The importance of Paracallisoma coecus and myctophid fishes to nesting fork-tailed and Leach's storm-petrels in the Queen Charlotte Islands, British Columbia

The diet of fork-tailed (Oceanodroma furcata) and Leach's storm-petrel(O.leucorhoa) was studied at Petrel Island, a small island northof Hippa Island in the Queen Charlotte Islands, British Columbiain 1983. The diet of the two storm-petrel species consistedmostly of the amphipod Paracallisoma coecus and fish. Most fisheswhich could be identified were myctophids, and may have beenacquired by storm-petrels at night. Fishes increased while P.coecusdeclined in importance as a food over the nestling season ofthe storm-petrels. The amount of oil also declined in the Leach'sstorm-petrel diet over the season. Since P.coecus contains muchoil, the seasonal decline of both P.coecus and oil was probablyassociated. Other less important foods were jellyfishes, copepods,isopods, euphausiids, shrimp, squid and octopus. Jellyfishes(Velella velella) only occurred in the diet of Leach's storm-petrels,which may relate to that storm-petrel feeding in open oceanwhere V. velella occurs most frequently. The most numerous copepodwas Neocalanus cristatus, which was only observed in the storm-petreldiet early on during the study period. An abyssal copepod, Bathycalanusbradyi, and the isopod Cirolana californiensis (tentativelyidentified, the latter only previously known from sea canyonsoff California) were surprising food items of storm-petrels.Most euphausiid prey consisted of Thysanoessa spinifera andEuphausia pacifica.     

Benefits of mass reduction for commuting flight with heavy food load in Leach’s storm-petrel,Oceanodroma leucorhoa

When rearing chicks, Leach’s storm-petrels (Oceanodroma leucorhoa) commute between foraging areas and breeding colonies with heavy food loads. At this time they should maximize the size of energy-supplying organs in response to increased energy expenditure but minimize total body mass to decrease the energetic cost of flight. Nineteen storm-petrels were killed to examine the changes in body composition and the masses of energy-supplying organs in birds that were incubating and rearing chicks. Parents lost a mean of 7.95 g in body mass between the stages of incubation and chick-rearing mainly via a loss of skin including subcutaneous adipose tissue, and a small fraction of heart and digestive organs, which are considered energy-supplying organs. This mass loss actually enables them to decrease flight cost by 14.4%. The benefits of decreasing flight costs by reducing total body mass are greater than if the energy-supplying organs of birds are enlarged only.

     

Determining the factors affecting the distribution of Muscari latifolium,an endemic plant of Turkey,and a mapping species distribution model

Species distribution modeling was used to determine factors among the large predictor candidate data set that affect the distribution of Muscari latifolium , an endemic bulbous plant species of Turkey, to quantify the relative importance of each factor and make a potential spatial distribution map of M. latifolium . Models were built using the Boosted Regression Trees method based on 35 presence and 70 absence records obtained through field sampling in the Gönen Dam watershed area of the Kazda?? Mountains in West Anatolia. Large candidate variables of monthly and seasonal climate, fine‐scale land surface, and geologic and biotic variables were simplified using a BRT simplifying procedure. Analyses performed on these resources, direct and indirect variables showed that there were 14 main factors that influence the species’ distribution. Five of the 14 most important variables influencing the distribution of the species are bedrock type, Quercus cerris density, precipitation during the wettest month, Pinus nigra density, and northness. These variables account for approximately 60% of the relative importance for determining the distribution of the species. Prediction performance was assessed by 10 random subsample data sets and gave a maximum the area under a receiver operating characteristic curve (AUC) value of 0.93 and an average AUC value of 0.8. This study provides a significant contribution to the knowledge of the habitat requirements and ecological characteristics of this species. The distribution of this species is explained by a combination of biotic and abiotic factors. Hence, using biotic interaction and fine‐scale land surface variables in species distribution models improved the accuracy and precision of the model. The knowledge of the relationships between distribution patterns and environmental factors and biotic interaction of M. latifolium can help develop a management and conservation strategy for this species.     

