Fine‐scale spatial distribution of murundus structures in the semi‐arid region of Brazil

Murundus are earth mounds widespread in most landscapes in the semi‐arid region of Brazil. Evidence obtained from predictive modelling has suggested a termite origin for these structures, opening up new opportunities for further research. Distribution of densely packed murundus at larger spatial scales is most related to climatic regime and soil nutrient availability. However, factors and processes underlying their distribution and density at smaller spatial scales are not yet fully understood. In this study, we adopted an approach based on mapping point data using high‐resolution satellite imagery, multi‐scale second‐order analysis and general linear models to examine the fine‐scale spatial distribution and density of murundus. Our results suggest that the distribution of those structures within densely packed areas is regulated by more than one process acting or interacting across multiple spatial scales. All densely packed murundus showed a significant regular distribution at the distance scale of up to 50 m radially and a completely random distribution across all other upper distance scales. We interpret the regular pattern as a result of competition for foraging territories between different termite colonies during the process formation of densely packed murundus. The random pattern at larger distance scales (above 50 m radially) can be attributed to habitat selection preferences by termite species builders of murundus mediated by local environmental resources and conditions (i.e. availability of food resources and nesting and open habitat), which would be randomly distributed in space. Thus, at finer spatial scales murundus distributions are associated with biotic interactions acting on an abiotic template. On the basis of significant linear correlations, we suggest that the density of murundus is strongly related to local temperature regime with soil‐type influencing its effect on the murundus densities. Our findings provide novel evidences that mound‐building termites are involved in the formation of murundus in the semi‐arid region of Brazil.     

The predictability of a lake phytoplankton community,over time‐scales of hours to years

Forecasting changes to ecological communities is one of the central challenges in ecology. However, nonlinear dependencies, biotic interactions and data limitations have limited our ability to assess how predictable communities are. Here, we used a machine learning approach and environmental monitoring data (biological, physical and chemical) to assess the predictability of phytoplankton cell density in one lake across an unprecedented range of time‐scales. Communities were highly predictable over hours to months: model R² decreased from 0.89 at 4 hours to 0.74 at 1 month, and in a long‐term dataset lacking fine spatial resolution, from 0.46 at 1 month to 0.32 at 10 years. When cyanobacterial and eukaryotic algal cell densities were examined separately, model‐inferred environmental growth dependencies matched laboratory studies, and suggested novel trade‐offs governing their competition. High‐frequency monitoring and machine learning can set prediction targets for process‐based models and help elucidate the mechanisms underlying ecological dynamics.     

When the company does not matter: High‐quality ant seed‐disperser does not drive the spatial distribution of large‐seeded myrmecochorous plants

Mutualisms are one of the main forces shaping species spatial patterns at all geographic scales. In generalised mutualisms, however, the dependence among partners is highly variable in time and space, and therefore, the effect of diffuse mutualisms on species geographic distributions is unclear. Myrmecochorous seeds in Brazilian semi‐arid vegetation are dispersed by several ant species. However, large‐seeded species are especially dependent on dispersal by the giant ant Dinoponera quadriceps, which is the main disperser of such diaspores and the species that provide the longest dispersal distance among ant species in this system. Hence, we hypothesise that the presence of D. quadriceps shapes the distribution of large‐seeded, but not the distribution of small‐seeded myrmecochorous plant species. To evaluate this hypothesis, we modelled the potential distribution of two large‐seeded (which are predominantly dispersed by D. quadriceps) and two small‐seeded (which are barely dispersed by D. quadriceps) Euphorbiaceae species and the potential distribution of D. quadriceps. We analysed the relationship between the occurrence suitability of D. quadriceps and the occurrence suitability of plant species. We found that the potential distribution of both large‐seeded and small‐seeded myrmecochorous plants was unrelated to D. quadriceps occurrence suitability. It means that the disproportional benefits provided by high‐quality disperser at local scales may not emerge at broader geographical scales. In Caatinga vegetation, diaspores are submitted to strong abiotic filters that constraint seed germination and establishment after the dispersal phase. Such abiotic filters may dilute the initial benefit provided by long‐distance dispersers. Therefore, we suggest that in dry environments like the Caatinga, the benefits of long‐distance removals should be outweighed by the risk of reach new habitats with unfavourable conditions for germination and establishment.     

