Which spatial distribution model best predicts the occurrence of dominant species in semi-arid rangeland of northern Iran?

Rangelands with more than 8000 plant species occupy nearly 54.6% of the land area of Iran and thus are accounted for a rich plant genetic storage. Mazandaran province has 378,000 ha of rangelands with high plant species richness and diversity due to its climate conditions but plants distribution is at risk because of non-principle management, land use change and as a result changing environmental factors. Vegetation management strategies can be guided by models that predict plant species distribution based on governing environmental variables. This is especially useful for the dominant species that determine ecosystem processes. In fact, modelling algorithm in each SDM determines its suitability for different ecosystems. Our aim was to compare the predictive power of a number of SDMs and to evaluate the importance of a range of environmental variables as predictors in the context of semi-arid rangeland vegetation. The selected study area, the Sarkhas rangelands (northern Iran, 36°10′ 42˝ N - 51°19′ 11˝ E), covers approximately 4358.9 ha of Mazandaran province. The efficacy of four different modelling techniques as well as Ensemble model was evaluated to predict the distribution of five dominant forage plant species (Vicia villosa, Stachys lavandulifolia, Coronilla balansae, Sanguisorba minor and Alopecurus textilis). The used models included artificial neural network (ANN), boosted regression trees (BRT), classification and regression trees (CART), and random forest (RF). Ensemble, RF and CART had the highest area under curve. The AUC obtained for Vicia villosa, Stachys lavandulifolia, Coronilla balansae, Sanguisorba minor and Alopecurus textilis, were 0.90, 0.72, 0.76, 0.69 and 0.75 respectively. Ensemble model was the model that most consistently demonstrated high predictive power across species in the rangeland context investigated here. BRT exhibited the least predictive power. An importance analysis of variables showed that soil organic C according to the CART model (0.396) and K according to the RF model (0.396) were the most important environmental variables.     

Dispersal of fleshy-fruited species: a matter of spatial scale?

The processes associated with the dispersal of fleshy-fruited species have been an important focus of ecological research during the last two decades. These processes include fruit removal, seed rain, seed predation, seed bank dynamics, germination and establishment. Some of them interfere with the mutualistic interaction of frugivorous birds and fleshy-fruited plants. We might expect such interference to be most pronounced where the intensity of the different processes has a spatial distribution similar to that of the original seed shadow. The central theme of this review is that the main processes associated with dispersal and recruitment act at different spatial scales. To investigate this idea, about 140 publications on dispersal of fleshy-fruited species from 1980 to 2000 were screened for the spatial scaling of these processes. Microhabitat, habitat, landscape, region and biome were the five spatial scales most commonly used. However, the representation of the different scales was not fully balanced; large-scale studies were scarce and most publications considered only one scale.The review reveals some trends in scaling of the main processes of plant dispersal and recruitment. Seed dispersal by birds and seed predation by rodents are strongly determined at the habitat level, and several studies report negative results for contrasts between microhabitats. Germination and seedling establishment, on the other hand, appear to be mainly influenced by differences between microhabitats, though information on larger scales is scarce. Genetic differentiation and phenology of fruiting have mostly been investigated at the habitat, landscape and regional scale, whereas information on the abundance of frugivorous birds and patterns in plant distribution results are available across the full range of scales from the level of the microhabitat to the region and biome. Future research should be directed to the major gaps in our knowledge, i.e. regional and zonal comparisons of the processes associated with dispersal. They should also be more sensitive to scale issues and ideally should have a multi-scaled design.     

Does spatial distribution of tree size account for spatial variation in soil respiration in a tropical forest?

We explored the relationship between soil processes, estimated through soil respiration (R _soil ), and the spatial variation in forest structure, assessed through the distribution of tree size, in order to understand the determinism of spatial variations in R _soil in a tropical forest. The influence of tree size was examined using an index (I _c ) calculated for each tree as a function of (1) the trunk cross section area and (2) the distance from the measurement point. We investigated the relationships between I _c and litterfall, root mass and R _soil , respectively. Strong significant relationships were found between I _c and both litterfall and root mass. R _soil showed a large range of variations over the 1-ha experimental plot, from 1.5 to 12.6 g_C m^?2 d^?1. The best relationship between I _c and R _soil only explained 17% of the spatial variation in R _soil . These results support the assumption that local spatial patterns in litter production and root mass depend on tree distribution in tropical forests. Our study also emphasizes the modest contribution of tree size distribution–which is mainly influenced by the presence of the biggest trees (among the large range size of the inventoried trees greater than 10 cm diameter at 1.30 m above ground level or at 0.5 m above the buttresses)–in explaining spatial variations in R _soil .     

