmental models are commonly used to describe the spread of infectious diseases by estimating the probabilities of transitions between important disease states. A significant challenge in fitting Bayesian compartmental models lies in the need to estimate the duration of the infectious period, based on limited data providing only symptom onset date or another proxy for the start of infectiousness. Commonly, the exponential distribution is used to describe the infectious duration, an overly simplistic approach, which is not biologically plausible. More flexible distributions can be used, but parameter identifiability and computational cost can worsen for moderately sized or large epidemics. In this article, we present a novel approach, which considers a curve of transmissibility over a fixed infectious duration. The incorporation of infectious duration-dependent (IDD) transmissibility, which decays to zero during the infectious period, is biologically reasonable for many viral infections and fixing the length of the infectious period eases computational complexity in model fitting. Through simulation, we evaluate different functional forms of IDD transmissibility curves and show that the proposed approach offers improved estimation of the time-varying reproductive number. We illustrate the benefit of our approach through a new analysis of the 1995 outbreak of Ebola Virus Disease in the Democratic Republic of the Congo.     

Incorporating biotic interactions in the distribution models of African wild silk moths (Gonometa species,Lasiocampidae) using different representations of modelled host tree distributions

Biotic interactions influence species niches and may thus shape distributions. Nevertheless, species distribution modelling has traditionally relied exclusively on environmental factors to predict species distributions, while biotic interactions have only seldom been incorporated into models. This study tested the ability of incorporating biotic interactions, in the form of host plant distributions, to increase model performance for two host‐dependent lepidopterans of economic interest, namely the African silk moth species, Gonometa postica and Gonometa rufobrunnea (Lasiocampidae). Both species are dependent on a small number of host tree species for the completion of their life cycle. We thus expected the host plant distribution to be an important predictor of Gonometa distributions. Model performance of a species distribution model trained only on abiotic predictors was compared to four species distribution models that additionally incorporated biotic interactions in the form of four different representations of host plant distributions as predictors. We found that incorporating the moth–host plant interactions improved G. rufobrunnea model performance for all representations of host plant distribution, while for G. postica model performance only improved for one representation of host plant distribution. The best performing representation of host plant distribution differed for the two Gonometa species. While these results suggest that incorporating biotic interactions into species distribution models can improve model performance, there is inconsistency in which representation of the host tree distribution best improves predictions. Therefore, the ability of biotic interactions to improve species distribution models may be context‐specific, even for species which have obligatory interactions with other organisms.     

Incorporating movement into models of grey seal population dynamics

1. One of the most difficult problems in developing spatially explicit models of population dynamics is the validation and parameterization of the movement process. We show how movement models derived from capture-recapture analysis can be improved by incorporating them into a spatially explicit metapopulation model that is fitted to a time series of abundance data. 2. We applied multisite capture-recapture analysis techniques to photo-identification data collected from female grey seals at the four main breeding colonies in the North Sea between 1999 and 2001. The best-fitting movement models were then incorporated into state-space metapopulation models that explicitly accounted for demographic and observational stochasticity. 3. These metapopulation models were fitted to a 20-year time series of pup production data for each colony using a Bayesian approach. The best-fitting model, based on the Akaike Information Criterion (AIC), had only a single movement parameter, whose confidence interval was 82% less than that obtained from the capture-recapture study, but there was some support for a model that included an effect of distance between colonies. 4. The state-space modelling provided improved estimates of other demographic parameters. 5. The incorporation of movement, and the way in which it was modelled, affected both local and regional dynamics. These differences were most evident as colonies approached their carrying capacities, suggesting that our ability to discriminate between models should improve as the length of the grey seal time series increases.     

Incorporating climate change into ecosystem service assessments and decisions: a review

Climate change is having a significant impact on ecosystem services and is likely to become increasingly important as this phenomenon intensifies. Future impacts can be difficult to assess as they often involve long timescales, dynamic systems with high uncertainties, and are typically confounded by other drivers of change. Despite a growing literature on climate change impacts on ecosystem services, no quantitative syntheses exist. Hence, we lack an overarching understanding of the impacts of climate change, how they are being assessed, and the extent to which other drivers, uncertainties, and decision making are incorporated. To address this, we systematically reviewed the peer‐reviewed literature that assesses climate change impacts on ecosystem services at subglobal scales. We found that the impact of climate change on most types of services was predominantly negative (59% negative, 24% mixed, 4% neutral, 13% positive), but varied across services, drivers, and assessment methods. Although uncertainty was usually incorporated, there were substantial gaps in the sources of uncertainty included, along with the methods used to incorporate them. We found that relatively few studies integrated decision making, and even fewer studies aimed to identify solutions that were robust to uncertainty. For management or policy to ensure the delivery of ecosystem services, integrated approaches that incorporate multiple drivers of change and account for multiple sources of uncertainty are needed. This is undoubtedly a challenging task, but ignoring these complexities can result in misleading assessments of the impacts of climate change, suboptimal management outcomes, and the inefficient allocation of resources for climate adaptation.     