Performance of predictive models in marine benthic environments based on predictions of sponge distribution on the Australian continental shelf

This study tested the performance of 15 predictive models in predicting the distribution of sponge assemblages on the Australian continental shelf using a common set of marine environmental variables. The models included traditional regression and more recently developed machine learning models. The results demonstrate that the spatial distribution of sponge assemblages can be successfully predicted, although the effectiveness of predictions varied among models. Overall, machine learning models achieved the best prediction performance. The direct variable of bottom-water temperature and the resource variables that describe bottom-water nutrient status were found to be useful surrogates for the distribution of sponge assemblages at the broad regional scale. A new method of deriving pseudo-absence data (weighted pseudo-absence) was compared with random pseudo-absence data — the new data were able to improve modelling performance for all the models both in terms of statistics (~ 10%) and in the predicted spatial distributions. Results from this study will further refine modelling methods used to predict the spatial distribution of marine biota at broad spatial scales, an outcome especially relevant to managers of marine resources.     

From species distributions to meta‐communities

The extent that biotic interactions and dispersal influence species ranges and diversity patterns across scales remains an open question. Answering this question requires framing an analysis on the frontier between species distribution modelling (SDM), which ignores biotic interactions and dispersal limitation, and community ecology, which provides specific predictions on community and meta‐community structure and resulting diversity patterns such as species richness and functional diversity. Using both empirical and simulated datasets, we tested whether predicted occurrences from fine‐resolution SDMs provide good estimates of community structure and diversity patterns at resolutions ranging from a resolution typical of studies within reserves (250 m) to that typical of a regional biodiversity study (5 km). For both datasets, we show that the imprint of biotic interactions and dispersal limitation quickly vanishes when spatial resolution is reduced, which demonstrates the value of SDMs for tracking the imprint of community assembly processes across scales.     

Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA

Preserving biodiversity is a global challenge requiring data on species’ distribution and abundance over large geographic and temporal scales. However, traditional methods to survey mobile species’ distribution and abundance in marine environments are often inefficient, environmentally destructive, or resource‐intensive. Metabarcoding of environmental DNA (eDNA) offers a new means to assess biodiversity and on much larger scales, but adoption of this approach for surveying whole animal communities in large, dynamic aquatic systems has been slowed by significant unknowns surrounding error rates of detection and relevant spatial resolution of eDNA surveys. Here, we report the results of a 2.5 km eDNA transect surveying the vertebrate fauna present along a gradation of diverse marine habitats associated with a kelp forest ecosystem. Using PCR primers that target the mitochondrial 12S rRNA gene of marine fishes and mammals, we generated eDNA sequence data and compared it to simultaneous visual dive surveys. We find spatial concordance between individual species’ eDNA and visual survey trends, and that eDNA is able to distinguish vertebrate community assemblages from habitats separated by as little as ~60 m. eDNA reliably detected vertebrates with low false‐negative error rates (1/12 taxa) when compared to the surveys, and revealed cryptic species known to occupy the habitats but overlooked by visual methods. This study also presents an explicit accounting of false negatives and positives in metabarcoding data, which illustrate the influence of gene marker selection, replication, contamination, biases impacting eDNA count data and ecology of target species on eDNA detection rates in an open ecosystem.     

