Incorporating physiology into species distribution models moderates the projected impact of warming on selected Mediterranean marine species

Species distribution models (SDMs) correlate species occurrences with environmental predictors, and can be used to forecast distributions under future climates. SDMs have been criticized for not explicitly including the physiological processes underlying the species response to the environment. Recently, new methods have been suggested to combine SDMs with physiological estimates of performance (physiology-SDMs). In this study, we compare SDM and physiology-SDM predictions for select marine species in the Mediterranean Sea, a region subjected to exceptionally rapid climate change. We focused on six species and created physiology-SDMs that incorporate physiological thermal performance curves from experimental data with species occurrence records. We then contrasted projections of SDMs and physiology-SDMs under future climate (year 2100) for the entire Mediterranean Sea, and particularly the ‘warm’ trailing edge in the Levant region. Across the Mediterranean, we found cross-validation model performance to be similar for regular SDMs and physiology-SDMs. However, we also show that for around half the species the physiology-SDMs substantially outperform regular SDM in the warm Levant. Moreover, for all species the uncertainty associated with the coefficients estimated from the physiology-SDMs were much lower than in the regular SDMs. Under future climate, we find that both SDMs and physiology-SDMs showed similar patterns, with species predicted to shift their distribution north-west in accordance with warming sea temperatures. However, for the physiology-SDMs predicted distributional changes are more moderate than those predicted by regular SDMs. We conclude, that while physiology-SDM predictions generally agree with the regular SDMs, incorporation of the physiological data led to less extreme range shift forecasts. The results suggest that climate-induced range shifts may be less drastic than previously predicted, and thus most species are unlikely to completely disappear with warming climate. Taken together, the findings emphasize that physiological experimental data can provide valuable supplemental information to predict range shifts of marine species.     

Phenological delay despite warming in wood frog Rana sylvatica reproductive timing: a 20-year study

Across all taxa, amphibians exhibit some of the strongest phenological shifts in response to climate change. As climates warm, amphibians and other animals are expected to breed earlier in response to temperature cues. However, if species use fixed cues such as daylight, their breeding timing might remain fixed, potentially creating disconnects between their life history and environmental conditions. Wood frogs Rana sylvatica are a cold-adapted species that reproduce in early spring, immediately after breeding ponds are free of ice. We used long-term surveys of wood frog oviposition timing in 64 breeding ponds over 20 yr to show that, despite experiencing a warming of 0.29°C per decade in annual temperature, wood frog breeding phenology has shifted later by 2.8 d since 2000 (1.4 d per decade; 4.8 d per °C). This counterintuitive pattern is likely the result of changes in the timing of snowpack accumulation and melting. Finally, we used relationships between climate and oviposition between 2000 and 2018 to hindcast oviposition dates from climate records to model longer-term trends since 1980. Our study indicates that species can respond to fine-grained seasonal climate heterogeneity within years that is not apparent or counterintuitive when related to annual trends across years.     

未来气候变化对四种木姜子地理分布的影响

该文利用最大墒模型(Maxent)和地理信息系统(ArcGIS 10.3)软件对中国木姜子属(Litsea)四种资源植物在我国当代、未来(2061年—2080年)气候条件下的潜在分布区进行预测,并对其适宜区进行分析和划分。结果表明:山鸡椒(Litsea cubeba)适宜区广泛分布在长江以南区域,在未来时段2061年—2080年两种(RCP2.6、RCP8.5)二氧化碳浓度情景下适宜区面积分别减少4.9%和0.5%; 毛豹皮樟(L. coreana)适宜区主要分布在中亚热带及北亚热带区域,分布相对偏北,其在未来2061年—2080年两种二氧化碳浓度情景下适宜区面积分别增加5.6%和4.5%; 华南木姜子(L. greenmaniana)适宜区主要分布在我国南亚热带区域; 毛叶木姜子(L. mollis)适宜区广泛分布在亚热带区域。这两种树种在未来气候RCP2.6情景下适生面积减少1.0%和3.3%,在RCP8.5情景下减少5.6%和8.3%。上述结果说明木姜子属不同种由于生态习性差异对未来的气候变化的响应不尽相同,对这些植物引种栽培须考虑气候变化的影响。     