Impact of introduced house mice (Mus musculus) on burrowing seabirds on Steeple Jason and Grand Jason Islands,Falklands, South Atlantic

Whilst there is good evidence for negative impacts of introduced rat species on island ecosystems, the effects of house mice (Mus musculus) are generally less well documented. In some situations, introduced house mice can exert severe impacts, particularly where this is the only introduced mammal. Here, we examine the distribution, relative abundance and breeding success of small burrowing seabirds on Steeple Jason Island, Falklands, in relation to habitat types and the distribution of house mice which is the sole introduced mammal species, and we make comparisons with seabird distribution and densities on the neighbouring island of Grand Jason where mice are absent. Grey-backed storm-petrel (Garrodia nereis) and Wilson’s storm-petrel (Oceanites oceanicus), which due to their extremely small size are likely to be the most vulnerable to mouse predation, were considerably more abundant on mouse-free Grand Jason than on Steeple Jason. Grey-backed storm-petrel, which are typically associated with tussac grass, avoided this habitat on Steeple Jason where it is associated with high levels of house mouse activity (assessed from the proportion of wax baits gnawed overnight), whereas on mouse-free Grand Jason, there was no such avoidance. Wilson’s storm-petrel nesting on Steeple Jason suffered high rates of egg and chick loss. Whilst we found evidence for detrimental impacts of house mice on the two small storm-petrel species, there was no relationship between relative mouse activity levels and the distribution or abundance of the larger thin-billed Prion (Pachyptila belcheri).     

Nocturnal foraging by great skuas Stercorarius skua: implications for conservation of storm-petrel populations

At St Kilda, Outer Hebrides, a large colony of great skuas Stercorarius skua feed extensively on one of the largest colonies of Leach’s storm-petrels Oceanodroma leucorhoa in Europe, but little is known about the dynamics of this predator–prey system. Recently published population estimates of storm-petrels make it possible to estimate the impact of skua predation for the first time. Although skuas in the southern hemisphere catch petrels attending breeding colonies at night, it is not known whether congeners in the northern hemisphere also forage during the hours of darkness. We found (using radio-transmitters) that skuas regularly forage at night and (using light intensifying equipment) observed them catching storm-petrels at night. However, skuas also foraged during daylight hours, and it is unknown whether they might also catch storm-petrels at sea. Data on diet composition reveals that the proportion of storm-petrels in skua diet declined between 1996 and 1997, but remained constant thereafter. Although a large proportion of the storm-petrel prey is likely to consist of non-breeders, numbers consumed suggest that breeders and an unknown quantity of transients may also been eaten. The numbers of storm-petrels eaten are not sustainable and may result in substantial long-term population declines. Under current conditions, maintenance of large populations of both Leach’s storm-petrels and great skuas at St Kilda appears to be mutually exclusive.     

Modeling potential invasion range of alien invasive species,Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP

Bushmint (Hyptis suaveolens (L.) Poit.) is one among the world's most noxious weeds. Bushmint is rapidly invading tropical ecosystems across the world, including India, and is major threat to native biodiversity, ecosystems and livelihoods. Knowledge about the likely areas under bushmint invasion has immense importance for taking rapid response and mitigation measures. In the present study, we model the potential invasion range of bushmint in India and investigate prediction capabilities of two popular species distribution models (SDM) viz., MaxEnt (Maximum Entropy) and GARP (Genetic Algorithm for Rule-Set Production). We compiled spatial layers on 22 climatic and non-climatic (soil type and land use land cover) environmental variables at India level and selected least correlated 14 predictor variables. 530 locations of bushmint along with 14 predictor variables were used to predict bushmint distribution using MaxEnt and GARP. We demonstrate the relative contribution of predictor variables and species-environmental linkages in modeling bushmint distribution. A receiver operating characteristic (ROC) curve was used to assess each model's performance and robustness. GARP had a relatively lower area under curve (AUC) score (AUC: 0.75), suggesting its lower ability in discriminating the suitable/unsuitable sites. Relative to GARP, MaxEnt performed better with an AUC value of 0.86. Overall the outputs of MaxEnt and GARP matched in terms of geographic regions predicted as suitable/unsuitable for bushmint in India, however, predictions were closer in the spatial extent in Central India and Western Himalayan foothills compared to North-East India, Chottanagpur and Vidhayans and Deccan Plateau in India.     