Influence of fire history on small mammal distributions: insights from a 100‐year post‐fire chronosequence

Aim Fire affects the structure and dynamics of ecosystems world‐wide, over long time periods (decades and centuries) and at large spatial scales (landscapes and regions). A pressing challenge for ecologists is to develop models that explain and predict faunal responses to fire at broad temporal and spatial scales. We used a 105‐year post‐fire chronosequence to investigate small mammal responses to fire across an extensive area of ‘tree mallee’ (i.e. vegetation characterized by small multi‐stemmed eucalypts). Location The Murray Mallee region (104,000 km²) of semi‐arid Australia. Methods First, we surveyed small mammals at 260 sites and explored the fire responses of four species using nonlinear regression models. Second, we assessed the predictive accuracy of models using cross‐validation and by testing with independent data. Third, we examined our results in relation to an influential model of animal succession, the habitat accommodation model. Results Two of four study species showed a clear response to fire history. The distribution of the Mallee Ningaui Ningaui yvonneae, a carnivorous marsupial, was strongly associated with mature vegetation characterized by its cover of hummock grass. The occurrence of breeding females was predicted to increase up to 40–105 years post‐fire, highlighting the extensive time periods over which small mammal populations may be affected by fire. Evaluation of models for N. yvonneae demonstrated that accurate predictions of species occurrence can be made from fire history and vegetation data, across large geographical areas. The introduced House Mouse Mus domesticus was the only species positively associated with recently burnt vegetation. Main conclusions Understanding the impact of fire over long time periods will benefit ecological and conservation management. In this example, tracts of long‐unburnt mallee vegetation were identified as important habitat for a fire‐sensitive native mammal. Small mammal responses to fire can be predicted accurately at broad spatial scales; however, a conceptual model of post‐fire change in community structure developed in temperate Australia is not, on its own, sufficient for small mammals in semi‐arid systems.     

Fine‐scale Spatial Genetic Structure of Ten Dipterocarp Tree Species in a Bornean Rain Forest

Fine‐scale spatial genetic structure is increasingly recognized as an important factor in the studies of tropical forest trees as it influences genetic diversity of local populations. The biologic mechanisms that generate fine‐scale spatial genetic structure are not fully understood. We studied fine‐scale spatial genetic structure in ten coexisting dipterocarp tree species in a Bornean rain forest using microsatellite markers. Six of the ten species showed statistically significant fine‐scale spatial genetic structure. Fine‐scale spatial genetic structure was stronger at smaller spatial scales (≤ 100 m) than at larger spatial scales (> 100 m) for each species. Multiple regression analysis suggested that seed dispersal distance was important at the smaller spatial scale. At the larger scale (> 100 m) and over the entire sample range (0–1000 m), pollinators and spatial distribution of adult trees were more important determinants of fine‐scale spatial genetic structure. Fine‐scale spatial genetic structure was stronger in species pollinated by less mobile small beetles than in species pollinated by the more mobile giant honeybee (Apis dorsata). It was also stronger in species where adult tree distributions were more clumped. The hypothesized mechanisms underlying the negative correlation between clump size and fine‐scale spatial genetic structure were a large overlap among seed shadows and genetic drift within clumped species.     

Urbanization drives cross‐taxon declines in abundance and diversity at multiple spatial scales

