A bird distribution model for ring recovery data: where do the European robins go?

For the study of migratory connectivity, birds have been individually marked by metal rings for more than 100 years. The resulting ring recovery data have been compiled in numerous bird migration atlases. However, estimation of what proportion of a particular population is migrating to which region is confounded by spatial heterogeneity in ring recovery probability. We present a product multinomial model that enables quantifying the continent‐wide distribution of different bird populations during different seasons based on ring recovery data while accounting for spatial heterogeneity of ring recovery probability. We applied the model to an example data set of the European robin Erithacus rubecula. We assumed that ring recovery probability was equal between different groups of birds and that survival probability was constant. Simulated data indicate that violation of the assumption of constant survival did not affect our estimated bird distribution parameters but biased the estimates for recovery probability. Posterior predictive model checking indicated a good general model fit but also revealed lack of fit for a few groups of birds. This lack of fit may be due to between‐group differences in the spatial distribution on smaller scales within regions. We found that 48% of the Scandinavian robins, but only 31% of the central European robins, wintered in northern Africa. The remaining parts of both populations wintered in southern and central Europe. Therefore, a substantial part of the Scandinavian population appears to leap over individuals from the central European population during migration. The model is applied to summary tables of numbers of ringed and recovered birds. This allows us to handle very large data sets as, for example, those presented in bird migration atlases.     

Testing the consistency of wildlife data types before combining them: the case of camera traps and telemetry

Wildlife data gathered by different monitoring techniques are often combined to estimate animal density. However, methods to check whether different types of data provide consistent information (i.e., can information from one data type be used to predict responses in the other?) before combining them are lacking. We used generalized linear models and generalized linear mixed-effects models to relate camera trap probabilities for marked animals to independent space use from telemetry relocations using 2 years of data for fishers (Pekania pennanti) as a case study. We evaluated (1) camera trap efficacy by estimating how camera detection probabilities are related to nearby telemetry relocations and (2) whether home range utilization density estimated from telemetry data adequately predicts camera detection probabilities, which would indicate consistency of the two data types. The number of telemetry relocations within 250 and 500 m from camera traps predicted detection probability well. For the same number of relocations, females were more likely to be detected during the first year. During the second year, all fishers were more likely to be detected during the fall/winter season. Models predicting camera detection probability and photo counts solely from telemetry utilization density had the best or nearly best Akaike Information Criterion (AIC), suggesting that telemetry and camera traps provide consistent information on space use. Given the same utilization density, males were more likely to be photo-captured due to larger home ranges and higher movement rates. Although methods that combine data types (spatially explicit capture–recapture) make simple assumptions about home range shapes, it is reasonable to conclude that in our case, camera trap data do reflect space use in a manner consistent with telemetry data. However, differences between the 2 years of data suggest that camera efficacy is not fully consistent across ecological conditions and make the case for integrating other sources of space-use data.     

Sensitivity of marine fish thermal habitat models to fishery data sources

Statistical models built using different data sources and methods can exhibit conflicting patterns. We used the northern stock of black sea bass (Centropristis striata) as a case study to assess the impacts of using different fisheries data sources and laboratory‐derived physiological metrics in the development of thermal habitat models for marine fishes. We constructed thermal habitat models using generalized additive models (GAMs) based on various fisheries datasets as input, including the NOAA Northeast Fisheries Science Center (NEFSC) bottom trawl surveys, various inshore fisheries‐independent trawl surveys (state waters), NEFSC fisheries‐dependent observer data, and laboratory‐based physiological metrics. We compared each model''s GAM response curve and coupled them to historical ocean conditions in the U.S. Northeast Shelf using bias‐corrected ocean temperature output from a regional ocean model. Thermal habitat models based on shelf‐wide data (NEFSC fisheries‐dependent observer data and fisheries‐independent spring and fall surveys) explained the most variation in black sea bass presence/absence data at ~15% deviance explained. Models based on a narrower range of sampled thermal habitat from inshore survey data in the Northeast Area Monitoring and Assessment Program (NEAMAP) and the geographically isolated Long Island Sound data performed poorly. All models had similar lower thermal limits around 8.5℃, but thermal optima, when present, ranged from 16.7 to 24.8℃. The GAMs could reliably predict habitat from years excluded from model training, but due to strong seasonal temperature fluctuations in the region, could not be used to predict habitat in seasons excluded from training. We conclude that survey data source can greatly impact development and interpretation of thermal habitat models for marine fishes. We suggest that model development be based on data sources that sample the widest range of ocean temperature and physical habitat throughout multiple seasons when possible, and encourage thorough consideration of how data gaps may influence model uncertainty.     

