Climate Change Influences on Environmental and Human Health Chemical Standards

Regulatory agencies use many chemical standards to help protect human health and the environment. Some of these standards could potentially be adversely influenced by climate change, so it is important to scope the likelihood of such changes, and develop a plan for addressing them. A review of the literature showed that many complex interactions could occur between climate change factors, chemicals, and receptors. However, the way in which chemical standards are currently set is already likely to take into account predicted climate changes in temperate regions, at least implicitly. In contrast, compliance monitoring to ensure that chemical standards are not breached may be compromised if climate change leads to more variability in measured parameters, with a consequent reduction in the statistical power of monitoring programs.     

Incorporating Climate Change and Exotic Species into Forecasts of Riparian Forest Distribution

We examined the impact climate change (CC) will have on the availability of climatically suitable habitat for three native and one exotic riparian species. Due to its increasing prevalence in arid regions throughout the western US, we predicted that an exotic species, Tamarix, would have the greatest increase in suitable habitat relative to native counterparts under CC. We used an ecological niche model to predict range shifts of Populus fremontii, Salix gooddingii, Salix exigua and Tamarix, from present day to 2080s, under five general circulation models and one climate change scenario (A1B). Four major findings emerged. 1) Contrary to our original hypothesis, P. fremontii is projected to have the greatest increase in suitable habitat under CC, followed closely by Tamarix. 2) Of the native species, S. gooddingii and S. exigua showed the greatest loss in predicted suitable habitat due to CC. 3) Nearly 80 percent of future P. fremontii and Salix habitat is predicted to be affected by either CC or Tamarix by the 2080s. 4) By the 2080s, 20 percent of S. gooddingii habitat is projected to be affected by both Tamarix and CC concurrently, followed by S. exigua (19 percent) and P. fremontii (13 percent). In summary, while climate change alone will negatively impact both native willow species, Tamarix is likely to affect a larger portion of all three native species'' distributions. We discuss these and other results in the context of prioritizing restoration and conservation efforts to optimize future productivity and biodiversity. As we are accounting for only direct effects of CC and Tamarix on native habitat, we present a possible hierarchy of effects- from the direct to the indirect- and discuss the potential for the indirect to outweigh the direct effects. Our results highlight the need to account for simultaneous challenges in the face of CC.     

Effects of Climate Change on Birds

2010, Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK

344 pages, hardcover or paperback, 75 black-and-white illustrations, and a four-page color plate section

ISBN 978-0-19-956974-8 (hardcover), 978-0-19-956975-5 (softcover). Price R575     

Anthropogenic Environmental Change, Mosquito-borne Diseases and Human Health in New Zealand

Anthropogenic environmental change is acknowledged as a primary factor in the emergence of diseases. New Zealand’s history since human occupation has been characterized by extensive anthropogenic disturbance and the establishment of numerous exotic species. As a result, highly modified habitats have been created, which, in conjunction with introduced organisms, provide suitable conditions for the establishment of exotic systems likely to lead to outbreaks of mosquito-borne disease. The interactions among some disturbance factors in the New Zealand environment are discussed, and the potential public health implications.     

Climate Change Influences on the Global Potential Distribution of Bluetongue Virus

The geographic distribution of arboviruses has received considerable attention after several dramatic emergence events around the world. Bluetongue virus (BTV) is classified among category “A” diseases notifiable to the World Organization of Animal Health (OIE), and is transmitted among ruminants by biting midges of the genus Culicoides. Here, we developed a comprehensive occurrence data set to map the current distribution, estimate the ecological niche, and explore the future potential distribution of BTV globally using ecological niche modeling and based on diverse future climate scenarios from general circulation models (GCMs) for four representative concentration pathways (RCPs). The broad ecological niche and potential geographic distribution of BTV under present-day conditions reflected the disease’s current distribution across the world in tropical, subtropical, and temperate regions. All model predictions were significantly better than random expectations. As a further evaluation of model robustness, we compared our model predictions to 331 independent records from most recent outbreaks from the Food and Agriculture Organization Emergency Prevention System for Transboundary Animal and Plant Pests and Diseases Information System (EMPRES-i); all were successfully anticipated by the BTV model. Finally, we tested ecological niche similarity among possible vectors and BTV, and could not reject hypotheses of niche similarity. Under future-climate conditions, the potential distribution of BTV was predicted to broaden, especially in central Africa, United States, and western Russia.     

The Effects of Weather and Climate Change on Dengue

Background

There is much uncertainty about the future impact of climate change on vector-borne diseases. Such uncertainty reflects the difficulties in modelling the complex interactions between disease, climatic and socioeconomic determinants. We used a comprehensive panel dataset from Mexico covering 23 years of province-specific dengue reports across nine climatic regions to estimate the impact of weather on dengue, accounting for the effects of non-climatic factors.

