Phenological advancement in arctic bird species: relative importance of snow melt and ecological factors

Previous studies have documented advancement in clutch initiation dates (CIDs) in response to climate change, most notably for temperate-breeding passerines. Despite accelerated climate change in the Arctic, few studies have examined nest phenology shifts in arctic breeding species. We investigated whether CIDs have advanced for the most abundant breeding shorebird and passerine species at a long-term monitoring site in arctic Alaska. We pooled data from three additional nearby sites to determine the explanatory power of snow melt and ecological variables (predator abundance, green-up) on changes in breeding phenology. As predicted, all species (semipalmated sandpiper, Calidris pusilla, pectoral sandpiper, Calidris melanotos, red-necked phalarope, Phalaropus lobatus, red phalarope, Phalaropus fulicarius, Lapland longspur, Calcarius lapponicus) exhibited advanced CIDs ranging from 0.40 to 0.80 days/year over 9 years. Timing of snow melt was the most important variable in explaining clutch initiation advancement (“climate/snow hypothesis”) for four of the five species, while green-up was a much less important explanatory factor. We found no evidence that high predator abundances led to earlier laying dates (“predator/re-nest hypothesis”). Our results support previous arctic studies in that climate change in the cryosphere will have a strong impact on nesting phenology although factors explaining changes in nest phenology are not necessarily uniform across the entire Arctic. Our results suggest some arctic-breeding shorebird and passerine species are altering their breeding phenology to initiate nesting earlier enabling them to, at least temporarily, avoid the negative consequences of a trophic mismatch.     

The importance of bryophytes in the classification of human-disturbed high arctic vegetation

Abstract. Evidence is presented from a variety of tundra cover types under human disturbance at three sites in the Canadian High Arctic to indicate that higher plants may be insufficient to differentiate among the apparently distinct geobotanical signatures of discrete surface disturbances. Unlike in the Low Arctic, woody growth forms are often minimal or lacking on heavily disturbed ground and several prominent species of ruderal herbs and especially graminoids occur on a wide variety of substrates. Therefore, cryptogams, particularly bryophytes, are important indicator taxa. Presence-absence data on bryophytes from minerotrophic and oligotrophic soils, combined with vascular cover-abundance data, enhanced detection of patch-level floristic gradients within and among disjunct coastal lowlands. However, the pool of ruderal bryophytes is limited, and ultimately factors such as frequency and abundance should be considered.     

Spatial interrelationships between terrain, snow distribution and vegetation patterns at an arctic foothills site in Alaska

A multidisciplinary approach combining field surveys, aerial photographic techniques, digital terrain modelling, and GIS technology was used to analyze spatial interrelationships at a study site in the northern foothills of the Brooks Range, The sensitivity of snow drifting to topography at the site is pronounced. The drift patterns indicate winter winds are predominantly from the south with a major secondary component from the southwest. These southwest winds are likely in conjunction with storm events. The deepest snow beds are found on the steeper, north-facing slopes. Snow also has an effect on vegetation that is evident at the scale of mapping (1:6000). Communities dominated by Cassiope tetragona are associated with deeper snow regimes, and may be useful indicators of deeper snow regimes even at much smaller scales because of their unique spectral signatures. The analyses conducted to date demonstrate the power of the GIS for analyzing terrain-geobotanical interrelationships, which will increase as we add new layers for other variables, and are able to correlate these with satellite data.     

Testing the ability of species distribution models to infer variable importance

Models of species’ distributions and niches are frequently used to infer the importance of range- and niche-defining variables. However, the degree to which these models can reliably identify important variables and quantify their influence remains unknown. Here we use a series of simulations to explore how well models can 1) discriminate between variables with different influence and 2) calibrate the magnitude of influence relative to an ‘omniscient’ model. To quantify variable importance, we trained generalized additive models (GAMs), Maxent and boosted regression trees (BRTs) on simulated data and tested their sensitivity to permutations in each predictor. Importance was inferred by calculating the correlation between permuted and unpermuted predictions, and by comparing predictive accuracy of permuted and unpermuted predictions using AUC and the continuous Boyce index. In scenarios with one influential and one uninfluential variable, models failed to discriminate reliably between variables when training occurrences were < 8–64, prevalence was > 0.5, spatial extent was small, environmental data had coarse resolution and spatial autocorrelation was low, or when pairwise correlation between environmental variables was |r| > 0.7. When two variables influenced the distribution equally, importance was underestimated when species had narrow or intermediate niche breadth. Interactions between variables in how they shaped the niche did not affect inferences about their importance. When variables acted unequally, the effect of the stronger variable was overestimated. GAMs and Maxent discriminated between variables more reliably than BRTs, but no algorithm was consistently well-calibrated vis-à-vis the omniscient model. Algorithm-specific measures of importance like Maxent's change-in-gain metric were less robust than the permutation test. Overall, high predictive accuracy did not connote robust inferential capacity. As a result, requirements for reliably measuring variable importance are likely more stringent than for creating models with high predictive accuracy.     