Habitat use at fine spatial scale: how does patch clustering criteria explain the use of meadows by red deer?

Large mammalian herbivores are keystone species in different ecosystems. To mediate the effects of large mammalian herbivores on ecosystems, it is crucial to understand their habitat selection pattern. At finer scales, herbivore patch selection depends strongly on plant community traits and therefore its understanding is constrained by patch definition criteria. Our aim was to assess which criteria for patch definition best explained use of meadows by wild, free-ranging, red deer (Cervus elaphus) in a study area in Northeast Portugal. We used two clustering criteria types based on floristic composition and gross forage classes, respectively. For the floristic criteria, phytosociological approach was used to classify plant communities, and its objectivity evaluated with a mathematical clustering of the floristic relevés. Cover of dominant plant species was tested as a proxy for the phytosociological method. For the gross forage classes, the graminoids/forbs ratio and the percentage cover of legumes were used. For assessing deer relative use of meadows we used faecal accumulation rates. Patches clustered according to floristic classification better explained selection of patches by deer. Plant community classifications based on phytosociology, or proxies of this, used for characterizing meadow patches resulted useful to understand herbivore selection pattern at fine scales and thus potentially suitable to assist wildlife management decisions.     

Do traits of plant species predict the efficacy of species distribution models for finding new occurrences?

Species distribution models (SDMs) are used to test ecological theory and to direct targeted surveys for species of conservation concern. Several studies have tested for an influence of species traits on the predictive accuracy of SDMs. However, most used the same set of environmental predictors for all species and/or did not use truly independent data to test SDM accuracy. We built eight SDMs for each of 24 plant species of conservation concern, varying the environmental predictors included in each SDM version. We then measured the accuracy of each SDM using independent presence and absence data to calculate area under the receiver operating characteristic curve (AUC) and true positive rate (TPR). We used generalized linear mixed models to test for a relationship between species traits and SDM accuracy, while accounting for variation in SDM performance that might be introduced by different predictor sets. All traits affected one or both SDM accuracy measures. Species with lighter seeds, animal‐dispersed seeds, and a higher density of occurrences had higher AUC and TPR than other species, all else being equal. Long‐lived woody species had higher AUC than herbaceous species, but lower TPR. These results support the hypothesis that the strength of species–environment correlations is affected by characteristics of species or their geographic distributions. However, because each species has multiple traits, and because AUC and TPR can be affected differently, there is no straightforward way to determine a priori which species will yield useful SDMs based on their traits. Most species yielded at least one useful SDM. Therefore, it is worthwhile to build and test SDMs for the purpose of finding new populations of plant species of conservation concern, regardless of these species’ traits.     

On the use of climate covariates in aquatic species distribution models: are we at risk of throwing out the baby with the bath water?

Species distribution models (SDMs) in river ecosystems can incorporate climate information by using air temperature and precipitation as surrogate measures of instream conditions or by using independent models of water temperature and hydrology to link climate to instream habitat. The latter approach is preferable but constrained by the logistical burden of developing water temperature and hydrology models. We therefore assessed whether regional scale, freshwater SDM predictions are fundamentally different when climate data versus instream temperature and hydrology are used as covariates. Maximum entropy (MaxEnt) SDMs were built for 15 freshwater fishes using one of two covariate sets: 1) air temperature and precipitation (climate variables) in combination with physical habitat variables; or 2) water temperature, hydrology (instream variables) and physical habitat. Three procedures were then used to compare results from climate vs instream models. First, equivalence tests assessed average pairwise differences (site‐specific comparisons throughout each species’ range) among climate and instream models. Second, ‘congruence’ tests determined how often the same stream segments were assigned high habitat suitability by climate and instream models. Third, Schoener's D and Warren's I niche overlap statistics quantified range‐wide similarity in predicted habitat suitability from climate vs instream models. Equivalence tests revealed small, pairwise differences in habitat suitability between climate and instream models (mean pairwise differences in MaxEnt raw scores for all species < 3 × 10^–4). Congruence tests showed a strong tendency for climate and instream models to predict high habitat suitability at the same stream segments (median congruence = 68%). D and I statistics reflected a high margin of overlap among climate and instream models (median D = 0.78, median I = 0.96). Overall, we found little support for the hypothesis that SDM predictions are fundamentally different when climate versus instream covariates are used to model fish species’ distributions at the scale of the Columbia Basin.     

Patterns of prey size and taxonomic composition in larval fish: are there general size-dependent models?

