Demographic effects of extreme winter weather in the barn owl

Extreme weather events can lead to immediate catastrophic mortality. Due to their rare occurrence, however, the long-term impacts of such events for ecological processes are unclear. We examined the effect of extreme winters on barn owl (Tyto alba) survival and reproduction in Switzerland over a 68-year period (∼20 generations). This long-term data set allowed us to compare events that occurred only once in several decades to more frequent events. Winter harshness explained 17 and 49% of the variance in juvenile and adult survival, respectively, and the two harshest winters were associated with major population crashes caused by simultaneous low juvenile and adult survival. These two winters increased the correlation between juvenile and adult survival from 0.63 to 0.69. Overall, survival decreased non-linearly with increasing winter harshness in adults, and linearly in juveniles. In contrast, brood size was not related to the harshness of the preceding winter. Our results thus reveal complex interactions between climate and demography. The relationship between weather and survival observed during regular years is likely to underestimate the importance of climate variation for population dynamics.     

气候变化对我国干旱/半干旱区小麦生产影响的模拟研究

利用随机天气模型,将气候模式对大气中CO₂倍增时预测的气候情景与CERES-小麦模式相连接,研究了气候变化对我国冬小麦和春小麦生产的可能影响。并对水分、温度、CO₂综合对小麦的作用进行初步模拟分析。所得结论为:①气候变化后小麦发育将加快,生育期缩短,春小麦生育期缩短的绝对数和相对数均小于冬小麦。②北方十个站点小麦生产的最适水分条件在不同站点、不同气候情景下都有所不同。最适水分条件变幅在40%～80%。③在不考虑CO₂对小麦影响的情况下,由于热量充足,只要水分条件适宜,未来我国北方干旱、半干旱地区小麦产量整体都有增产趋势。如果考虑CO₂,增产效果更加明显。     

气候变化对小兴安岭森林影响的模拟研究(英文)

中国东北小兴安岭的温带针阔混交林是中国重要的商品木材资源。本文应用森林林窗模型 NEWCOP来模拟森林的生长和更新。研究表明 ,该模型可以再现森林的树种组成动态和森林的分布 ,故可以用来模拟气候变化对森林的可能影响。通过模拟在 GISS 2× CO2 和 GFDL 2× CO2 气候变化情景下的小兴安岭现有森林的动态发现 :在未来 10 0 a现存森林可能有一个快速衰退过程 ,随后可恢复为落叶阔叶树 (如蒙古栎 )为主要树种的森林 ,从而取代了目前针叶树种在森林树种组成的优势地位     

Implications of varying pipe model relationships on Scots Pine growth in different climates

1. The process-based model SIMFORG , based on the pipe theory, was parameterized for Scots Pine at six locations along a north–south gradient in Europe. The ratio of foliage mass to stem cross-sectional area was changed as a function of potential evapotranspiration as proposed by Berninger et al. (1995).
2. Allocation to the stem differed between the locations and affected consequently the stemwood production. Variation in the net primary production and differences in the pipe model parameters were responsible for these differences. There was good agreement between measured and simulated data.
3. Increase in primary production, as predicted by climate-change senarios, increased allocation to the stem. However, the results were sensitive to changes in the foliage mass to stem sapwood cross-sectional area ratio. The changes in allocation were higher in the north than in the south.     

不同蚕区蚕桑生产与气候因子关系的研究

本文采用灰色系统理论和方法,研究了安徽省各蚕区的蚕桑生产与气候因子的变化规律,通过关联分析,研究了影响各蚕区的气候因子,建立了年茧量及亩桑产茧量的灰色静动态模型．研究结果表明,气温、降水、日照是影响各蚕区蚕桑生产的主要因子,但由于不同蚕区气候条件的差异,各蚕区的重点气候因子又各不相同;利用气候因子所建立的方程,与年茧量、亩桑产茧量的实际值及变化规律有很好的拟合度．     

Novel epidemiological model of gastrointestinal nematode infection to assess grazing cattle resilience by integrating host growth,parasite, grass and environmental dynamics