羌塘国家级自然保护区地表土壤冻结天数时空变化特征

利用1981—2018年羌塘自然保护区周边5个气象台站的地表逐日最低温度和平均气温资料,采用线性回归和Mann-Kendall非参数检验方法,分析了近38 a以及全球变暖1.5℃和2℃阈值时羌塘自然保护区地表土壤冻结天数的时空变化特征。结果表明:(1)近38 a近地表土壤冻结开始日期呈推迟趋势,变化率为7.72 d·10 a^-1,冻结终止日期以8.17d·10 a^-1的速率显著提早;冻结持续时间和冻结天数均呈显著缩短趋势,平均每10年分别缩短14.69和11.19 d;同时段内,自然保护区大部分土壤冻结参数的变化率均大于青藏高原。(2)在年代际变化上,自然保护区呈现土壤冻结开始日期推迟、冻结终止日期提前、冻结持续时间和冻结天数缩短的变化特征。(3)土壤冻结参数在21世纪初均发生了气候突变,较青藏高原土壤冻融时间的突变点偏晚。(4)在全球变暖1.5℃时,RCP4.5和RCP8.5情景下的自然保护区土壤冻结参数变化值相同,冻结开始日期推迟25 d,冻结终止日期提早22 d,冻结持续时间和冻结天数分别缩短46和28 d;变暖2.0℃时,RCP4.5和RCP8.5情景下的土壤冻结开始日期推迟35和33 d,冻结终止日期提早30和29 d,冻结持续时间减少64和62 d,冻结天数缩短40和39 d。     

气候变化和经济发展对肾综合征出血热发生的影响

肾综合征出血热(hemorrhagic fever with renal syndrome, HFRS)是一种啮齿动物传播的自然疫源性疾病, 危害严重, 已成为全球重要的公共卫生问题。本研究采用数理统计模型及小波分析方法, 对陕西省西安市鄠邑区1984-2016年HFRS的发生与鼠类、气候和经济因素的关系进行分析, 探讨气候和经济因素对HFRS发生的影响。小波分析结果表明, 该地区的HFRS暴发史可能分为两个时期, 推测每个时期具有不同的主要宿主, 在2002年褐家鼠(Rattus norvegicus)可能取代黑线姬鼠(Apodemus agrarius)成为HFRS疫源地的主要宿主。广义可加模型模拟结果表明, HFRS的发生与1984-2001年黑线姬鼠密度间存在极显著非线性效应(F_2.06,9.02 = 102.415, P < 0.01), 两者间显现为正相关; 与2002-2016年的褐家鼠密度间呈正相关(F_1.67,9.02 = 73.929, P < 0.01); HFRS主要宿主的这种变化可能与当地气候变化和经济发展有关: HFRS的发生与年平均温度存在极显著的非线性效应(F_2.93,9.02 = 12.164, P < 0.01), 两者间呈负相关; 同样, HFRS的发生与上一年的国内生产总值(GDP)也存在显著非线性效应(F_1.70,9.02 = 2.917, P < 0.05), 两者间也呈负相关。结构方程模型通过直接和间接的影响途径证明了这种转移机制, 发现温度对HFRS发生有显著的直接负向影响以及通过褐家鼠的间接正向影响; GDP对HFRS发生有直接的负向影响。本研究表明HFRS的发生与气候变化和经济发展相关, 两者均能影响HFRS的暴发, 该结论有助于今后更好地对HFRS疾病进行预防和控制。     

松栎林天然更新模拟与不确定性分析

森林天然更新的复杂性和不确定性是森林生态系统动态预测中的关键问题。本研究引入贝叶斯技术和全局敏感性分析,构建基于竞争、气候和地形3类因子的秦岭松栎林天然更新模型。备选模型形式以泊松(Poisson)模型、负二项(negative binomial,NB)模型、零膨胀泊松(zero-inflated Poisson,ZIP)模型和零膨胀负二项(zero-inflated negative binomial,ZINB)模型为基础。同时,根据模型参数传递的不确定性量化分析结果,阐释影响森林更新小概率事件的主导因子。结果表明: ZINB模型在油松和锐齿栎更新模拟中均优于其他模型。林分总断面积、光截留、坡位和生长季最低温是影响松栎林中油松天然更新的最关键因子;而林分总断面积、坡向与海拔的组合、年均温和最热季节降水量则是影响松栎林中锐齿栎天然更新的关键因子。油松更新模拟中,各类因子对模型输出的不确定性贡献率从小到大依次为: 竞争因子(25%)＜气候因子(29%)＜地形因子(46%);锐齿栎更新模拟中为: 气候因子(12%)＜竞争因子(24%)＜地形因子(64%)。油松天然更新数量对生长季最低温和最干季节降水量为正响应,对最干季节均温为负响应;锐齿栎天然更新数量对年均温、生长季最低温和最热季节降水量为正响应,对最干季节均温为负响应。基于贝叶斯技术的ZINB模型可以量化森林更新的影响因子,并解释参数传递的不确定性,是预测森林天然更新的有力工具。     