The allometric relationship of maxilla length to larval fish weight approached a slope of 1/3 for 11 species from Conception Bay, Newfoundland. In 10 of these species, mean prey size increased with length of the maxilla but not constantly. In six species, niche breadth increased with length of maxilla. The rate of change in niche breadth was inversely related to the rate of change in mean prey size. Nauplii and copepodites of calanoids were selected positively, and cyclopoids negatively for almost all species of larval fish.     

Does the interpolation accuracy of species distribution models come at the expense of transferability?

Model transferability (extrapolative accuracy) is one important feature in species distribution models, required in several ecological and conservation biological applications. This study uses 10 modelling techniques and nationwide data on both (1) species distribution of birds, butterflies, and plants and (2) climate and land cover in Finland to investigate whether good interpolative prediction accuracy for models comes at the expense of transferability – i.e. markedly worse performance in new areas. Models’ interpolation and extrapolation performance was primarily assessed using AUC (the area under the curve of a receiver characteristic plot) and Kappa statistics, with supplementary comparisons examining model sensitivity and specificity values. Our AUC and Kappa results show that extrapolation to new areas is a greater challenge for all included modelling techniques than simple filling of gaps in a well‐sampled area, but there are also differences among the techniques in the degree of transferability. Among the machine‐learning modelling techniques, MAXENT, generalized boosting methods (GBM), and artificial neural networks (ANN) showed good transferability while the performance of GARP and random forest (RF) decreased notably in extrapolation. Among the regression‐based methods, generalized additive models (GAM) and generalized linear models (GLM) showed good transferability. A desirable combination of good prediction accuracy and good transferability was evident for three modelling techniques: MAXENT, GBM, and GAM. However, examination of model sensitivity and specificity revealed that model types may differ in their tendencies to either increased over‐prediction of presences or absences in extrapolation, and some of the methods show contrasting changes in sensitivity vs specificity (e.g. ANN and GARP). Among the three species groups, the best transferability was seen with birds, followed closely by butterflies, whereas reliable extrapolation for plant species distribution models appears to be a major challenge at least at this scale. Overall, detailed knowledge of the behaviour of different techniques in various study settings and with different species groups is of utmost importance in predictive modelling.     

Lost in space? Searching for directions in the spatial modelling of individuals,populations and species ranges

The workshop ‘Spatial models in animal ecology, management and conservation’ held at Silwood Park (UK), 9–11 March 2010, aimed to synthesize recent progress in modelling the spatial dynamics of individuals, populations and species ranges and to provide directions for research. It brought together marine and terrestrial researchers working on spatial models at different levels of organization, using empirical as well as theory-driven approaches. Different approaches, temporal and spatial scales, and practical constraints predominate at different levels of organization and in different environments. However, there are theoretical concepts and specific methods that can fruitfully be transferred across levels and systems, including: habitat suitability characterization, movement rules, and ways of estimating uncertainty.     

Combining multiple data sets in a likelihood analysis: which models are the best?

Until recently, phylogenetic analyses have been routinely based on homologous sequences of a single gene. Given the vast number of gene sequences now available, phylogenetic studies are now based on the analysis of multiple genes. Thus, it has become necessary to devise statistical methods to combine multiple molecular data sets. Here, we compare several models for combining different genes for the purpose of evaluating the likelihood of tree topologies. Three methods of branch length estimation were studied: assuming all genes have the same branch lengths (concatenate model), assuming that branch lengths are proportional among genes (proportional model), or assuming that each gene has a separate set of branch lengths (separate model). We also compared three models of among-site rate variation: the homogenous model, a model that assumes one gamma parameter for all genes, and a model that assumes one gamma parameter for each gene. On the basis of two nuclear and one mitochondrial amino acid data sets, our results suggest that, depending on the data set chosen, either the separate model or the proportional model represents the most appropriate method for branch length analysis. For all the data sets examined, one gamma parameter for each gene represents the best model for among-site rate variation. Using these models we analyzed alternative mammalian tree topologies, and we describe the effect of the assumed model on the maximum likelihood tree. We show that the choice of the model has an impact on the best phylogeny obtained.     

Distribution of plants in a California serpentine grassland: are rocky hummocks spatial refuges for native species?