Gastrointestinal nematode (GIN) infections are ubiquitous and often cause morbidity and reduced performance in livestock. Emerging anthelmintic resistance and increasing change in climate patterns require evaluation of alternatives to traditional treatment and management practices. Mathematical models of parasite transmission between hosts and the environment have contributed towards the design of appropriate control strategies in ruminants, but have yet to account for relationships between climate, infection pressure, immunity, resources, and growth. Here, we develop a new epidemiological model of GIN transmission in a herd of grazing cattle, including host tolerance (body weight and feed intake), parasite burden and acquisition of immunity, together with weather-dependent development of parasite free-living stages, and the influence of grass availability on parasite transmission. Dynamic host, parasite and environmental factors drive a variable rate of transmission. Using literature sources, the model was parametrised for Ostertagia ostertagi, the prevailing pathogenic GIN in grazing cattle populations in temperate climates. Model outputs were validated on published empirical studies from first season grazing cattle in northern Europe. These results show satisfactory qualitative and quantitative performance of the model; they also indicate the model may approximate the dynamics of grazing systems under co-infection by O. ostertagi and Cooperia oncophora, a second GIN species common in cattle. In addition, model behaviour was explored under illustrative anthelmintic treatment strategies, considering impacts on parasitological and performance variables. The model has potential for extension to explore altered infection dynamics as a result of management and climate change, and to optimise treatment strategies accordingly. As the first known mechanistic model to combine parasitic and free-living stages of GIN with host feed-intake and growth, it is well suited to predict complex system responses under non-stationary conditions. We discuss the implications, limitations and extensions of the model, and its potential to assist in the development of sustainable parasite control strategies.     

Integrating Homo sapiens into ecological models: Imperatives of climate change

In the opening lecture at a 2013 Banff International Research Station (BIRS) workshop on the impact of climate change on biological invasions and population distributions, Henri Berestycki (École des Hautes Études en Sciences Sociales) asked a crucial question: Can a species keep pace with a changing climate? “Species” in this context was generally understood to be all living things on Earth (except humans). But mounting scientific evidence suggests that it is time to pose the parallel question: Can Homo sapiens keep pace with a changing climate? Furthermore, should we merely “keep pace”, or should we strive to get ahead and then do our utmost to stop any further climate change?In this paper we document the very real potential for climate change to have devastating consequences before the end of this century. The urgency of the situation calls for concerted action by anyone who understands the problem, and mathematical ecologists are uniquely trained to contribute to such efforts. We ask modellers to deliberately incorporate the species H. sapiens into their modelling work, and offer suggestions as to how this might be done. Ultimately modellers must seek ways to provide guidance to citizens and policy-makers as we all wrestle with the most important existential threat of our time.     

Predicted short-term radial-growth changes of trees based on past climate on Vancouver Island, British Columbia

Tree-ring radial expansion estimator (TREE) is an integrated radial growth model that allows users to define short-term climate change scenarios to anticipate the impact upon mature trees found growing at high elevation on Vancouver Island, British Columbia. Five individualistic models were built to represent the radial growth behaviour of mountain hemlock (Tsuga mertensiana (Bong.) Carr), yellow-cedar (Chamaecyparis nootkatensis (D. Don) Spach), western red-cedar (Thuja plicata Donn), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), and western hemlock (Tsuga heterophylla (Raf.) Sarg.) trees. The models were developed on climate-radial growth relationships incorporating Nanaimo climate station data, and were able to explain from 55 to 68 per cent of the variance in radial growth. The models can be run with modifications to yearly precipitation and temperature variables, giving the user the ability to investigate the radial-growth impacts of a wide range of possible climate change scenarios. Results from eight such scenarios show that species growing within their ecological limits illustrate a limited change in radial growth to forecasted climate, while species growing at an ecotonal boundary are usually very sensitive to a specific climate variables (e.g., July temperature). A forecasted alteration to this key variable will then radically alter the radial-growth rate of the species.     

Future wildfire in circumboreal forests in relation to global warming

Abstract. Despite increasing temperatures since the end of the Little Ice Age (ca. 1850), wildfire frequency has decreased as shown in many field studies from North America and Europe. We believe that global warming since 1850 may have triggered decreases in fire frequency in some regions and future warming may even lead to further decreases in fire frequency. Simulations of present and future fire regimes, using daily outputs from the General Circulation Model (GCM), were in good agreement with recent trends observed in fire history studies. Daily data, rather than monthly data, were used because the weather and, consequently, fire behavior can change dramatically over time periods much shorter than a month. The simulation and fire history results suggest that the impact of global warming on northern forests through forest fires may not be disastrous and that, contrary to the expectation of an overall increase in forest fires, there may be large regions of the Northern Hemisphere with a reduced fire frequency.     