Incorporating uncertainty and prior information into stable isotope mixing models

Stable isotopes are a powerful tool for ecologists, often used to assess contributions of different sources to a mixture (e.g. prey to a consumer). Mixing models use stable isotope data to estimate the contribution of sources to a mixture. Uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. We developed a Bayesian-mixing model that estimates probability distributions of source contributions to a mixture while explicitly accounting for uncertainty associated with multiple sources, fractionation and isotope signatures. This model also allows for optional incorporation of informative prior information in analyses. We demonstrate our model using a predator–prey case study. Accounting for uncertainty in mixing model inputs can change the variability, magnitude and rank order of estimates of prey (source) contributions to the predator (mixture). Isotope mixing models need to fully account for uncertainty in order to accurately estimate source contributions.     

Incorporating habitat use in models of fauna fatalities on roads

Aim  To highlight the benefit of using habitat use to improve the accuracy of predictive road fatality models.
Location  The Snowy Mountains Highway in southern New South Wales, Australia.
Methods  A binary logistic regression model was constructed using wombat fatality presences and randomly generated absences. Species-specific habitat variables were included as predictors in the model selection process as well as two spatially explicit measures of wombat habitat use. Generalized additive models (GAMs) were constructed for each possible combination of predictors in R. The final model was selected by comparing all models subsets for the eight predictors and employing the one standard error rule to select the best model set.
Results  The final predictive model had high discriminatory power and incorporated both measures of species habitat use, greatly exceeding the variation explained by a previously published model for the same species and road.
Main Conclusions  Our findings highlight the importance of incorporating variables that describe habitat use by fauna for predictive modelling of animal-vehicle crashes. Reliance upon models that ignore landscape patterns are limited in their capacity to identify hotspots and inform managers of locations to engage in mitigation.     

Evaluation of enteric methane prediction equations for dairy cows used in whole farm models

The importance of evaluating greenhouse gas (GHG) emissions from dairy cows within the whole farm setting is being realized as more important than evaluating these emissions in isolation. Current whole farm models aimed at evaluating GHG emissions make use of simple regression equations to predict enteric methane (CH₄) production. The objective of the current paper is to evaluate the performance of nine CH₄ prediction equations that are currently being used in whole farm GHG models. Data used to evaluate the prediction equations came from a collection of individual (IND) and treatment averaged (TRT) data. Equations were compared based on mean square prediction error (MSPE) and concordance correlation coefficient (CCC) analysis. In general, predictions were poor, with root MSPE (as a percentage of observed mean) values ranging from 20.2 to 52.5 for the IND database and from 24.0 to 38.2 for the TRT database and CCC values ranging from 0.009 to 0.493 for the IND database and from 0.000 to 0.271 for the TRT database. Overall, the equations of Moe & Tyrrell and IPCC Tier II performed best on the IND dataset, and the equations of Moe & Tyrrell and Kirchgeßner et al., performed best on the TRT dataset. Results show that the simple more generalized equations performed worse than those that attempted to represent important aspects of diet composition, but in general significant amounts of bias and deviation of the regression slope from unity existed for all equations. The low prediction accuracy of CH₄ equations in whole farm models may introduce substantial error into inventories of GHG emissions and lead to incorrect mitigation recommendations.     

Incorporating hydrodynamics into spatiotemporal metabolic models of bubble column gas fermentation

Gas fermentation has emerged as a technologically and economically attractive option for producing renewable fuels and chemicals from carbon monoxide (CO) rich waste streams. LanzaTech has developed a proprietary strain of the gas fermentating acetogen Clostridium autoethanogenum as a microbial platform for synthesizing ethanol, 2,3-butanediol, and other chemicals. Bubble column reactor technology is being developed for the large-scale production, motivating the investigation of multiphase reactor hydrodynamics. In this study, we combined hydrodynamics with a genome-scale reconstruction of C. autoethanogenum metabolism and multiphase convection–dispersion equations to compare the performance of bubble column reactors with and without liquid recycle. For both reactor configurations, hydrodynamics was predicted to diminish bubble column performance with respect to CO conversion, biomass production, and ethanol production when compared with bubble column models in which the gas phase was modeled as ideal plug flow plus axial dispersion. Liquid recycle was predicted to be advantageous by increasing CO conversion, biomass production, and ethanol and 2,3-butanediol production compared with the non-recycle reactor configuration. Parametric studies performed for the liquid recycle configuration with two-phase hydrodynamics showed that increased CO feed flow rates (more gas supply), smaller CO gas bubbles (more gas–liquid mass transfer), and shorter column heights (more gas per volume of liquid per time) favored ethanol production over acetate production. Our computational results demonstrate the power of combining cellular metabolic models and two-phase hydrodynamics for simulating and optimizing gas fermentation reactors.     