Invasions by non-native taxa can have severe consequences for native species. In the heavily invaded serpentine grasslands of central California, many native species appear to be restricted to isolated outcrops of shallow serpentine soil, or hummocks, although the extent to which these hummocks function as refuges for native vegetation has never been quantified. We tested whether native plant species were restricted to hummocks within a serpentine grassland at the University of California Sedgwick Reserve near Santa Barbara, California by sampling species along hummock-grassland gradients. We also examined the influence of soil parameters, hummock area, proximity to other hummocks, and spatial location on species composition across 16 hummocks at this site. Both the hummocks and the surrounding grassland had high Mg, low Ca, and low Ca to Mg ratios typical of serpentine systems. Hummocks appeared to be more stressful environments because of their shallower soils, lower cation exchange capacity, and greater percent sand. Of the 27 most common plant species sampled along hummock-grassland transects, we identified 8 hummock specialists, 7 edge specialists, 8 matrix specialists, and 4 generalists. Importantly, both the hummock and matrix specialist groups included native species. Plant community composition was correlated with spatial positioning of the hummocks and with soil Ca, Na, K, and N. The number of species increased and community composition changed with increasing hummock area. Species composition was most similar among hummocks in close proximity to each other, and decreased with increasing distance between hummocks. Our results suggest that the community structure of serpentine grasslands is spatially complex and an effective management or restoration plan must address this complexity.     

Female aggression in red deer: Does it indicate competition for mates?

Female–female competition over mates is often considered of minor importance, particularly in polygynous species. In red deer (Cervus elaphus), female–female aggression within harems during the breeding season has not been studied to date. Herein, we examined if oestrous female red deer in harems show elevated aggression rates, compared to when they are in harems but not in oestrous, and also when they are in foraging groups outside of the breeding season. Any increased levels of aggression involving oestrous females, could indicate the potential for female–female competition for mates in this species. We found that aggressive interactions among female red deer were clearly evident. The most common forms of aggression were displacements, nose threats and kicking. Biting and ear threats occurred less frequently, and chases were rare. There were no differences in the proportion of the different aggression types in the three social contexts. More importantly, we found that the highest overall rates of aggression were for oestrous females in harems, and for females in foraging groups. The lowest rates of aggression were found in harems (when the focal female was not in oestrous). If high rates of aggression also occur when several females are simultaneously in oestrous within single harems, then it is possible that this aggression could affect either mate choice or mating order. These results suggest that female–female competition over mates could play a role in the mating behaviour of red deer.     

Understanding sample size: what determines the required number of microarrays for an experiment?

DNA microarray experiments have become a widely used tool for studying gene expression. An important, but difficult, part of these experiments is deciding on the appropriate number of biological replicates to use. Often, researchers will want a number of replicates that give sufficient power to recognize regulated genes while controlling the false discovery rate (FDR) at an acceptable level. Recent advances in statistical methodology can now help to resolve this issue. Before using such methods it is helpful to understand the reasoning behind them. In this Research Focus article we explain, in an intuitive way, the effect sample size has on the FDR and power, and then briefly survey some recently proposed methods in this field of research and provide an example of use.     

Quantifying the degree of bias from using county‐scale data in species distribution modeling: Can increasing sample size or using county‐averaged environmental data reduce distributional overprediction?

Citizen‐science databases have been used to develop species distribution models (SDMs), although many taxa may be only georeferenced to county. It is tacitly assumed that SDMs built from county‐scale data should be less precise than those built with more accurate localities, but the extent of the bias is currently unknown. Our aims in this study were to illustrate the effects of using county‐scale data on the spatial extent and accuracy of SDMs relative to true locality data and to compare potential compensatory methods (including increased sample size and using overall county environmental averages rather than point locality environmental data). To do so, we developed SDMs in maxent with PRISM‐derived BIOCLIM parameters for 283 and 230 species of odonates (dragonflies and damselflies) and butterflies, respectively, for five subsets from the OdonataCentral and Butterflies and Moths of North America citizen‐science databases: (1) a true locality dataset, (2) a corresponding sister dataset of county‐centroid coordinates, (3) a dataset where the average environmental conditions within each county were assigned to each record, (4) a 50/50% mix of true localities and county‐centroid coordinates, and (5) a 50/50% mix of true localities and records assigned the average environmental conditions within each county. These mixtures allowed us to quantify the degree of bias from county‐scale data. Models developed with county centroids overpredicted the extent of suitable habitat by 15% on average compared to true locality models, although larger sample sizes (>100 locality records) reduced this disparity. Assigning county‐averaged environmental conditions did not offer consistent improvement, however. Because county‐level data are of limited value for developing SDMs except for species that are widespread and well collected or that inhabit regions where small, climatically uniform counties predominate, three means of encouraging more accurate georeferencing in citizen‐science databases are provided.