利用花粉-气候响应面恢复察素齐泥炭剖面全新世古气候的尝试

利用花粉气候响应面模型进行古气候重建是通过将化石花粉数据与花粉气候响应面模型进行相似性对比分析来实现的,研究结果表明,在内蒙古中部地区１万年来经历了凉湿期→冷暖干湿剧烈波动期,且有凉湿、温干的气候组合→全新世温暖期→气候波动期的气候变化。经与传统、常规直观分析方法对比,利用响应面模型恢复的古气候数据基本上与采用常规方法得到的结论相符,但它能够提供更多的气候变迁细节,且能提供定量的古气候数据,便于据此检验全球变化模型的可靠性及可信度。据此可以认为,利用响应面恢复古气候是很有前途的一种新方法,对于两种方法所得结论的矛盾之处,还需要在增加用于建模的表土花粉数据的基础上继续进行研究     

Temperature is the key factor explaining interannual variability of Daphnia development in spring: a modelling study

Plankton succession during spring/early summer in temperate lakes is characterised by a highly predictable pattern: a phytoplankton bloom is grazed down by zooplankton (Daphnia) inducing a clear-water phase. This sequence of events is commonly understood as a cycle of consumer-resource dynamics, i.e. zooplankton growth is driven by food availability. Here we suggest, using a modelling study based on a size-structured Daphnia population model, that temperature and not food is the dominant factor driving interannual variability of Daphnia population dynamics during spring. Simply forcing this model with a seasonal temperature regime typical for temperate lakes is sufficient for generating the distinctive seasonal trajectory of Daphnia abundances observed in meso-eutrophic temperate lakes. According to a scenario analysis, a forward shift of the vernal temperature increase by 60 days will advance the timing of the Daphnia maximum on average by 54 days, while a forward shift in the start of the spring bloom by 60 days will advance the Daphnia maximum only by less than a third (17 days). Hence, the timing of temperature increase was more important for the timing of Daphnia development than the timing of the onset of algal growth. The effect of temperature is also large compared to the effect of applying different Daphnia mortality rates (0.055 or 0.1 day(-1), 38 days), an almost tenfold variation in phytoplankton carrying capacity (25 days) and a tenfold variation in Daphnia overwintering abundance (3 days). However, the standing stock of Daphnia at its peak was almost exclusively controlled by the phytoplankton carrying capacity of the habitat and seems to be essentially independent of temperature. Hence, whereas food availability determines the standing stock of Daphnia at its spring maximum, temperature appears to be the most important factor driving the timing of the Daphnia maximum and the clear-water phase in spring.     

Stochastic simulation of daily air temperature and precipitation from monthly normals in North America north of Mexico

A simple, stochastic daily temperature and precipitation generator (TEMPGEN) was developed to generate inputs for the study of the effects of climate change on models driven by daily weather information when climate data are available as monthly summaries. The model uses as input only 11 sets of monthly normal statistics from individual weather stations. It needs no calibration, and was parameterized and validated for use in Canada and the continental United States. Monthly normals needed are: mean and standard deviation of daily minimum and maximum temperature, first and second order autoregressive terms for daily deviations of minimum and maximum temperatures from their daily means, correlation of deviations of daily minimum and maximum temperatures, total precipitation, and the interannual variance of total precipitation. The statistical properties and distributions of daily temperature and precipitation data produced by this generator compared quite favorably with observations from 708 stations throughout North America (north of Mexico). The algorithm generates realistic seasonal patterns, variability and extremes of temperature, precipitation, frost-free periods and hot spells. However, it predicts less accurately the daily probability of precipitation, extreme precipitation events and the duration of extreme droughts.     