Incorporating the temporal autocorrelation of demographic rates into structured population models

Population dynamics are typically temporally autocorrelated: population sizes are positively or negatively correlated with past population sizes. Previous studies have found that positive temporal autocorrelation increases the risk of extinction due to ‘inertia’ that prolongs downward fluctuations in population size. However, temporal autocorrelation has not yet been analyzed at the level of life cycle transitions. We developed an R package, colorednoise, which creates stochastic matrix population projections with distinct temporal autocorrelation values for each matrix element. We used it to analyze long-term demographic data on 25 populations from the COMADRE and COMPADRE databases and simulate their stochastic dynamics. We found a broad range of temporal autocorrelation across species, populations and life cycle stages. The number of stage-classes in the matrix strongly affected the temporal autocorrelation of the growth rate. In the plant populations, reproduction transitions had more negative temporal autocorrelation than survival transitions, and matrices dominated by positive temporal autocorrelation had higher extinction risk, while in animal populations transition type was not associated with noise color. Our results indicate that temporal autocorrelation varies across life cycle transitions, even among populations of the same species. We present the colorednoise package for researchers to analyze the temporal autocorrelation of structured demographic rates.     

Digging through model complexity: using hierarchical models to uncover evolutionary processes in the wild

The growing interest for studying questions in the wild requires acknowledging that eco-evolutionary processes are complex, hierarchically structured and often partially observed or with measurement error. These issues have long been ignored in evolutionary biology, which might have led to flawed inference when addressing evolutionary questions. Hierarchical modelling (HM) has been proposed as a generic statistical framework to deal with complexity in ecological data and account for uncertainty. However, to date, HM has seldom been used to investigate evolutionary mechanisms possibly underlying observed patterns. Here, we contend the HM approach offers a relevant approach for the study of eco-evolutionary processes in the wild by confronting formal theories to empirical data through proper statistical inference. Studying eco-evolutionary processes requires considering the complete and often complex life histories of organisms. We show how this can be achieved by combining sequentially all life-history components and all available sources of information through HM. We demonstrate how eco-evolutionary processes may be poorly inferred or even missed without using the full potential of HM. As a case study, we use the Atlantic salmon and data on wild marked juveniles. We assess a reaction norm for migration and two potential trade-offs for survival. Overall, HM has a great potential to address evolutionary questions and investigate important processes that could not previously be assessed in laboratory or short time-scale studies.     

Beyond simple adaptation: Incorporating other evolutionary processes and concepts into eco-evolutionary dynamics

Studies of eco-evolutionary dynamics have integrated evolution with ecological processes at multiple scales (populations, communities and ecosystems) and with multiple interspecific interactions (antagonistic, mutualistic and competitive). However, evolution has often been conceptualised as a simple process: short-term directional adaptation that increases population growth. Here we argue that diverse other evolutionary processes, well studied in population genetics and evolutionary ecology, should also be considered to explore the full spectrum of feedback between ecological and evolutionary processes. Relevant but underappreciated processes include (1) drift and mutation, (2) disruptive selection causing lineage diversification or speciation reversal and (3) evolution driven by relative fitness differences that may decrease population growth. Because eco-evolutionary dynamics have often been studied by population and community ecologists, it will be important to incorporate a variety of concepts in population genetics and evolutionary ecology to better understand and predict eco-evolutionary dynamics in nature.     

养鸭数量对CH4排放的影响

探讨不同养鸭数量对稻田甲烷(CH4)排放的影响,为确定稻鸭共育模式中最佳养鸭数量提供环境学支撑.运用静止箱原位采样技术测定了不同养鸭数量的稻田甲烷排放通量、稻田土壤化学性质、产甲烷细菌种群数量以及水层溶解氧含量.结果表明,不同养鸭数量稻田水层溶解氧含量间差异显著(p＜0.01),养鸭数量越多,溶解氧含量越高.20只鸭/667m2 稻田的水层溶解氧含量最大,与对照比,早稻增加了2.2%～68.7%,晚稻增加了11.07%～110.77%;养鸭稻田土壤还原物质含量减少,产甲烷细菌数量下降.不同养鸭数量的稻田甲烷排放量之间差异显著,养鸭数量越多,甲烷排放量越少,与对照比,早稻减少了18.22%～28.13%,晚稻减少了17.73%～34.44%.相关分析表明,甲烷排放通量与水层溶解氧含量呈极显著负相关(p＜0.001),与土壤还原物质含量及产甲烷细菌数量呈显著正相关(p＜0.01).因此,稻鸭共育减排甲烷的主要原因是养鸭提高了水体和土壤中溶解氧含量,增加养鸭数量促进甲烷减排.     

稻飞虱预报模型及防治技术

应用回归分析法,对江苏省高邮市１９８３～１９９７年共１５年稻飞虱发生程度的历史观测资料和气象资料进行了分析,建立了中、短期预测模型,经历史资料回验,符合率８５％以上。并提出规范化药剂防治策略及技术。