竞争和气候对新疆阿尔泰山西伯利亚五针松树木径向生长的影响

阿尔泰山的北方森林是中亚以及全球的生态系统的重要组成部分, 其生长动态可以影响到全球范围的热辐射、碳平衡等。因此, 探究影响阿尔泰山树木径向生长的主要因素至关重要。该研究以新疆喀纳斯国家级自然保护区的西伯利亚五针松(Pinus sibirica)为研究对象, 建立西伯利亚五针松年表, 通过分析不同时间间隔累年生长量、竞争指数以及气候因子之间的关系, 运用线性混合效应模型、相关分析等方法, 探究竞争和气候对新疆阿尔泰山西伯利亚五针松树木径向生长的影响。结果表明: (1)线性混合效应模型结果显示竞争树胸径和与西伯利亚五针松过去30年的累年生长量之间的拟合效果最好; (2)标准年表与3月的平均气温、平均最高气温、平均最低气温之间有显著正相关关系; (3)累年生长量最高值出现在气温0-5 ℃, 竞争指数低于100的时候。累年生长量最低时, 气温达到-10 ℃, 竞争指数也超过了300。目标树的树木径向生长受到竞争树胸径和及生长季前期气温的影响, 两者共同作用。但相较于气候因子而言, 竞争对西伯利亚五针松的树木径向生长有更大的影响作用。     

气候变暖背景下青藏高原草本植物物候变化空间换时间预测

气候变暖背景下的植物物候变化广受关注, 然而常用的植物物候变化预测模型未充分考虑植物对环境的适应性, 给预测结果带来了较大的不确定性。该文基于2002-2011年青藏高原10个站点的地面物候观测资料以及年平均气温数据, 对空间换时间模型预测车前(Plantago asiatica)和蒲公英(Taraxacum mongolicum)各主要物候事件(展叶始期、开花始期和黄枯普遍期)变化的可行性及其在升温背景下的变化规律进行了分析。首先利用不同海拔高度的气温和物候事件分别与地理因子(经度、纬度和海拔)建立多元线性回归模型, 然后在此基础上剔除经度和纬度的影响, 单独考察海拔变化所引起的气温与植物物候变化, 最后以海拔高度作为桥梁来考察物候变化与温度变化的关系。结果表明, 采用各站点对应的海拔高度来模拟年平均气温空间差异的R²均大于0.89, 表明海拔梯度可以用来反映时间尺度下的年际温度变化; 车前和蒲公英各物候事件发生日期拟合值均与海拔高度变化关系显著, R²均大于0.70, 表明海拔变化是影响它们各物候事件变化的主要地理因子; 在物候事件发生日期拟合值和年平均气温拟合值的回归方程中, R²均大于0.93, 说明基于不同海拔高度模拟得到的年平均气温变化可以对时间尺度上车前和蒲公英的物候事件变化进行预测。空间换时间预测表明, 温度每升高1 ℃, 车前展叶始期和开花始期分别提前5.1和5.4 d, 而黄枯普遍期推迟4.8 d; 蒲公英展叶始期和开花始期分别提前6.5和7.8 d, 而黄枯普遍期推迟6.7 d。     

Resilience in reef‐building corals: The ecological and evolutionary importance of the host response to thermal stress

Coral reefs are under extreme threat due to a number of stressors, but temperature increases due to changing climate are the most severe. Rising ocean temperatures coupled with local extremes lead to extensive bleaching, where the coral‐algal symbiosis breaks down and corals may die, compromising the structure and function of reefs. Although the symbiotic nature of the coral colony has historically been a focus of research on coral resilience, the host itself is a foundational component in the response to thermal stress. Fixed effects in the coral host set trait baselines through evolutionary processes, acting on many loci of small effect to create mosaics of thermal tolerance across latitudes and individual coral reefs. These genomic differences can be strongly heritable, producing wide variation among clones of different genotypes or families of a specific larval cross. Phenotypic plasticity is overlaid on these baselines and a growing body of knowledge demonstrates the potential for acclimatization of reef‐building corals through a variety of mechanisms that promote resilience and stress tolerance. The long‐term persistence of coral reefs will require many of these mechanisms to adjust to warmer temperatures within a generation, bridging the gap to reproductive events that allow recombination of standing diversity and adaptive change. Business‐as‐usual climate scenarios will probably lead to the loss of some coral populations or species in the future, so the interaction between intragenerational effects and evolutionary pressure is critical for the survival of reefs.     

What processes must we understand to forecast regional-scale population dynamics?

An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.