Documenting lemming population change in the Arctic: Can we detect trends?

Lemmings are a key component of tundra food webs and changes in their dynamics can affect the whole ecosystem. We present a comprehensive overview of lemming monitoring and research activities, and assess recent trends in lemming abundance across the circumpolar Arctic. Since 2000, lemmings have been monitored at 49 sites of which 38 are still active. The sites were not evenly distributed with notably Russia and high Arctic Canada underrepresented. Abundance was monitored at all sites, but methods and levels of precision varied greatly. Other important attributes such as health, genetic diversity and potential drivers of population change, were often not monitored. There was no evidence that lemming populations were decreasing in general, although a negative trend was detected for low arctic populations sympatric with voles. To keep the pace of arctic change, we recommend maintaining long-term programmes while harmonizing methods, improving spatial coverage and integrating an ecosystem perspective.     

Linking intra-seasonal variations in climate and tree-ring δC: A functional modelling approach

Stable carbon isotopic composition (δ¹³C) in tree rings is a widely recognized tool for climate reconstruction, and several works suggest that seasonal information can be extracted from intra-ring δ¹³C variations. In this study, we explored the link between climate and intra-seasonal oak ring δ¹³C using a process-based modelling approach. The ISOCASTANEA model was developed to compute the seasonal dynamics of tree-ring δ¹³C for deciduous species from half-hourly climatic data by accounting for photosynthetic discrimination and carbon translocation and allocation at the tree scale and in tree rings.The model was applied from March 2005 to December 2007 in a 150-year-old deciduous oak forest. Canopy photosynthesis and stomatal conductance were calibrated using H₂O and CO₂ fluxes measured by the eddy flux technique, and simulated δ¹³C values were compared to seasonal patterns of total organic matter δ¹³C measured in tree rings for 2006 and 2007 at the same site. With the inclusion of carbon translocation and with regard to ¹³C enrichment of starch compared to soluble sugars, the model can reasonably simulate the intra-seasonal and inter-annual variability of tree-ring δ¹³C using the same parameter values for 2006 and 2007. The amplitude of the seasonal carbon isotope pattern in tree rings was influenced by both photosynthetic and post-photosynthetic processes (starch enrichment and reserve use). The δ¹³C variations in the early part of the ring, i.e., mainly in the earlywood, were related mostly to carbohydrate metabolism, although diluted information about environmental conditions during the previous year could also be found. The last part of the ring, consisting mainly of latewood, was found to be a good recorder of current-year environmental conditions, in particular relative humidity, at a fine temporal resolution when the growth rate was high. The sensitivity of the δ¹³C in the early part of the ring to carbohydrate metabolism suggests that intra-ring δ¹³C could be used to explore the relationship between tree decline or mortality and carbohydrate deficiency.     

Assessing aboveground tropical forest biomass using Google Earth canopy images

Reducing Emissions from Deforestation and Forest Degradation (REDD) in efforts to combat climate change requires participating countries to periodically assess their forest resources on a national scale. Such a process is particularly challenging in the tropics because of technical difficulties related to large aboveground forest biomass stocks, restricted availability of affordable, appropriate remote-sensing images, and a lack of accurate forest inventory data. In this paper, we apply the Fourier-based FOTO method of canopy texture analysis to Google Earth's very-high-resolution images of the wet evergreen forests in the Western Ghats of India in order to (1) assess the predictive power of the method on aboveground biomass of tropical forests, (2) test the merits of free Google Earth images relative to their native commercial IKONOS counterparts and (3) highlight further research needs for affordable, accurate regional aboveground biomass estimations. We used the FOTO method to ordinate Fourier spectra of 1436 square canopy images (125 x 125 m) with respect to a canopy grain texture gradient (i.e., a combination of size distribution and spatial pattern of tree crowns), benchmarked against virtual canopy scenes simulated from a set of known forest structure parameters and a 3-D light interception model. We then used 15 1-ha ground plots to demonstrate that both texture gradients provided by Google Earth and IKONOS images strongly correlated with field-observed stand structure parameters such as the density of large trees, total basal area, and aboveground biomass estimated from a regional allometric model. Our results highlight the great potential of the FOTO method applied to Google Earth data for biomass retrieval because the texture-biomass relationship is only subject to 15% relative error, on average, and does not show obvious saturation trends at large biomass values. We also provide the first reliable map of tropical forest aboveground biomass predicted from free Google Earth images.     

Incompletely resolved phylogenetic trees inflate estimates of phylogenetic conservatism

The tendency for more closely related species to share similar traits and ecological strategies can be explained by their longer shared evolutionary histories and represents phylogenetic conservatism. How strongly species traits co-vary with phylogeny can significantly impact how we analyze cross-species data and can influence our interpretation of assembly rules in the rapidly expanding field of community phylogenetics. Phylogenetic conservatism is typically quantified by analyzing the distribution of species values on the phylogenetic tree that connects them. Many phylogenetic approaches, however, assume a completely sampled phylogeny: while we have good estimates of deeper phylogenetic relationships for many species-rich groups, such as birds and flowering plants, we often lack information on more recent interspecific relationships (i.e., within a genus). A common solution has been to represent these relationships as polytomies on trees using taxonomy as a guide. Here we show that such trees can dramatically inflate estimates of phylogenetic conservatism quantified using S. P. Blomberg et al.'s K statistic. Using simulations, we show that even randomly generated traits can appear to be phylogenetically conserved on poorly resolved trees. We provide a simple rarefaction-based solution that can reliably retrieve unbiased estimates of K, and we illustrate our method using data on first flowering times from Thoreau's woods (Concord, Massachusetts, USA).     