Intra-specific niche partitioning obscures the importance of fine-scale habitat data in species distribution models

Geographic information systems (GIS) allow researchers to make cost-effective, spatially explicit predictions of species’ distributions across broad geographic areas. However, there has been little research on whether using fine-scale habitat data collected in the field could produce more robust models of species’ distributions. Here we used radio-telemetry data collected on a declining species, the North American wood turtle (Glyptemys insculpta), to test whether fine-scale habitat variables were better predictors of occurrence than land-cover and topography variables measured in a GIS. Patterns of male and female occurrence were similar in the spring; however, females used a much wider array of land-cover types and topographic positions in the summer and early fall, making it difficult for GIS-based models to accurately predict female occurrence at this time of year. Males on the other hand consistently selected flat, low-elevation, riparian areas throughout the year, and this consistency in turn led to the development of a strong GIS-based model. These results demonstrate the importance of taking a more sex-specific and temporally dynamic view of the environmental niche.     

The importance of xylem constraints in the distribution of conifer species

Vulnerability of stem xylem to cavitation was measured in 10 species of conifers using high pressure air to induce xylem embolism. Mean values of air pressure required to induce a 50% loss in hydraulic conductivity (φ₅₀) varied enormously between species, ranging from a maximum of 14.2±0.6 MPa (corresponding to a xylem water potential of −14.2 MPa) in the semi-arid species Actinostrobus acuminatus to a minimum of 2.3±0.2 MPa in the rainforest species Dacrycarpus dacrydioides . Mean φ₅₀ was significantly correlated with the mean rainfall of the driest quarter within the distribution of each species. The value of φ₅₀ was also compared with leaf drought tolerance data for these species in order to determine whether xylem dysfunction during drought dictated drought response at the leaf level. Previous data describing the maximum depletion of internal CO₂ concentration (c_i) in the leaves of these species during artificial drought was strongly correlated with φ₅₀ suggesting a primary role of xylem in effecting leaf drought response. The possibility of a trade-off between xylem conductivity and xylem vulnerability was tested in a sub-sample of four species, but no evidence of an inverse relationship between φ₅₀ and either stem-area specific (K_a) or leaf-area specific conductivity (K₁) was found.     

物种分布模型的发展及评价方法

物种分布模型已被广泛地应用于以保护区规划、气候变化对物种分布的影响等为目的的研究。回顾了已经得到广泛应用的多种物种分布模型,总结了评价模型性能的方法。基于物种分布模型的发展和应用以及性能评价中尚存在的问题,本文认为:在物种分布模型中集成样本选择模块能够避免模型预测过程中的过度拟合及欠拟合,增加变量选择模块可评估和降低变量之间自相关性的影响,增加生物因子以及将物种对环境的适应性机制(及扩散行为特征)和潜在分布模型进行结合,是提高模型预测性能的可行方案;在模型性能的评价方面,采用赤池信息量可对模型的预测性能进行客观评价。相关建议可为物种分布建模提供参考。     

Ecological traps and species distribution models: a challenge for prioritizing areas of conservation importance