When rediscovery is not enough: taxonomic uncertainty hinders conservation of a critically endangered bird

In 2003, birds similar to the extinct New Zealand storm-petrel Oceanites maorianus were observed in Hauraki Gulf NZ, raising the possibility of rediscovery after 150 years. O. maorianus has and continues to be surrounded by taxonomic uncertainty, being variously described as a distinct genus, a distinct species, or merely a plumage variant. This uncertainty has hindered conservation planning and funding for the species. Here we examine the taxonomic identity of the rediscovered birds and museum specimens using phylogenetic analysis of mitochondrial (1143 bp cytochrome b) and nuclear (890 bp β-fibrinogen) genes. Using cytochrome b sequence amplified from the 150+ year old specimens, we found that the extant and museum O. maorianus were the same taxon (0.01 genetic distance), with both differing from all other storm-petrel taxa. Using both genes, we examined the phylogenetic affinities of O. maorianus to the Oceanitinae and Hydrobatinae storm-petrels finding that O. maorianus was more closely aligned to Fregetta (0.08-0.09) than Oceanites (0.11-0.12), thereby confirming its status as a distinct taxon, not a plumage variant of O. oceanicus. Our analysis verifies that the previously presumed extinct New Zealand storm-petrel has been rediscovered and can now be assigned a conservation priority commensurate with its critically endangered status.     

Physiological effects of climate on distributions of endothermic species

Aim Determining the mechanisms underlying climatic limitation of species distributions is essential for understanding responses to current climatic change. Disentangling direct (e.g. physiological) and indirect (e.g. trophic) effects of climate on distributions through occurrence‐based modelling is problematic because most species use the same area for both shelter and food acquisition. By focusing on marine birds that breed on land but feed at sea, we exploit a rare opportunity to dissociate direct from indirect climatic effects on endothermic species. Location Coastal Europe. Methods We developed climate‐response surfaces (CRS) for 13 seabird species in coastal Europe, linking terrestrial climatic variables considered important for heat transfer with presence/absence data across each species’ entire European breeding range. Agreement between modelled and actual distribution was assessed for jackknifed samples using area under the curve (AUC) of receiver operating characteristic plots. Higher AUC values indicated closer correspondence between observed breeding distribution and terrestrial climate. We assessed the influence of several ecological factors on model performance across species. Results Species maximum foraging range and breeding latitude explained the greatest proportion of variation in AUC across species. AUC was positively related to both latitude and foraging range. Main conclusions The positive relationship between foraging range and AUC suggests that species foraging further are more likely to be constrained by environmental heat stress conditions at the breeding site. One plausible explanation is that long foraging trips result in one parent spending long periods in continuous nest attendance, exposed to such conditions. These may include negative impacts through predation and parasitism in addition to physiological responses to the thermal environment, which probably explains why our models performed better for species breeding at higher latitudes, where such species interactions are considered less important. These data highlight the importance of considering physiological impacts of climate for endothermic species, and suggest that widespread oceanographic changes that reduce prey quality and quantity for seabirds at sea may be exacerbated by additional impacts of climate at the breeding site.     

Differentiation of sympatric populations of the band-rumped storm-petrel in the Galapagos Islands: an examination of genetics, morphology, and vocalizations

In each of at least two locations within the Galapagos Islands, breeding band-rumped storm-petrels (Oceanodroma castro) form two distinct populations that use the same colony site at separate times of the year for reproduction. Temporal segregation of these populations raises the possibility that they are reproductively isolated and represent cryptic species. We examined variation in mitochondrial DNA, morphology, and vocalizations of storm-petrel populations nesting 6 months apart on the islet of Plaza Norte in the Galapagos. Seasonal populations displayed low but significant levels of differentiation in the mitochondrial control region, five morphological variables, and one feature of male vocalizations. Breeding populations appear to have been separated for approximately 1700 years. Given the recent divergence date and relatively high effective population sizes (4000-5600 females each), seasonal populations are unlikely to be in genetic equilibrium. As a result, the low divergence estimate probably reflects historical association and not contemporary genetic exchange. These populations are not sufficiently differentiated to be considered cryptic species. However, they are probably in the early stages of divergence. Consequently, we recommend that cool- and hot season populations on Plaza Norte be recognized as separate management units.     