The increasing urbanization process is hypothesized to drastically alter (semi‐)natural environments with a concomitant major decline in species abundance and diversity. Yet, studies on this effect of urbanization, and the spatial scale at which it acts, are at present inconclusive due to the large heterogeneity in taxonomic groups and spatial scales at which this relationship has been investigated among studies. Comprehensive studies analysing this relationship across multiple animal groups and at multiple spatial scales are rare, hampering the assessment of how biodiversity generally responds to urbanization. We studied aquatic (cladocerans), limno‐terrestrial (bdelloid rotifers) and terrestrial (butterflies, ground beetles, ground‐ and web spiders, macro‐moths, orthopterans and snails) invertebrate groups using a hierarchical spatial design, wherein three local‐scale (200 m × 200 m) urbanization levels were repeatedly sampled across three landscape‐scale (3 km × 3 km) urbanization levels. We tested for local and landscape urbanization effects on abundance and species richness of each group, whereby total richness was partitioned into the average richness of local communities and the richness due to variation among local communities. Abundances of the terrestrial active dispersers declined in response to local urbanization, with reductions up to 85% for butterflies, while passive dispersers did not show any clear trend. Species richness also declined with increasing levels of urbanization, but responses were highly heterogeneous among the different groups with respect to the richness component and the spatial scale at which urbanization impacts richness. Depending on the group, species richness declined due to biotic homogenization and/or local species loss. This resulted in an overall decrease in total richness across groups in urban areas. These results provide strong support to the general negative impact of urbanization on abundance and species richness within habitat patches and highlight the importance of considering multiple spatial scales and taxa to assess the impacts of urbanization on biodiversity.     

Spatial distribution of aquatic marine fungi across the western Arctic and sub‐arctic

Fungi are important parasites of primary producers and nutrient cyclers in aquatic ecosystems. In the Pacific‐Arctic domain, fungal parasitism is linked to light intensities and algal stress that can elevate disease incidence on algae and reduce diatom concentrations. Fungi are vastly understudied in the marine realm and knowledge of their function is constrained by the current understanding of fungal distribution and drivers on global scales. To investigate the spatial distribution of fungi in the western Arctic and sub‐Arctic, we used high throughput methods to sequence 18S rRNA, cloned and sequenced 28S rRNA and microscopically counted chytrid‐infected diatoms. We identified a broad distribution of fungal taxa predominated by Chytridiomycota and Dikarya. Phylogenetic analysis of our Chytridiomycota clones placed Arctic marine fungi sister to the order Lobulomycetales. This clade of fungi predominated in fungal communities under ice with low snowpack. Microscopic examination of fixed seawater and sea ice samples revealed chytrids parasitizing diatoms collected across the Arctic that notably infected 25% of a single diatom species in the Bering Sea. The Pezizomycotina comprised > 95% of eukaryotic sequence reads in Greenland, providing preliminary evidence for osmotrophs being a substitute for algae as the base of food webs.     

Sex‐biased transcriptome divergence along a latitudinal gradient

Sex‐dependent gene expression is likely an important genomic mechanism that allows sex‐specific adaptation to environmental changes. Among Drosophila species, sex‐biased genes display remarkably consistent evolutionary patterns; male‐biased genes evolve faster than unbiased genes in both coding sequence and expression level, suggesting sex differences in selection through time. However, comparatively little is known of the evolutionary process shaping sex‐biased expression within species. Latitudinal clines offer an opportunity to examine how changes in key ecological parameters also influence sex‐specific selection and the evolution of sex‐biased gene expression. We assayed male and female gene expression in Drosophila serrata along a latitudinal gradient in eastern Australia spanning most of its endemic distribution. Analysis of 11 631 genes across eight populations revealed strong sex differences in the frequency, mode and strength of divergence. Divergence was far stronger in males than females and while latitudinal clines were evident in both sexes, male divergence was often population specific, suggesting responses to localized selection pressures that do not covary predictably with latitude. While divergence was enriched for male‐biased genes, there was no overrepresentation of X‐linked genes in males. By contrast, X‐linked divergence was elevated in females, especially for female‐biased genes. Many genes that diverged in D. serrata have homologs also showing latitudinal divergence in Drosophila simulans and Drosophila melanogaster on other continents, likely indicating parallel adaptation in these distantly related species. Our results suggest that sex differences in selection play an important role in shaping the evolution of gene expression over macro‐ and micro‐ecological spatial scales.     