Methods and Findings

Using a Generalized Additive Model, we estimated statistically significant effects of weather and access to piped water on dengue. The effects of weather were highly nonlinear. Minimum temperature (Tmin) had almost no effect on dengue incidence below 5°C, but Tmin values above 18°C showed a rapidly increasing effect. Maximum temperature above 20°C also showed an increasing effect on dengue incidence with a peak around 32°C, after which the effect declined. There is also an increasing effect of precipitation as it rose to about 550 mm, beyond which such effect declines. Rising access to piped water was related to increasing dengue incidence. We used our model estimations to project the potential impact of climate change on dengue incidence under three emission scenarios by 2030, 2050, and 2080. An increase of up to 40% in dengue incidence by 2080 was estimated under climate change while holding the other driving factors constant.

Conclusions

Our results indicate that weather significantly influences dengue incidence in Mexico and that such relationships are highly nonlinear. These findings highlight the importance of using flexible model specifications when analysing weather–health interactions. Climate change may contribute to an increase in dengue incidence. Rising access to piped water may aggravate dengue incidence if it leads to increased domestic water storage. Climate change may therefore influence the success or failure of future efforts against dengue.     

Incorporating Field Studies into Species Distribution and Climate Change Modelling: A Case Study of the Koomal Trichosurus vulpecula hypoleucus (Phalangeridae)

Species distribution models (SDMs) are an effective way of predicting the potential distribution of species and their response to environmental change. Most SDMs apply presence data to a relatively generic set of predictive variables such as climate. However, this weakens the modelling process by overlooking the responses to more cryptic predictive variables. In this paper we demonstrate a means by which data gathered from an intensive animal trapping study can be used to enhance SDMs by combining field data with bioclimatic modelling techniques to determine the future potential distribution for the koomal (Trichosurus vulpecula hypoleucus). The koomal is a geographically isolated subspecies of the common brushtail possum, endemic to south-western Australia. Since European settlement this taxon has undergone a significant reduction in distribution due to its vulnerability to habitat fragmentation, introduced predators and tree/shrub dieback caused by a virulent group of plant pathogens of the genus Phytophthora. An intensive field study found: 1) the home range for the koomal rarely exceeded 1 km in in length at its widest point; 2) areas heavily infested with dieback were not occupied; 3) gap crossing between patches (>400 m) was common behaviour; 4) koomal presence was linked to the extent of suitable vegetation; and 5) where the needs of koomal were met, populations in fragments were demographically similar to those found in contiguous landscapes. We used this information to resolve a more accurate SDM for the koomal than that created from bioclimatic data alone. Specifically, we refined spatial coverages of remnant vegetation and dieback, to develop a set of variables that we combined with selected bioclimatic variables to construct models. We conclude that the utility value of an SDM can be enhanced and given greater resolution by identifying variables that reflect observed, species-specific responses to landscape parameters and incorporating these responses into the model.     

Contrasting Effects of Climate Change on Alpine Chamois

Global climate change can affect animal ecology in numerous ways, but researchers usually emphasize undesirable consequences. Temperature increases, for instance, can induce direct physiological costs and indirect effects via mismatches in resource needs and availability. Species living in mountainous regions, however, could experience beneficial effects because winters might become less severe. We examined the potentially opposing effects of climate change during spring, summer, and winter on recruitment in Alpine chamois (Rupicapra rupicapra). We examined initial recruitment (i.e., the ratio of kids to adult females) and net recruitment (i.e., the ratio of yearlings to adult females) of Alpine chamois through the use of linear mixed effects models and data from block count censuses performed across a 1,500-km² study area in the Italian Alps during summer from 2001 to 2015. Initial recruitment was relatively resistant to the effects of climate change, declining slightly over the study period. We suggest that the effects of increased forage availability and lower snow cover in winter may benefit the reproductive output of adult females, compensating for any negative effects of trophic mismatch and higher temperatures during summer. By contrast, net recruitment strongly declined throughout the study period, consistent with the slight decline of initial recruitment and the negative effects of increasing summer temperatures on the survival of kids during their first winter. These negative effects seemed to outweigh positive effects of climate change, even in a species strongly challenged by winter conditions. These findings provide important information for hunted populations; setting more appropriate hunting bags for yearling chamois should be considered. The ecological plasticity of the chamois, which also inhabits low altitudes, may allow a possible evolutionary escape for the species. © 2020 The Wildlife Society.     