DMS dynamics in the most oligotrophic subtropical zones of the global ocean

The influences of physico-chemical and biological processes on dimethylsulfide (DMS) dynamics in the most oligotrophic subtropical zones of the global ocean were investigated. As metrics for the dynamics of DMS and the so-called ‘summer DMS paradox’ of elevated summer concentrations when surface chlorophyll a (Chl) and particulate organic carbon (POC) levels are lowest, we used the DMS-to-Chl and DMS-to-POC ratios in the context of three independent and complementary approaches. Firstly, field observations of environmental variables (such as the solar radiation dose, phosphorus limitation of phytoplankton and bacterial growth) were used alongside discrete DMS, Chl and POC estimates extracted from global climatologies (i.e., a ‘station based’ approach). We then used monthly climatological data for DMS, Chl, and POC averaged over the biogeographic province wherein a given oligotrophic subtropical zone resides (i.e., a ‘province based’ approach). Finally we employed sensitivity experiments with a new DMS module coupled to the ocean general circulation and biogeochemistry model PISCES to examine the influence of various processes in governing DMS dynamics in oligotrophic regions (i.e., a ‘model based’ approach). We find that the ‘station based’ and ‘province based’ approaches yield markedly different results. Interestingly, the ‘province based’ approach suggests the presence of a ‘summer DMS paradox’ in most all of the oligotrophic regions we studied. In contrast, the ‘station based’ approach suggests that the ‘summer DMS paradox’ is only present in the Sargasso Sea and eastern Mediterranean. Overall, we found the regional differences in the absolute and relative concentrations of DMS between 5 of the most oligotrophic regions of the world’s oceans were better accounted for by their nutrient dynamics (specifically phosphorus limitation) than by physical factors often invoked, e.g., the solar radiation dose. Our ‘model based’ experiments suggest that it is the limitation of phytoplankton/bacterial production and bacterial consumption of DMS by pervasive phosphorus limitation that is responsible for the ‘summer DMS paradox’.     

The presence of an avian co-forager reduces vigilance in a cooperative mammal

Many animals must trade-off anti-predator vigilance with other behaviours. Some species facilitate predator detection by joining mixed-species foraging parties and ‘eavesdropping’ on the predator warnings given by other taxa. Such use of heterospecific warnings presumably reduces the likelihood of predation, but it is unclear whether it also provides wider benefits, by allowing individuals to reduce their own vigilance. We examine whether the presence of an avian co-forager, the fork-tailed drongo (Dicrurus adsimilis), affects rates of vigilance (including sentinel behaviour) in wild dwarf mongooses (Helogale parvula). We simulate the presence of drongos—using playbacks of their non-alarm vocalizations—to show that dwarf mongooses significantly reduce their rate of vigilance when foraging with this species. This is, to our knowledge, the first study to demonstrate experimentally that a mammal reduces vigilance in the presence of an avian co-forager.     

Behavioural context of multi-scale species distribution models assessed by radio-tracking

Incorporating ecological processes and animal behaviour into Species Distribution Models (SDMs) is difficult. In species with a central resting or breeding place, there can be conflict between the environmental requirements of the ‘central place’ and foraging habitat. We apply a multi-scale SDM to examine habitat trade-offs between the central place, roost sites, and foraging habitat in Myotis nattereri. We validate these derived associations using habitat selection from behavioural observations of radio-tracked bats. A Generalised Linear Model (GLM) of roost occurrence using land cover variables with mixed spatial scales indicated roost occurrence was positively associated with woodland on a fine scale and pasture on a broad scale. Habitat selection of radio-tracked bats mirrored the SDM with bats selecting for woodland in the immediate vicinity of individual roosts but avoiding this habitat in foraging areas, whilst pasture was significantly positively selected for in foraging areas. Using habitat selection derived from radio-tracking enables a multi-scale SDM to be interpreted in a behavioural context. We suggest that the multi-scale SDM of M. nattereri describes a trade-off between the central place and foraging habitat. Multi-scale methods provide a greater understanding of the ecological processes which determine where species occur and allow integration of behavioural processes into SDMs. The findings have implications when assessing the resource use of a species at a single point in time. Doing so could lead to misinterpretation of habitat requirements as these can change within a short time period depending on specific behaviour, particularly if detectability changes depending on behaviour.     