Scale‐dependence of vegetation‐environment relationships in semi‐natural grasslands

Questions: Which environmental and management factors determine plant species composition in semi‐natural grasslands within a local study area? Are vegetation and explanatory factors scale‐dependent? Location: Semi‐natural grasslands in Lærdal, Sognog Fjordane County, western Norway. Methods: We recorded plant species composition and explanatory variables in six grassland sites using a hierarchically nested sampling design with three levels: plots randomly placed within blocks selected within sites. We evaluated vegetation‐environment relationships at all three levels by means of DCA ordination and split‐plot GLM analyses. Results: The most important complex gradient determining variation in grassland species composition showed a broad‐scale relationship with management. Soil moisture conditions were related to vegetation variation on block scale, whereas element concentrations in the soil were significantly related to variation in species composition on all spatial scales. Our results show that vegetation‐environment relationships are dependent on the scale of observation. We suggest that scale‐related (and therefore methodological) issues may explain the wide range of vegetation‐environment relationships reported in the literature, for semi‐natural grassland in particular but also for other ecosystems. Conclusions: Interpretation of the variation in species composition of semi‐natural grasslands requires consideration of the spatial scales on which important environmental variables vary.     

Characterizing habitat suitability for a central‐place forager in a dynamic marine environment

Characterizing habitat suitability for a marine predator requires an understanding of the environmental heterogeneity and variability over the range in which a population moves during a particular life cycle. Female California sea lions (Zalophus californianus) are central‐place foragers and are particularly constrained while provisioning their young. During this time, habitat selection is a function of prey availability and proximity to the rookery, which has important implications for reproductive and population success. We explore how lactating females may select habitat and respond to environmental variability over broad spatial and temporal scales within the California Current System. We combine near‐real‐time remotely sensed satellite oceanography, animal tracking data (n = 72) from November to February over multiple years (2003–2009) and Generalized Additive Mixed Models (GAMMs) to determine the probability of sea lion occurrence based on environmental covariates. Results indicate that sea lion presence is associated with cool ( <14°C ), productive waters, shallow depths, increased eddy activity, and positive sea‐level anomalies. Predictive habitat maps generated from these biophysical associations suggest winter foraging areas are spatially consistent in the nearshore and offshore environments, except during the 2004–2005 winter, which coincided with an El Niño event. Here, we show how a species distribution model can provide broadscale information on the distribution of female California sea lions during an important life history stage and its implications for population dynamics and spatial management.     

Long‐term panmixia in a cosmopolitan Indo‐Pacific coral reef fish and a nebulous genetic boundary with its broadly sympatric sister species

Phylogeographical studies have shown that some shallow‐water marine organisms, such as certain coral reef fishes, lack spatial population structure at oceanic scales, despite vast distances of pelagic habitat between reefs and other dispersal barriers. However, whether these dispersive widespread taxa constitute long‐term panmictic populations across their species ranges remains unknown. Conventional phylogeographical inferences frequently fail to distinguish between long‐term panmixia and metapopulations connected by gene flow. Moreover, marine organisms have notoriously large effective population sizes that confound population structure detection. Therefore, at what spatial scale marine populations experience independent evolutionary trajectories and ultimately species divergence is still unclear. Here, we present a phylogeographical study of a cosmopolitan Indo‐Pacific coral reef fish Naso hexacanthus and its sister species Naso caesius, using two mtDNA and two nDNA markers. The purpose of this study was two‐fold: first, to test for broad‐scale panmixia in N. hexacanthus by fitting the data to various phylogeographical models within a Bayesian statistical framework, and second, to explore patterns of genetic divergence between the two broadly sympatric species. We report that N. hexacanthus shows little population structure across the Indo‐Pacific and a range‐wide, long‐term panmictic population model best fit the data. Hence, this species presently comprises a single evolutionary unit across much of the tropical Indian and Pacific Oceans. Naso hexacanthus and N. caesius were not reciprocally monophyletic in the mtDNA markers but showed varying degrees of population level divergence in the two nuclear introns. Overall, patterns are consistent with secondary introgression following a period of isolation, which may be attributed to oceanographic conditions of the mid to late Pleistocene, when these two species appear to have diverged.     

