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Data from: Estimating the relative fitness of escaped farmed salmon offspring in the wild and modeling the consequences of invasion for wild populations
Throughout their native range, wild Atlantic salmon populations are threatened by hybridization and introgression with escapees from net-pen salmon aquaculture. Although domestic-wild hybrid offspring have shown reduced fitness in lab and field experiments, consequential impacts on population abundance and genetic integrity remain difficult to predict in the field, in part because the strength of selection against domestic offspring is often unknown and context-dependent. Here we follow a single large escape event of farmed Atlantic salmon in southern Newfoundland and monitor changes in the in-river proportions of hybrids and feral individuals over time using genetically-based hybrid identification. Over a three-year period following the escape, the overall proportion of wild parr increased consistently (total wild proportion of 71.6%, 75.1%, 87.5% each year, respectively), with subsequent declines in feral (genetically pure farmed individuals originating from escaped, farmed adults) and hybrid parr. We quantify the strength of selection against parr of aquaculture ancestry and explore the genetic and demographic consequences for populations in the region. Within-cohort changes in the relative proportions of feral and F1 parr suggest reduced relative survival compared to wild individuals over the first (0.15 and 0.81 for feral and F1, respectively), and second years of life (0.26, 0.83). These relative survivorship estimates were used to inform an individual-based salmon eco-genetic model to project changes in adult abundance and overall allele frequency across three invasion scenarios ranging from short-term to long-term invasion and three relative survival scenarios. Modeling results indicate that total population abundance and time to recovery were greatly affected by relative survivorship and predict significant declines in wild population abundance under continued large escape events and calculated survivorship. Overall this work demonstrates the importance of estimating the strength of selection against domestic offspring in the wild to predict the long-term impact of farmed salmon escape events on wild populations.
Data from: Morphological, phylogenetic, and ecological diversity of the new model species Setaria viridis (Poaceae: Paniceae) and its close relatives
Premise of the study: Species limits of the emerging model organism Setaria viridis (tribe Paniceae, subtribe Cenchrinae) are not well defined. It is thought to be related to S. adhaerens, S. faberi, S. verticillata, and S. verticilliformis and in North America occurs with the morphologically similar S. pumila. An integrated approach was taken to evaluate its variation and relationships with the other taxa. Methods: Statistical morphology, flow cytometry, molecular phylogenetics, and growth experiments were employed to examine the group's physical variation, polyploidy, evolutionary relationships, and drought ecology, respectively. Key results: Setaria viridis contributed one genome to the tetraploids S. faberi, S. verticillata, and S. verticilliformis; the other genome of the latter two was contributed by S. adhaerens. Setaria pumila is unrelated. Morphologically, S. viridis is most similar to S. faberi, but all tested accessions of S. viridis were diploid, whereas those of S. faberi were all tetraploid. Principal component analysis of 70 morphological characters consistently separated S. viridis from S. faberi, largely by spikelet characters. The diagnostic morphological characters are not affected by watering. Setaria faberi is far more sensitive to drought, in terms of mortality and morphological stunting, than S. viridis or S. pumila. Conclusions: Setaria viridis is a diploid species and has contributed to several polyploid derivatives. The most morphologically similar of the polyploids is S. faberi, which differs in spikelet features, phylogenetics, genome size, and ecological response to drought. Researchers using field-collected S. viridis as a model organism will benefit from the clear delimitation provided in this study.