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.     

A Bayesian modeling of wildfire probability in the Zagros Mountains,Iran

The preparation of probability distribution maps is the first important step in risk assessment and wildfire management. Here we employed Weights-of-Evidence (WOE) Bayesian modeling to investigate the spatial relationship between historical fire events in the Chaharmahal-Bakhtiari Province of Iran, using a wide range of binary predictor variables (i.e., presence or absence of a variable characteristic or condition) that represent topography, climate, and human activities. Model results were used to produce distribution maps of wildfire probability. Our modeling approach is based on the assumption that the probabilities reflect the observed proportions of the total landscape area occupied by the corresponding events (i.e., fire incident or no fire) and conditions (i.e., classes) of predictor variables. To assess the effect of each predictor variable on model outputs, we excluded each variable in turn during calculations. The results were validated and compared by the receiver operating characteristic (ROC) using both success rate and prediction rate curves. Seventy percent of fire events were used for the former, while the remainder was used for the latter. The validation results showed that the area under the curves (AUC) for success and prediction rates of the model that included all thirteen predictor variables that represent topography, climate, and human influences were 84.6 and 80.4%, respectively. The highest AUC for success and prediction rates (86.8 and 84.6%) were achieved when the altitude variable was excluded from the analysis. We found slightly decreased AUC values when the slope-aspect and proximity to settlements variables were excluded. These findings clearly demonstrate that the probability of a fire is strongly dependent upon the topographic characteristics of landscapes and, perhaps more importantly, human infrastructure and associated human activities. The results from this study may be useful for land use planning, decision-making for wildfire management, and the allocation of fire resources prior to the start of the main fire season.     

Remote sensing variables improve species distribution models for alpine plant species

Species distribution models (SDMs) are cost-effective, transparent and flexible planning tools to support various areas in nature conservation. Variables taken from remote sensing (RS) are broadly applicable to biodiversity studies. In our study, we combined RS-variables (normalized differenced vegetation index and land surface temperatures), with topographic and geological variables to produce detailed SDMs in the context of a seed collection campaign of the Alpine Seed Conservation and Research Project. To identify effective predictor variable combinations we compiled three different variable sets and compared the predictive model performance.The full model, that combines all types of variables, slightly outperforms (average values for TSS: 0.91, AUC: 0.98, Kappa: 0.7) models that use topo-climatic variables (average values for TSS: 0.91, AUC: 0.98, Kappa: 0.68) or NDVI (average values for TSS: 0.85, AUC: 0.96, Kappa: 0.54) alone. We also produced ensemble models that performed slightly better compared to the different model algorithms used in our approach. We identified the temperature of the coldest month, mean NDVI and bedrock as important variables that determine the distribution of alpine plant species.Our full models show high accordance with actual species distribution ranges and are highly relevant for efforts to identify special areas for either in-situ or ex-situ conservation.     

Mapping Current and Potential Distribution of Non-Native Prosopis juliflora in the Afar Region of Ethiopia

We used correlative models with species occurrence points, Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, and topo-climatic predictors to map the current distribution and potential habitat of invasive Prosopis juliflora in Afar, Ethiopia. Time-series of MODIS Enhanced Vegetation Indices (EVI) and Normalized Difference Vegetation Indices (NDVI) with 250 m² spatial resolution were selected as remote sensing predictors for mapping distributions, while WorldClim bioclimatic products and generated topographic variables from the Shuttle Radar Topography Mission product (SRTM) were used to predict potential infestations. We ran Maxent models using non-correlated variables and the 143 species- occurrence points. Maxent generated probability surfaces were converted into binary maps using the 10-percentile logistic threshold values. Performances of models were evaluated using area under the receiver-operating characteristic (ROC) curve (AUC). Our results indicate that the extent of P. juliflora invasion is approximately 3,605 km² in the Afar region (AUC  = 0.94), while the potential habitat for future infestations is 5,024 km² (AUC  = 0.95). Our analyses demonstrate that time-series of MODIS vegetation indices and species occurrence points can be used with Maxent modeling software to map the current distribution of P. juliflora, while topo-climatic variables are good predictors of potential habitat in Ethiopia. Our results can quantify current and future infestations, and inform management and policy decisions for containing P. juliflora. Our methods can also be replicated for managing invasive species in other East African countries.     