黑龙江省未来气候生态与森林的作用研究

本文在扼要概述全球气候变化背景的基础上,根据近百年气候历史资料以及海温、太阳黑子活动、火山爆发、ＣＯ２浓度变化、Ｏ３浓度变化、大气环流指数特征等因子与我省气修变化的定量与定性关系,分析了各因子的综合影响,预测了我省未来十年气候变化趋势。结果表明：我省未来十年气候以温暖、干旱为主,年际间仍存有波动。虽然总的气候变化趋势与全球变化是一致的,但具有明显的区域特征。同时提出了气候变化可能对森林的影响,初步探讨了应采取的森林对策。     

Incorporating Disease and Population Structure into Models of SIR Disease in Contact Networks

We consider the recently introduced edge-based compartmental models (EBCM) for the spread of susceptible-infected-recovered (SIR) diseases in networks. These models differ from standard infectious disease models by focusing on the status of a random partner in the population, rather than a random individual. This change in focus leads to simple analytic models for the spread of SIR diseases in random networks with heterogeneous degree. In this paper we extend this approach to handle deviations of the disease or population from the simplistic assumptions of earlier work. We allow the population to have structure due to effects such as demographic features or multiple types of risk behavior. We allow the disease to have more complicated natural history. Although we introduce these modifications in the static network context, it is straightforward to incorporate them into dynamic network models. We also consider serosorting, which requires using dynamic network models. The basic methods we use to derive these generalizations are widely applicable, and so it is straightforward to introduce many other generalizations not considered here. Our goal is twofold: to provide a number of examples generalizing the EBCM method for various different population or disease structures and to provide insight into how to derive such a model under new sets of assumptions.     

Deforestation Induced Climate Change: Effects of Spatial Scale

Deforestation is associated with increased atmospheric CO₂ and alterations to the surface energy and mass balances that can lead to local and global climate changes. Previous modelling studies show that the global surface air temperature (SAT) response to deforestation depends on latitude, with most simulations showing that high latitude deforestation results in cooling, low latitude deforestation causes warming and that the mid latitude response is mixed. These earlier conclusions are based on simulated large scal land cover change, with complete removal of trees from whole latitude bands. Using a global climate model we examine the effects of removing fractions of 5% to 100% of forested areas in the high, mid and low latitudes. All high latitude deforestation scenarios reduce mean global SAT, the opposite occurring for low latitude deforestation, although a decrease in SAT is simulated over low latitude deforested areas. Mid latitude SAT response is mixed. In all simulations deforested areas tend to become drier and have lower SAT, although soil temperatures increase over deforested mid and low latitude grid cells. For high latitude deforestation fractions of 45% and above, larger net primary productivity, in conjunction with colder and drier conditions after deforestation cause an increase in soil carbon large enough to produce a net decrease of atmospheric CO₂. Our results reveal the complex interactions between soil carbon dynamics and other climate subsystems in the energy partition responses to land cover change.     

Effects of Climate Change on Plant Population Growth Rate and Community Composition Change

The impacts of climate change on forest community composition are still not well known. Although directional trends in climate change and community composition change were reported in recent years, further quantitative analyses are urgently needed. Previous studies focused on measuring population growth rates in a single time period, neglecting the development of the populations. Here we aimed to compose a method for calculating the community composition change, and to testify the impacts of climate change on community composition change within a relatively short period (several decades) based on long-term monitoring data from two plots—Dinghushan Biosphere Reserve, China (DBR) and Barro Colorado Island, Panama (BCI)—that are located in tropical and subtropical regions. We proposed a relatively more concise index, Slnλ, which refers to an overall population growth rate based on the dominant species in a community. The results indicated that the population growth rate of a majority of populations has decreased over the past few decades. This decrease was mainly caused by population development. The increasing temperature had a positive effect on population growth rates and community change rates. Our results promote understanding and explaining variations in population growth rates and community composition rates, and are helpful to predict population dynamics and population responses to climate change.     

Sleep Deprivation Influences Diurnal Variation of Human Time Perception with Prefrontal Activity Change: A Functional Near-Infrared Spectroscopy Study

Human short-time perception shows diurnal variation. In general, short-time perception fluctuates in parallel with circadian clock parameters, while diurnal variation seems to be modulated by sleep deprivation per se. Functional imaging studies have reported that short-time perception recruits a neural network that includes subcortical structures, as well as cortical areas involving the prefrontal cortex (PFC). It has also been reported that the PFC is vulnerable to sleep deprivation, which has an influence on various cognitive functions. The present study is aimed at elucidating the influence of PFC vulnerability to sleep deprivation on short-time perception, using the optical imaging technique of functional near-infrared spectroscopy. Eighteen participants performed 10-s time production tasks before (at 21:00) and after (at 09:00) experimental nights both in sleep-controlled and sleep-deprived conditions in a 4-day laboratory-based crossover study. Compared to the sleep-controlled condition, one-night sleep deprivation induced a significant reduction in the produced time simultaneous with an increased hemodynamic response in the left PFC at 09:00. These results suggest that activation of the left PFC, which possibly reflects functional compensation under a sleep-deprived condition, is associated with alteration of short-time perception.