Finding common ground: Toward comparable indicators of adaptive capacity of tree species to a changing climate

Adaptive capacity, one of the three determinants of vulnerability to climate change, is defined as the capacity of species to persist in their current location by coping with novel environmental conditions through acclimation and/or evolution. Although studies have identified indicators of adaptive capacity, few have assessed this capacity in a quantitative way that is comparable across tree species. Yet, such multispecies assessments are needed by forest management and conservation programs to refine vulnerability assessments and to guide the choice of adaptation measures. In this paper, we propose a framework to quantitatively evaluate five key components of tree adaptive capacity to climate change: individual adaptation through phenotypic plasticity, population phenotypic diversity as influenced by genetic diversity, genetic exchange within populations, genetic exchange between populations, and genetic exchange between species. For each component, we define the main mechanisms that underlie adaptive capacity and present associated metrics that can be used as indices. To illustrate the use of this framework, we evaluate the relative adaptive capacity of 26 northeastern North American tree species using values reported in the literature. Our results show adaptive capacity to be highly variable among species and between components of adaptive capacity, such that no one species ranks consistently across all components. On average, the conifer Picea glauca and the broadleaves Acer rubrum and A. saccharinum show the greatest adaptive capacity among the 26 species we documented, whereas the conifers Picea rubens and Thuja occidentalis, and the broadleaf Ostrya virginiana possess the lowest. We discuss limitations that arise when comparing adaptive capacity among species, including poor data availability and comparability issues in metrics derived from different methods or studies. The breadth of data required for such an assessment exemplifies the multidisciplinary nature of adaptive capacity and the necessity of continued cross‐collaboration to better anticipate the impacts of a changing climate.     

光照和不同药剂浸种对龙葵种子发芽率的影响

为探索超富集植物龙葵种子的快速发芽方法,以滤纸为发芽基质,采用恒温培养箱及温室培养的方法,对龙葵种子进行不同浸种处理,探讨光照及不同浸种液对龙葵种子发芽的影响.结果表明: 光照条件是龙葵种子发芽的重要条件之一,光照条件下的发芽率约为无光照条件下发芽率的5倍(P<0.05);不同浸种液均能显著提高龙葵种子的发芽率,其中以双氧水的处理效果最好,发芽时间最短(P<0.05);浸种后不经清洗处理的龙葵种子发芽率约为浸种后再经清洗处理发芽率的2～3倍(P<0.05).     

铜陵铜矿区凤丹根际和非根际土壤酶活性

对铜陵铜矿区凤丹根际和非根际土壤酶活性特征进行了研究.结果表明,凤丹根际土壤各种酶活性显著大于非根际土壤.凤丹非根际土壤过氧化氢酶、磷酸酶和脲酶活性能敏感地反映土壤重金属复合污染状况;根际环境对土壤酶活性的影响表现为: 磷酸酶＞脲酶＞过氧化氢酶＞蔗糖酶＞多酚氧化酶,影响率分别为131.562%、92.492%、87.557%、59.673%34.076%;土壤各种酶活性与重金属复合污染程度呈显著负相关,相关系数均-0.868以上,表现出重金属复合污染对土壤酶活性的抑制效应.凤丹可有效地改善土壤环境,提高土壤各种酶的活性.     

老工矿区污染生态问题与今后研究展望

老工矿区的环境污染及其引发的生态问题,已成为当今制约经济与社会可持续发展的世界性技术难题.对老工矿区这一概念的基本定义与范畴仍有争议,本文首先对这一概念从理论上进行了探讨;然后从剖析我国老工矿区现状尤其是其环境污染现实问题的复杂性入手,对老工矿区的区域二次污染这一科学前沿给予了重点关注;在深入阐述老工矿区环境污染问题复杂性与其特点的基础上,建议今后从国家层面上开展老工矿区二次污染发生机理与控制的系统研究,以及从分子生态毒理学角度系统开展有关新型疾病对环境污染响应的研究,是21世纪初环境科学和生态学研究的两大关键科学问题,这无疑是今后我国环境保护战略的重要组成部分.