Data from: Bioenergetics modeling of the annual consumption of zooplankton by pelagic fish feeding in the Northeast Atlantic
The present study uses bioenergetics modeling to estimate the annual consumption of the main zooplankton groups by some of the most commercially important planktivorous fish stocks in the Northeast Atlantic, namely Norwegian spring-spawning (NSS) herring (Clupea harengus), blue whiting (Micromesistius poutassou) and NEA mackerel (Scomber scombrus). The data was obtained from scientific surveys in the main feeding area (Norwegian Sea) in the period 2005-2010. By incorporating novel information about ambient temperature, seasonal growth and changes in the diet from stomach content analyses, annual consumption of the different zooplankton groups by pelagic fish is estimated. The present study estimates higher consumption estimates than previous studies for the three species and suggests that fish might have a greater impact on the zooplankton community as foragers. This way, NEA mackerel, showing the highest daily consumption rates, and NSS herring, annually consume around 10 times their total biomass, whereas blue whiting consume about 6 times their biomass in zooplankton. The three species were estimated to consume an average of 135 million (M) tonnes of zooplankton each year, consisting of 53-85 M tonnes of copepods, 20-32 M tonnes of krill, 8-42 M tonnes of appendicularians and 0.2-1.2 M tonnes of fish, depending on the year. For NSS herring and NEA mackerel the main prey groups are calanoids and appendicularians, showing a peak in consumption during June and June-July, respectively, and suggesting high potential for inter-specific feeding competition between these species. In contrast, blue whiting maintain a low consumption rate from April to September, consuming mainly larger euphausiids. Our results suggest that the three species can coexist regardless of their high abundance, zooplankton consumption rates and overlapping diet. Accordingly, the species might have niche segregation, as they are species specific, showing annual and inter-annual variability in total consumption of the different prey species. These estimates and their inter-annual and inter-specific variation are fundamental for understanding fundamental pelagic predator-prey interactions as well as to inform advanced multispecies ecosystem models.
Data from: A priori and a posteriori approaches for finding genes of evolutionary interest in non-model species: osmoregulatory genes in the kidney transcriptome of the desert rodent Dipodomys spectabilis (banner-tailed kangaroo rat)
One common goal in evolutionary biology is the identification of genes underlying adaptive traits of evolutionary interest. Recently next-generation sequencing techniques have greatly facilitated such evolutionary studies in species otherwise depauperate of genomic resources. Kangaroo rats (Dipodomys sp.) serve as exemplars of adaptation in that they inhabit extremely arid environments, yet require no drinking water because of ultra-efficient kidney function and osmoregulation. As a basis for identifying water conservation genes in kangaroo rats, we conducted a priori bioinformatics searches in model rodents (Mus musculus and Rattus norvegicus) to identify candidate genes with known or suspected osmoregulatory function. We then obtained 446,758 reads via 454 pyrosequencing to characterize genes expressed in the kidney of banner-tailed kangaroo rats (Dipodomys spectabilis). We also determined candidates a posteriori by identifying genes that were overexpressed in the kidney. The kangaroo rat sequences revealed nine different a priori candidate genes predicted from our Mus and Rattus searches, as well as 32 a posteriori candidate genes that were overexpressed in kidney. Mutations in two of these genes, Slc12a1 and Slc12a3, cause human renal diseases that result in the inability to concentrate urine. These genes are likely key determinants of physiological water conservation in desert rodents.
Data from: Use of hidden Markov capture-recapture models to estimate abundance in presence of uncertainty: application to estimating the prevalence of hybrids in animal populations
Estimating the relative abundance (prevalence) of different population segments is a key step in addressing fundamental research questions in ecology, evolution, and conservation. The raw percentage of individuals in the sample (naive prevalence) is generally used for this purpose, but it is likely to be subject to two main sources of bias. First, the detectability of individuals is ignored; second, classification errors may occur due to some inherent limits of the diagnostic methods. We developed a hidden Markov (also known as multievent) capture–recapture model to estimate prevalence in free‐ranging populations accounting for imperfect detectability and uncertainty in individual's classification. We carried out a simulation study to compare naive and model‐based estimates of prevalence and assess the performance of our model under different sampling scenarios. We then illustrate our method with a real‐world case study of estimating the prevalence of wolf (Canis lupus) and dog (Canis lupus familiaris) hybrids in a wolf population in northern Italy. We showed that the prevalence of hybrids could be estimated while accounting for both detectability and classification uncertainty. Model‐based prevalence consistently had better performance than naive prevalence in the presence of differential detectability and assignment probability and was unbiased for sampling scenarios with high detectability. We also showed that ignoring detectability and uncertainty in the wolf case study would lead to underestimating the prevalence of hybrids. Our results underline the importance of a model‐based approach to obtain unbiased estimates of prevalence of different population segments. Our model can be adapted to any taxa, and it can be used to estimate absolute abundance and prevalence in a variety of cases involving imperfect detection and uncertainty in classification of individuals (e.g., sex ratio, proportion of breeders, and prevalence of infected individuals).