Species distribution models analyse how species use different types of habitats. Their spatial predictions are often used to prioritize areas for conservation. Individuals may, however, prefer settling in habitat types of low quality compared to other available habitats. This ecological trap phenomenon is usually studied in a small number of habitat patches and consequences at the landscape level are largely unknown. It is therefore often unclear whether the spatial pattern of habitat use is aligned with the behavioural decisions made by the individuals during habitat selection or reflects actual variation in the quality of different habitat types. As species distribution models analyse the pattern of occurrence in different habitats, there is a conservation interest in examining what their predictions mean in terms of habitat quality when ecological traps are operating. Previous work in Belgium showed that red-backed shrikes Lanius collurio are more attracted to newly available clear-cut habitat in plantation forests than to the traditionally used farmland habitat. We developed models with shrike distribution data and compared their predictions with spatial variation in shrike reproductive performance used as a proxy for habitat quality. Models accurately predicted shrike distribution and identified the preferred clear-cut patches as the most frequently used habitat, but reproductive performance was lower in clear-cut areas than in farmland. With human-induced rapid environmental changes, organisms may indeed be attracted to low-quality habitats and occupy them at high densities. Consequently, the predictions of statistical models based on occurrence records may not align with variation in significant population parameters for the maintenance of the species. When species expand their range to novel habitats, such models are useful to document the spatial distribution of the organisms, but data on population growth rates are worth collecting before using model predictions to guide the spatial prioritization of conservation actions.     

Trophic interactions in a high arctic snow goose colony

We examined the role of trophic interactions in structuringa high arctic tundra community characterized by a large breedingcolony of greater snow geese (Chen caerulescens atlantica).According to the exploitation ecosystem hypothesis of Oksanenet al. (1981), food chains are controlled by top-down interactions.However, because the arctic primary productivity is low, herbivorepopulations are too small to support functional predator populationsand these communities should thus be dominated by the plant/herbivore trophic-level interaction. Since 1990, we have beenmonitoring annual abundance and productivity of geese, the impactof goose grazing, predator abundance (mostly arctic foxes, Alopexlagopus) and the abundance of lemmings, the other significantherbivore in this community, on Bylot Island, Nunavut, Canada.Goose grazing consistently removed a significant proportionof the standing crop (     

滩地植被景观斑块分布特征与生态潜力评价

斑块大小是研究景观要素特征的一个主要参数。物种动态、潜在生境、潜在林产品(木材)、能量、养分和水流以及很多其它参数都受到景观中斑块大小的影响。将研究区域--富阳市富阳镇段滩地划分为6大类81个基本类型,并将其归为低水平斑块(基本类型对应斑块)和高水平斑块(大类对应斑块),采用极大似然估计法进行了研究,Kotliar和Wiens提出的斑块结构的等级框架是对斑块意义的更完整的理解,研究过程中对斑块的确定符合Kotliar和Wiens的等级思想。充分考虑了两个水平上的斑块,及它们对应于斑块类型的两个水平,研究表明:低水平的斑块内部具有较高的同质性,而高水平的斑块内部具有较低的同质性;在两种斑块水平上都显示出斑块大小分布是明显右偏的;只有少数类型斑块大小的分布服从负指数分布,而绝大多数斑块大小的分布服从对数正态分布,这对斑块内的群落和生态系统的稳定性和保护提供了参考。     

The uncertain nature of absences and their importance in species distribution modelling

Species distribution models (SDM) are commonly used to obtain hypotheses on either the realized or the potential distribution of species. The reliability and meaning of these hypotheses depends on the kind of absences included in the training data, the variables used as predictors and the methods employed to parameterize the models. Information about the absence of species from certain localities is usually lacking, so pseudo‐absences are often incorporated to the training data. We explore the effect of using different kinds of pseudo‐absences on SDM results. To do this, we use presence information on Aphodius bonvouloiri, a dung beetle species of well‐known distribution. We incorporate different types of pseudo‐absences to create different sets of training data that account for absences of methodological (i.e. false absences), contingent and environmental origin. We used these datasets to calibrate SDMs with GAMs as modelling technique and climatic variables as predictors, and compare these results with geographical representations of the potential and realized distribution of the species created independently. Our results confirm the importance of the kind of absences in determining the aspect of species distribution identified through SDM. Estimations of the potential distribution require absences located farther apart in the geographic and/or environmental space than estimations of the realized distribution. Methodological absences produce overall bad models, and absences that are too far from the presence points in either the environmental or the geographic space may not be informative, yielding important overestimations. GLMs and Artificial Neural Networks yielded similar results. Synthetic discrimination measures such as the Area Under the Receiver Characteristic Curve (AUC) must be interpreted with caution, as they can produce misleading comparative results. Instead, the joint examination of ommission and comission errors provides a better understanding of the reliability of SDM results.