Choosing classes for size projection matrix models

Projection matrix models are intensely used in ecology to model the dynamics of structured populations. When dealing with size-structured populations, there is no satisfactory algorithm to partition size into discrete classes. We show that the Vandermeer-Moloney algorithm for choosing classes is inconsistent with the Usher model, and systematically selects the finest classes. Considering that the matrix model is a discrete approximation of a continuous model, we define an approximation error as the sum of a distribution error (the difference between the discrete distribution and its continuous counterpart), and a sample error. The optimal partition of size into classes is the one that minimizes the approximation error. This method for choosing classes also shows that the choice of the class width cannot be disconnected from the choice of the time step. When applied to 520 trees of Dicorynia guianensis in French Guiana, this algorithm identified 8 classes of 11.4 cm in width, which is in agreement with the empirical choice of foresters.     

Effect of Classification Procedure on the Performance of Numerically Defined Ecological Regions

Ecological regionalizations define geographic regions exhibiting relative homogeneity in ecological (i.e., environmental and biotic) characteristics. Multivariate clustering methods have been used to define ecological regions based on subjectively chosen environmental variables. We developed and tested three procedures for defining ecological regions based on spatial modeling of a multivariate target pattern that is represented by compositional dissimilarities between locations (e.g., taxonomic dissimilarities). The procedures use a “training dataset” representing the target pattern and models this as a function of environmental variables. The model is then extrapolated to the entire domain of interest. Environmental data for our analysis were drawn from a 400 m grid covering all of Switzerland and consisted of 12 variables describing climate, topography and lithology. Our target patterns comprised land cover composition of each grid cell that was derived from interpretation of aerial photographs. For Regionalization 1 we used conventional cluster analysis of the environmental variables to define 60 hierarchically organized levels comprising from 5 to 300 regions. Regionalization 1 provided a base-case for comparison with the model-based regionalizations. Regionalization 2, 3 and 4 also comprised 60 hierarchically organized levels and were derived by modeling land cover composition for 4000 randomly selected “training” cells. Regionalization 2 was based on cluster analysis of environmental variables that were transformed based on a Generalized Dissimilarity Model (GDM). Regionalization 3 and 4 were defined by clustering the training cells based on their land cover composition followed by predictive modeling of the distribution of the land cover clusters using Classification and Regression Tree (CART) and Random Forest (RF) models. Independent test data (i.e. not used to train the models) were used to test the discrimination of land cover composition at all hierarchical levels of the regionalizations using the classification strength (CS) statistic. CS for all the model-based regionalizations was significantly higher than for Regionalization 1. Regionalization 3 and 4 performed significantly better than Regionalization 2 at finer hierarchical levels (many regions) and Regionalization 4 performed significantly better than Regionalization 3 for coarse levels of detail (few regions). Compositional modeling can significantly increase the performance of numerically defined ecological regionalizations. CART and RF-based models appear to produce stronger regionalizations because discriminating variables are able to change at each hierarchic level.     

Nesting habits shape feeding preferences and predatory behavior in an ant genus

We tested if nesting habits influence ant feeding preferences and predatory behavior in the monophyletic genus Pseudomyrmex (Pseudomyrmecinae) which comprises terrestrial and arboreal species, and, among the latter, plant-ants which are obligate inhabitants of myrmecophytes (i.e., plants sheltering so-called plant-ants in hollow structures). A cafeteria experiment revealed that the diet of ground-nesting Pseudomyrmex consists mostly of prey and that of arboreal species consists mostly of sugary substances, whereas the plant-ants discarded all the food we provided. Workers forage solitarily, detecting prey from a distance thanks to their hypertrophied eyes. Approach is followed by antennal contact, seizure, and the manipulation of the prey to sting it under its thorax (next to the ventral nerve cord). Arboreal species were not more efficient at capturing prey than were ground-nesting species. A large worker size favors prey capture. Workers from ground- and arboreal-nesting species show several uncommon behavioral traits, each known in different ant genera from different subfamilies: leaping abilities, the use of surface tension strengths to transport liquids, short-range recruitment followed by conflicts between nestmates, the consumption of the prey’s hemolymph, and the retrieval of entire prey or pieces of prey after having cut it up. Yet, we never noted group ambushing. We also confirmed that Pseudomyrmex plant-ants live in a kind of food autarky as they feed only on rewards produced by their host myrmecophyte, or on honeydew produced by the hemipterans they attend and possibly on the fungi they cultivate.     

Negative relationship between dispersal distance and demography in butterfly metapopulations

Little is known about the connection between demography and dispersal in metapopulations. The meta-analysis of the population time series of five butterfly species indicated that (meta)population dynamics are driven by density-dependent factors. Inter-specific comparison reveals a significant inverse relationship between population growth rate and the magnitude of dispersal distance. As the range of dispersal distances is constrained by the patch system, dispersing individuals moving too far away would (probably) get lost. This generates selective pressures on individuals with a high dispersal propensity, but favors individuals investing more in reproduction and results in a higher (meta)population growth rate. From a conservation perspective, individuals from (meta)populations and species sacrificing dispersal for the sake of reproductive performances are most vulnerable because of their higher sensitivity to stochastic events: the temporal variation of growth rate was much higher in the two metapopulations where dispersal was limited.