Data from: Alternative tree-cover states of the boreal ecosystem: a conceptual model
Aim: Previous analyses of remotely-sensed data detected the multimodality of the tree-cover distribution of the boreal forest, and identified areas with potentially alternative tree-cover states. This paper aims at investigating the causes of multimodality and multistability of the boreal forest, their influence on the asymmetric tree-species distribution between Eurasia and North America, and whether multistability could be associated with recent greening trends in LAI/NDVI. Location: Eurasian and North American boreal forests. Time period: 2000--2010. Major taxa studied: Boreal forest plant functional types. Methods: We employ a conceptual model based on tree-species competition to simulate the sensitivity of tree cover to stochastic disturbances and to changes in environmental factors. We include different plant functional types based on survival adaptations, and force the model with remotely-sensed environmental data. We analyse the model as a dynamical system. We use metrics from statistics and information theory to compare the detection of alternative tree-cover states and greening trends in LAI/NDVI. Results: We find that multimodality and multistability can emerge through competition between different plant functional types. Additionally, our model is able to reproduce the asymmetry in tree-species distribution between Eurasia and North America. Moreover, changes in permafrost distribution can be associated with phenomenological bifurcation points of the model. Finally, we find that the detection of multistable areas is not affected by recent vegetation trends, whereas shifts between alternative states could have affected the greening trends. Main conclusions: Tree-cover multistability in the boreal region can emerge through competition between species subject to periodic disturbances. Changes in permafrost thaw and distribution could be responsible for the asymmetry in tree-species distribution between North America and Eurasia. Climate change and permafrost degradation could cause shifts in tree-cover states and dominant species. Recent vegetation greening trends in multistable areas could have been affected by shifts between alternative states.
Data from: Modelling mobile agent-based ecosystem services using kernel weighted predictors
1. Agriculture benefits from ecosystem services provided by mobile agents, such as biological pest control by natural enemies and pollination by bees. However, methods that can generate spatially explicit predictions and maps of these ecosystem services based on empirical data are still scarce. 2. Here we propose a generic statistical model to derive kernel functions to characterize the spatial distribution of ecosystem services provided by mobile agents. The model is similar in spirit to a generalized linear model, and uses data of landscape composition and ecosystem services assessed at target sites to estimate parameters of the kernel. The approach is tested in a simulation study and illustrated by an empirical case study on parasitism rates of the diamondback moth Plutella xylostella. 3. The simulation study shows that the scale parameter of the exponential power kernel can be estimated with limited bias, whereas estimation of the shape parameter is difficult. For the case study the model provides biologically relevant estimates for the kernel associated with parasitism of Plutella xylostella. These estimates can be used to generate ecosystem service maps for existing or planned landscapes. The case study reveals that predictions can be sensitive to the parameter values for the width and shape of the kernel, and to the link function used in the statistical model. 4. In the last two decades numerous empirical studies assessed ecosystem services at target sites and related these to the surrounding landscape. Our method can take advantage of these data by estimating underlying kernels that can be used to map the spatial distribution of ecosystem services. However, empirical data that can discriminate between alternative kernel shapes remain critical.