Where will species on the move go? Insights from climate connectivity modelling across European terrestrial habitats

Climate change can induce species range shifts. However, the intensity of climate change, the intrinsic dispersal ability of species and the anthropization of landscapes are impeding species movements in most cases. In this context, preserving and promoting climate corridors for species to migrate from their current habitats to their future climatically similar habitats is an important strategy for preventing species extinction. Climate connectivity modelling is a tool that can identify these potential movement pathways. Here, we aimed to model connectivity between climate analogues across Europe under various ecological assumptions and climate change scenarios, in order to identify areas of high potential connectivity and to quantify variation in connectivity across a range of hypotheses. We also overlapped connectivity maps with protected areas to determine whether climate connectivity was sufficiently protected. We showed that climatic connectivity did not differ much between different scenarios of climate change, but was strongly dependent on species’ dispersal assumptions. It was also relatively similar to a scenario of non-climatic connectivity. Therefore, it may be feasible to anticipate the effect of climate change on species movements regardless of the future trajectory of climate, but the implementation of protection strategies for multiple species will certainly prove complex. Overall, protected areas were located in the regions of high and stable connectivity, but some countries lack the appropriate protection schemes, especially regarding strong protections. Our results have the potential to serve in the construction of land cover change scenarios to identify the best strategies to improve climate connectivity.     

气候变化对5种植物分布的潜在影响

利用CART(分类和回归树Classification and regression tree)模型,采用A2和B2气候情景,模拟分析了气候变化对观光木(Tsoongiodendron odorum)、鹅掌楸(Liriodendron chinense)、独叶草(Kingdonia uniflora)、草苁蓉(Boschniakia rossica)和刺五加(Acanthopanax senticosus)分布范围及空间格局的影响。结果表明:与目前适宜分布范围相比,气候发生变化后,观光木的分布范围变化不大,其它植物则缩小;观光木的新适宜与总适宜分布范围扩大,而其它植物缩小;草丛蓉和刺五加的目前适宜、新适宜及总适宜分布范围缩小幅度较大,鹅掌楸和独叶草次之;观光木和鹅掌楸向目前适宜分布区北部区域扩展,独叶草向西南区域扩展,草丛蓉和刺五加的适宜范围在2081～2100年会散失。气候变化下,这些植物的适宜分布范围随年降水量和年均气温的变化不一致,观光木的目前适宜、独叶草的新适宜及总适宜分布范围与年降水量和年均气温,以及草苁蓉的分布范围与降水量的相关性不显著(P0.05)。这些植物的空间分布格局将随气候变化而发生变化。     

One-dimensional daisyworld: spatial interactions and pattern formation

The zero-dimensional daisyworld model of Watson and Lovelock (1983) demonstrates that life can unconsciously regulate a global environment. Here that model is extended to one dimension, incorporating a distribution of incoming solar radiation and diffusion of heat consistent with a spherical planet. Global regulatory properties of the original model are retained. The daisy populations are initially restricted to hospitable regions of the surface but exert both global and local feedback to increase this habitable area, eventually colonizing the whole surface. The introduction of heat diffusion destabilizes the coexistence equilibrium of the two daisy types. In response, a striped pattern consisting of blocks of all black or all white daisies emerges. There are two mechanisms behind this pattern formation. Both are connected to the stability of the system and an overview of the mathematics involved is presented. Numerical experiments show that this pattern is globally determined. Perturbations in one region have an impact over the whole surface but the regulatory properties of the system are not compromised by transient perturbations. The relevance of these results to the Earth and the wider climate modelling field is discussed.     

Heritability of hsp70 expression in the beetle Tenebrio molitor: Ontogenetic and environmental effects

Ectotherms constitute the vast majority of terrestrial biodiversity and are especially likely to be vulnerable to climate warming because their basic physiological functions such as locomotion, growth, and reproduction are strongly influenced by environmental temperature. An integrated view about the effects of global warming will be reached not just establishing how the increase in mean temperature impacts the natural populations but also establishing the effects of the increase in temperature variance. One of the molecular responses that are activated in a cell under a temperature stress is the heat shock protein response (HSP). Some studies that have detected consistent differences among thermal treatments and ontogenetic stages in HSP70 expression have assumed that these differences had a genetic basis and consequently expression would be heritable. We tested for changes in quantitative genetic parameters of HSP70 expression in a half-sib design where individuals of the beetle Tenebrio molitor were maintained in constant and varying thermal environments. We estimated heritability of HSP70 expression using a linear mixed modelling approach in different ontogenetic stages. Expression levels of HSP70 were consistently higher in the variable environment and heritability estimates were low to moderate. The results imply that within each ontogenetic stage additive genetic variance was higher in the variable environment and in adults compared with constant environment and larvae stage, respectively. We found that almost all the genetic correlations across ontogenetic stages and environment were positive. These suggest that directional selection for higher levels of expression in one environment will result in higher expression levels of HSP70 on the other environment for the same ontogenetic stage.     