Assessing the effectiveness of foraging radius models for seabird distributions using biotelemetry and survey data

Relatively simple foraging radius models have the potential to generate predictive distributions for a large number of species rapidly, thus providing a cost-effective alternative to large-scale surveys or complex modelling approaches. Their effectiveness, however, remains largely untested. Here we compare foraging radius distribution models for all breeding seabirds in Ireland, to distributions of empirical data collected from tracking studies and aerial surveys. At the local/colony level, we compared foraging radius distributions to GPS tracking data from seabirds with short (Atlantic puffin Fratercula arctica, and razorbill Alca torda) and long (Manx shearwater Puffinus puffinus, and European storm-petrel Hydrobates pelagicus) foraging ranges. At the regional/national level, we compared foraging radius distributions to extensive aerial surveys conducted over a two-year period. Foraging radius distributions were significantly positively correlated with tracking data for all species except Manx shearwater. Correlations between foraging radius distributions and aerial survey data were also significant, but generally weaker than those for tracking data. Correlations between foraging radius distributions and aerial survey data were benchmarked against generalised additive models (GAMs) of the aerial survey data that included a range of environmental covariates. While GAM distributions had slightly higher correlations with aerial survey data, the results highlight that the foraging radius approach can be a useful and pragmatic approach for assessing breeding distributions for many seabird species. The approach is likely to have acceptable utility in complex, temporally variable ecosystems and when logistic and financial resources are limited.     

Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling

Modeling the distributions of species, especially of invasive species in non‐native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species–environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space (i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our ‘best’ model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed.     

Modeling potential invasion of stored-product pest Cryptamorpha desjardinsii (Guérin-Méneville, 1844) (Coleoptera: Silvanidae) with emphasis on newly recorded areas

Desjardin’s flat bark beetle – Cryptamorpha desjardinsii (Guérin-Méneville, 1844) – is a widespread invasive species that can become a stored products pest and threaten native species. Our objectives were to provide the first detailed records of this species to South America and New Caledonia, and model suitable areas of C. desjardinsii globally based on different Ecological Niche Models (ENM). We combine several techniques to predict the ecological niche of C. desjardinsii using distribution records and a set of environmental predictor variables. Overall, the species appears to be well established in tropical and subtropical regions, particularly in islands, with new and previous records predicted by the consensus model. In South America, the driest and coldest regions were predicted to be less suitable for the occurrence of C. desjardinsii. In the current global spread context, these findings are pivotal to predicting areas of high risk of invasion of C. desjardinsii.     

Where do nivicolous myxomycetes occur? – Modeling the potential worldwide distribution of Physarum albescens

To identify potentially suitable areas for the mostly alpine ecological guild of nivicolous (snowbank) myxomycetes, the worldwide distribution of a distinct morphospecies, Physarum albescens, was modelled with a correlative spatial approach using the software MaxEnt from 537 unique occurrence points. Three models were developed, first with only the 19 bioclimatic variables plus elevation from the WorldClim database, second with regularization to correct for pseudo-absence, and third with additional categorical environmental layer on snow cover. All three models showed high mean AUC (area under the curve) values (>0.970). Output maps were comparable, with the third model perhaps the most realistic. For this model, snow cover, precipitation of the coldest quarter (of the year), and elevation predicted best the distribution of Ph. albescens. Elevation alone is a good predictor only in some regions, since (i) elevation of the occurrence points decreases with increasing latitude, and (ii) elevation wrongly predicts the species’ occurrence in arid mountain ranges. The model showed mountains in humid climates with highest incidence, which confirmed field studies: a long-lasting snow cover fluctuating with comparatively mild summers is the decisive factor. As such, the model can serve as a predictive map where fructifications of nivicolous myxomycetes can be expected. Limitations of the model are discussed: cryptic speciation within a morphospecies, including the evolution of reproductively isolated units which may lead to local adaptation and niche differentiation, and wider ranges for myxamoebal populations.