Data from: A mechanistic and empirically-supported lightning risk model for forest trees
<ol> <li>Tree death due to lightning influences tropical forest carbon cycling and tree community dynamics. However, the distribution of lightning damage among trees in forests remains poorly understood. </li> <li>We developed models to predict direct and secondary lightning damage to trees based on tree size, crown exposure, and local forest structure. We parameterized these models using data on the locations of lightning strikes and censuses of tree damage in strike zones, combined with drone-based maps of tree crowns and censuses of all trees within a 50-ha forest dynamics plot on Barro Colorado Island, Panama. </li> <li>The likelihood of a direct strike to a tree increased with larger exposed crown area and higher relative canopy position (emergent > canopy >>> subcanopy), whereas the likelihood of secondary lightning damage increased with tree diameter and proximity to neighboring trees. The predicted frequency of lightning damage in this mature forest was greater for tree species with larger average diameters.</li> <li>These patterns suggest that lightning influences forest structure and the global carbon budget by nonrandomly damaging large trees. Moreover, these models provide a framework for investigating the ecological and evolutionary consequences of lightning disturbance in tropical forests.</li> </ol> <p><b>Synthesis:</b> Our findings indicate that the distribution of lightning damage is stochastic at large spatial grain and relatively deterministic at smaller spatial grain (<15 m). Lightning is more likely to directly strike taller trees with large crowns and secondarily damage large neighboring trees that are closest to the directly struck tree. The results provide a framework for understanding how lightning can affect forest structure, forest dynamics, and carbon cycling. The resulting lightning risk model will facilitate informed investigations into the effects of lightning in tropical forests.</p>
Data from: Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change.
Unique responses to climate change can occur across intraspecific levels, resulting in individualistic adaptation or movement patterns among populations within a given species. Thus, the need to model potential responses among genetically distinct populations within a species is increasingly recognized. However, predictive models of future distributions are regularly fit at the species level, often because intraspecific variation is unknown or is identified only within limited sample locations. In this study, we considered the role of intraspecific variation to shape the geographic distribution of ponderosa pine (Pinus ponderosa), an ecologically and economically important tree species in North America. Morphological and genetic variation across the distribution of ponderosa pine suggest the need to model intraspecific populations: the two varieties (var. ponderosa and var. scopulorum) and several haplotype groups within each variety have been shown to occupy unique climatic niches, suggesting populations have distinct evolutionary lineages adapted to different environmental conditions. We utilized a recently-available, geographically-widespread dataset of intraspecific variation (haplotypes) for ponderosa pine and a recently-devised lineage distance modeling approach to derive additional, likely intraspecific occurrence locations. We confirmed the relative uniqueness of each haplotype-climate relationship using a niche-overlap analysis, and developed ecological niche models (ENMs) to project the distribution for two varieties and eight haplotypes under future climate forecasts. Future projections of haplotype niche distributions generally revealed greater potential range loss than predicted for the varieties. This difference may reflect intraspecific responses of distinct evolutionary lineages. However, directional trends are generally consistent across intraspecific levels, and include a loss of distributional area and an upward shift in elevation. Our results demonstrate the utility in modeling intraspecific response to changing climate and they inform management and conservation strategies, by identifying haplotypes and geographic areas that may be most at risk, or most secure, under projected climate change.
Data from: Integration of anatomy ontologies and evo-devo using structured Markov models suggests a new framework for modeling discrete phenotypic traits
Modeling discrete phenotypic traits for either ancestral character state reconstruction or morphology-based phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. These drawbacks occur because trait evolution is driven by two key processes – hierarchical and hidden – which are not accommodated simultaneously by the available phylogenetic methods. The hierarchical process refers to the dependencies between anatomical body parts, while the hidden process refers to the evolution of gene regulatory networks underlying trait development. Herein, I demonstrate that these processes can be efficiently modeled using structured Markov models equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of structured Markov models with anatomy ontologies can adequately incorporate the hierarchical dependencies, while the use of the hidden states accommodates hidden evolution of gene regulatory networks and substitution rate heterogeneity. I assess the new models using simulations and theoretical synthesis. The new approach solves the long-standing "tail color problem," in which the trait is scored for species with tails of different colors or no tails. It also presents a previously unknown issue called the "two-scientist paradox," in which the nature of coding the trait and the hidden processes driving the trait's evolution are confounded; failing to account for the hidden process may result in a bias, which can be avoided by using hidden state models. All this provides a clear guideline for coding traits into characters. This paper gives practical examples of using the new framework for phylogenetic inference and comparative analysis.