Scale‐Dependent Spatial Match between Fruits and Fruit‐eating Birds during the Breeding Season in Yungas Andean Forests

The multi‐scale spatial match between bird and food abundances is a main driver of the structure of fruit‐eating bird assemblages. We explored how the activity of fruit‐eating birds was influenced by the abundance of fruits at the local and landscape scales in Andean mountain forests during the breeding season, when most birds forage close to their nest. We measured: (1) the spatial scale of variation in the abundance of fruits, (2) the spatial scale of variation in the activity of fruit‐eating birds, and (3) the spatial match between both variables. The sampling design consisted of eleven 1.2‐ha sites, each subdivided into 30 cells of 20 × 20 m, where we sampled fruits and fruit‐eating birds. We found that fruit consumption, and to a lesser extent bird abundance, were associated with local spatial variation in abundance of selected fruit species. However, fruit‐eating birds did not modify their spatial distribution in the landscape following changes in availability of these fruits. Our study shows that fruit‐eating birds detect local spatial variation in fruit availability in their home breeding ranges, and exploit patches with large clusters of selected fruits. However, it may be unprofitable for breeding birds to stray too far from their nests to exploit fruit‐rich patches, accounting for the absence of fruit tracking at larger spatial scales.     

Machine learning of large‐scale spatial distributions of wild turkeys with high‐dimensional environmental data

Species distribution modeling often involves high‐dimensional environmental data. Large amounts of data and multicollinearity among covariates impose challenges to statistical models in variable selection for reliable inferences of the effects of environmental factors on the spatial distribution of species. Few studies have evaluated and compared the performance of multiple machine learning (ML) models in handling multicollinearity. Here, we assessed the effectiveness of removal of correlated covariates and regularization to cope with multicollinearity in ML models for habitat suitability. Three machine learning algorithms maximum entropy (MaxEnt), random forests (RFs), and support vector machines (SVMs) were applied to the original data (OD) of 27 landscape variables, reduced data (RD) with 14 highly correlated covariates being removed, and 15 principal components (PC) of the OD accounting for 90% of the original variability. The performance of the three ML models was measured with the area under the curve and continuous Boyce index. We collected 663 nonduplicated presence locations of Eastern wild turkeys (Meleagris gallopavo silvestris) across the state of Mississippi, United States. Of the total locations, 453 locations separated by a distance of ≥2 km were used to train the three ML algorithms on the OD, RD, and PC data, respectively. The remaining 210 locations were used to validate the trained ML models to measure ML performance. Three ML models had excellent performance on the RD and PC data. MaxEnt and SVMs had good performance on the OD data, indicating the adequacy of regularization of the default setting for multicollinearity. Weak learning of RFs through bagging appeared to alleviate multicollinearity and resulted in excellent performance on the OD data. Regularization of ML algorithms may help exploratory studies of the effects of environmental factors on the spatial distribution and habitat suitability of wildlife.     

Toward community predictions: Multi‐scale modelling of mountain breeding birds' habitat suitability,landscape preferences,and environmental drivers

Across a large mountain area of the western Swiss Alps, we used occurrence data (presence‐only points) of bird species to find suitable modelling solutions and build reliable distribution maps to deal with biodiversity and conservation necessities of bird species at finer scales. We have performed a multi‐scale method of modelling, which uses distance, climatic, and focal variables at different scales (neighboring window sizes), to estimate the efficient scale of each environmental predictor and enhance our knowledge on how birds interact with their complex environment. To identify the best radius for each focal variable and the most efficient impact scale of each predictor, we have fitted univariate models per species. In the last step, the final set of variables were subsequently employed to build ensemble of small models (ESMs) at a fine spatial resolution of 100 m and generate species distribution maps as tools of conservation. We could build useful habitat suitability models for the three groups of species in the national red list. Our results indicate that, in general, the most important variables were in the group of bioclimatic variables including “Bio11” (Mean Temperature of Coldest Quarter), and “Bio 4” (Temperature Seasonality), then in the focal variables including “Forest”, “Orchard”, and “Agriculture area” as potential foraging, feeding and nesting sites. Our distribution maps are useful for identifying the most threatened species and their habitat and also for improving conservation effort to locate bird hotspots. It is a powerful strategy to improve the ecological understanding of the distribution of bird species in a dynamic heterogeneous environment.     