On the degradation of forest ecosystems by extreme events: Statistical Model Checking of a hybrid model

In this paper, we study the vulnerability of forest ecosystems perturbed by extreme events, such as those arising from climate change. To investigate the complex interactions between the biological dynamics of the forest and the climatic activity, we construct an original hybrid model, obtained by coupling a continuous reaction–diffusion system, which describes the spatio-temporal dynamics of the forest ecosystem, with a discrete probabilistic process, which models the possible occurrences of extreme events. Properties of ecological interest are considered: invariance of the persistence equilibrium, attraction to the extinction equilibrium and emergence of degraded states. Those properties of the hybrid model are verified through an extension of the Statistical Model Checking framework. We establish the existence of a threshold above which the persistence equilibrium of the forest ecosystem is compromised and give a numerical assessment of this threshold in terms of the probability and intensity of extreme events. We also present non-trivial parameter conditions for which the forest ecosystem converges to a degraded savanna-like state.     

Advance in a terrestrial biogeochemical model—DNDC model

Global climate change is one of the most important issues of contemporary environmental safety. Quantifying regional or global greenhouse gas (GHG) emissions and searching for appropriate mitigation measures have become a relatively hot issue in international global climate change studies. The high temporal and spatial variability of GHG emissions from soils makes their field measurement at regional or national scales impractical. To develop emission factors for a wide range of management practices such as those given in the Intergovernmental Panel on Climate Change Tier I methodology are often considered as a convenient technique to estimate emissions, but these can result in substantial errors when applied to specific geographical regions. Accordingly, considering the complexity of greenhouse gas production in soils, process-based models are required to quantify and predict the GHG emissions, and also interpret the intricate relationships among the gas emissions, the environmental factors and the ecological drivers. Several detailed biogeochemical process-based models of GHG emissions have been developed and accepted in recent years to provide regional scale estimate of GHG emissions and assess the mitigation measures. Among these the DNDC (Denitrification–Decomposition) model, as a process-based biogeochemical model, is capable of predicting the soil fluxes of all three terrestrial greenhouse gases: nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄), as well as other important environmental and economic indicators such as crop production, ammonia (NH₃) volatilisation and nitrate NO₃^- leaching. Originally developed as a tool to simulate GHG emissions generated from agro-ecosystem, DNDC has since been expanded to include ecosystems such as rice paddies, grazed pastures, forests, and wetlands, and the model has attracted worldwide attention to simulate carbon and nitrogen biogeochemical cycles occurring in global ecosystems. This paper introduces the scientific basis underlying the modeling of greenhouse gas emissions from terrestrial soils, brings together the worldwide research undertaken on a wide range of ecosystems to test and verify, improve and modify, and apply the DNDC model to estimate GHG emissions from these systems, and furtherly sums up the advantages and disadvantages of the model for providing a reference for the application and development of the model. Most studies showed that there was a good agreement between the simulated and observed values of CO₂, CH₄ and N₂O emissions from arable, forest and grassland fields at different geographical locations over the world. However, some discrepancies still existed between observed and simulated seasonal patterns of CH₄ and N₂O emissions. Moreover, the DNDC model was mainly tested against experimental data on GHG emissions, but there were a few validations on NO₃^- leaching, soil water dynamics, NH₃ volatilisation which could greatly impact the GHG emissions. With the high development of society and economy, China had been facing a huge challenge between food production and environmental protection. Therefore, it was an urgent task to search optimal measures for optimizing land resource use, increasing crop productivity and reducing adverse environmental impacts. Process-based biogeochemical modeling, as with DNDC, can help in identifying optimal strategies to meet the needs. In future, the DNDC model need to not only improve the capability of predicting the GHG emissions, but also the accuracy of simulating the NO₃^- leaching and soil water dynamics for quantifying the non-point source pollution through modifying the parameters of the model or combining with other models, as SWAT model. The DNDC model will play more and more important role in future studies on global change.     

Advance in a terrestrial biogeochemical model—DNDC model

Global climate change is one of the most important issues of contemporary environmental safety. Quantifying regional or global greenhouse gas (GHG) emissions and searching for appropriate mitigation measures have become a relatively hot issue in international global climate change studies. The high temporal and spatial variability of GHG emissions from soils makes their field measurement at regional or national scales impractical. To develop emission factors for a wide range of management practices such as those given in the Intergovernmental Panel on Climate Change Tier I methodology are often considered as a convenient technique to estimate emissions, but these can result in substantial errors when applied to specific geographical regions. Accordingly, considering the complexity of greenhouse gas production in soils, process-based models are required to quantify and predict the GHG emissions, and also interpret the intricate relationships among the gas emissions, the environmental factors and the ecological drivers. Several detailed biogeochemical process-based models of GHG emissions have been developed and accepted in recent years to provide regional scale estimate of GHG emissions and assess the mitigation measures. Among these the DNDC (Denitrification–Decomposition) model, as a process-based biogeochemical model, is capable of predicting the soil fluxes of all three terrestrial greenhouse gases: nitrous oxide (N₂O), carbon dioxide (CO₂), and methane (CH₄), as well as other important environmental and economic indicators such as crop production, ammonia (NH₃) volatilisation and nitrate NO₃^- leaching. Originally developed as a tool to simulate GHG emissions generated from agro-ecosystem, DNDC has since been expanded to include ecosystems such as rice paddies, grazed pastures, forests, and wetlands, and the model has attracted worldwide attention to simulate carbon and nitrogen biogeochemical cycles occurring in global ecosystems. This paper introduces the scientific basis underlying the modeling of greenhouse gas emissions from terrestrial soils, brings together the worldwide research undertaken on a wide range of ecosystems to test and verify, improve and modify, and apply the DNDC model to estimate GHG emissions from these systems, and furtherly sums up the advantages and disadvantages of the model for providing a reference for the application and development of the model. Most studies showed that there was a good agreement between the simulated and observed values of CO₂, CH₄ and N₂O emissions from arable, forest and grassland fields at different geographical locations over the world. However, some discrepancies still existed between observed and simulated seasonal patterns of CH₄ and N₂O emissions. Moreover, the DNDC model was mainly tested against experimental data on GHG emissions, but there were a few validations on NO₃^- leaching, soil water dynamics, NH₃ volatilisation which could greatly impact the GHG emissions. With the high development of society and economy, China had been facing a huge challenge between food production and environmental protection. Therefore, it was an urgent task to search optimal measures for optimizing land resource use, increasing crop productivity and reducing adverse environmental impacts. Process-based biogeochemical modeling, as with DNDC, can help in identifying optimal strategies to meet the needs. In future, the DNDC model need to not only improve the capability of predicting the GHG emissions, but also the accuracy of simulating the NO₃^- leaching and soil water dynamics for quantifying the non-point source pollution through modifying the parameters of the model or combining with other models, as SWAT model. The DNDC model will play more and more important role in future studies on global change.     

A case study in resort climatology of Phoenix, Arizona, USA

Tourists often use weather data as a factor for determining vacation timing and location. Accuracy and perceptions of weather information may impact these decisions. This study: (a) examines air temperature and dew points from seven exclusive resorts in the Phoenix metropolitan area and compares them with official National Weather Service data for the same period, and (b) utilizes a comfort model called OUTCOMES—OUTdoor COMfort Expert System—in a seasonal appraisal of two resorts, one mesic and one xeric, compared with the urban Sky Harbor International Airport first-order weather station site in the central urban area of Phoenix, Arizona, USA (lat. 33.43°N; long. 112.02°W; elevation at 335 m). Temperature and humidity recording devices were placed within or immediately adjacent to common-use areas of the resorts, the prime recreational sites used by guests on most resort properties. Recorded data were compared with that of the official weather information from the airport station, a station most accessible to potential tourists through media and Web sites, to assess predicted weather for vacation planning. For the most part, Sky Harbor’s recorded air temperatures and often dew points were higher than those recorded at the resorts. We extrapolate our findings to a year-round estimate of human outdoor comfort for weather-station sites typical of resort landscapes and the Sky Harbor location using the OUTCOMES model to refine ideas on timing of comfortable conditions at resorts on a diurnal and seasonal basis.