Large herbivores maintain a two‐phase herbaceous vegetation mosaic in a semi‐arid savanna

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Extensive land cover change across Arctic–Boreal Northwestern North America from disturbance and climate forcing

A multitude of disturbance agents, such as wildfires, land use, and climate‐driven expansion of woody shrubs, is transforming the distribution of plant functional types across Arctic–Boreal ecosystems, which has significant implications for interactions and feedbacks between terrestrial ecosystems and climate in the northern high‐latitude. However, because the spatial resolution of existing land cover datasets is too coarse, large‐scale land cover changes in the Arctic–Boreal region (ABR) have been poorly characterized. Here, we use 31 years (1984–2014) of moderate spatial resolution (30 m) satellite imagery over a region spanning 4.7 × 10⁶ km² in Alaska and northwestern Canada to characterize regional‐scale ABR land cover changes. We find that 13.6 ± 1.3% of the domain has changed, primarily via two major modes of transformation: (a) simultaneous disturbance‐driven decreases in Evergreen Forest area (?14.7 ± 3.0% relative to 1984) and increases in Deciduous Forest area (+14.8 ± 5.2%) in the Boreal biome; and (b) climate‐driven expansion of Herbaceous and Shrub vegetation (+7.4 ± 2.0%) in the Arctic biome. By using time series of 30 m imagery, we characterize dynamics in forest and shrub cover occurring at relatively short spatial scales (hundreds of meters) due to fires, harvest, and climate‐induced growth that are not observable in coarse spatial resolution (e.g., 500 m or greater pixel size) imagery. Wildfires caused most of Evergreen Forest Loss and Evergreen Forest Gain and substantial areas of Deciduous Forest Gain. Extensive shifts in the distribution of plant functional types at multiple spatial scales are consistent with observations of increased atmospheric CO₂ seasonality and ecosystem productivity at northern high‐latitudes and signal continental‐scale shifts in the structure and function of northern high‐latitude ecosystems in response to climate change.     

High‐resolution DNA melt‐curve analysis for cost‐effective mass screening of pairwise species interactions

Ecological studies of pairwise interactions are constrained by the methods available for rapid species identification of the interacting organisms. The resolution of data required to characterize species interaction networks at multiple spatio‐temporal scales can be intensive, and therefore laborious and costly to collect. We explore the utility of high‐resolution DNA melt‐curve analysis (HRM) as a rapid species identification method. An approach was developed to identify organisms at the pairwise interaction level, with particular application to cryptic species interactions that are traditionally difficult to study. Here, we selected a challenging application; to identify the presence/absence of pathogenic fungi (Sporothrix inflata, Ophiostoma nigrocarpum and Ophiostoma galeiforme) transported by bark beetle vectors (Hylastes ater and Hylurgus ligniperda). The technique was able to distinguish between different species of DNA within a single, pooled sample. In test applications, HRM was effective in the mass screening and identification of pathogenic fungal species carried by many individual bark beetle vectors (n = 455 beetles screened) across large geographic scales. For two of the fungal species, there was no difference in the frequency of association with either of their vectors, but for the third fungal species there was a shift in vector–pathogen associations across locations. This technique allows rapid, mass screening and characterization of species interactions at a fraction of the time and cost of traditional methods. It is anticipated that this method can be readily applied to explore other cryptic species interactions, or other studies requiring rapid generation of large data sets and/or high‐throughput efficiency.