Data from: Modeling intraspecific adaptation of Abies sachalinensis to local altitude and responses to global warming, based on a 36-year reciprocal transplant experiment
Intraspecific adaptation in Abies sachalinensis was examined using models based on long-term monitoring data gathered during a reciprocal transplant experiment with eight seed source populations and six transplantation sites along an altitudinal gradient. The consequence of local adaptation was evaluated by testing the home-site advantage for upslope and downslope transplants at five ages. The populations' fitness-linked trait was set as their productivity (tree height ×survival rate) at each age. The effects of global warming were evaluated on the basis of the 36-year performance of downslope transplants. Evidence was found for adaptive genetic variation affecting both height and survival from an early age. Increasing the distance between seed source and planting site significantly reduced productivity for both upslope and downslope transplantation, demonstrating the existence of a significant home-site advantage. The decrease in productivity was most distinct for upslope transplantations, indicating strong local adaptation to high altitudes. Global warming is predicted to increase the productivity of high-altitude populations. However, due to their existing local adaptation, all tested populations exhibited lower productivity under warming than demes that were optimal for the new climate. These negative predictions should be considered when planning the management of locally-adapted plant species such as A. sachalinensis.
Data from: A phenotype-genotype codon model for detecting adaptive evolution
A central objective in biology is to link adaptive evolution in a gene to structural and/or functional phenotypic novelties. Yet most analytic methods make inferences mainly from either phenotypic data or genetic data alone. A small number of models have been developed to infer correlations between the rate of molecular evolution and changes in a discrete or continuous life history trait. But such correlations are not necessarily evidence of adaptation. Here we present a novel approach called the phenotype-genotype branch-site model (PG-BSM) designed to detect evidence of adaptive codon evolution associated with discrete-state phenotype evolution. An episode of adaptation is inferred under standard codon substitution models when there is evidence of positive selection in the form of an elevation in the nonsynonymous-to-synonymous rate ratio ω to a value ω > 1. As it is becoming increasingly clear that ω > 1 can occur without adaptation, the PG-BSM was formulated to infer an instance of adaptive evolution without appealing to evidence of positive selection. The null model makes use of a covarion-like component to account for general heterotachy (i.e., random changes in the evolutionary rate at a site over time). The alternative model employs samples of the phenotypic evolutionary history to test for phenomenological patterns of heterotachy consistent with specific mechanisms of molecular adaptation. These include (i) a persistent increase/decrease in ω at a site following a change in phenotype (the pattern) consistent with an increase/decrease in the functional importance of the site (the mechanism); and (ii) a transient increase in ω at a site along a branch over which the phenotype changed (the pattern) consistent with a change in the site's optimal amino acid (the mechanism). Rejection of the null is followed by post hoc analyses to identify sites with strongest evidence for adaptation in association with changes in the phenotype as well as the most likely evolutionary history of the phenotype. Simulation studies based on a novel method for generating mechanistically realistic signatures of molecular adaptation show that the PG-BSM has good statistical properties. Analyses of three real alignments show that site patterns identified post hoc are consistent with the specific mechanisms of adaptation included in the alternate model. Further simulation studies show that the covarion-like component of the PG-BSM plays a crucial role in mitigating recently discovered statistical pathologies associated with confounding by accounting for heterotachy-by-any-means.
Data from: Towards a common methodology for developing logistic tree mortality models based on ring-width data
Tree mortality is a key process shaping forest dynamics. Thus, there is a growing need for indicators of the likelihood of tree death. During the last decades, an increasing number of tree-ring based studies have aimed to derive growth–mortality functions, mostly using logistic models. The results of these studies, however, are difficult to compare and synthesize due to the diversity of approaches used for the sampling strategy (number and characteristics of alive and death observations), the type of explanatory growth variables included (level, trend, etc.), and the length of the time window (number of years preceding the alive/death observation) that maximized the discrimination ability of each growth variable. We assess the implications of key methodological decisions when developing tree-ring based growth–mortality relationships using logistic mixed-effects regression models. As examples, we use published tree-ring datasets from Abies alba (13 different sites), Nothofagus dombeyi (one site), and Quercus petraea (one site). Our approach is based on a constant sampling size and aims at (1) assessing the dependency of growth–mortality relationships on the statistical sampling scheme used, (2) determining the type of explanatory growth variables that should be considered, and (3) identifying the best length of the time window used to calculate them. The performance of tree-ring-based mortality models was reasonably high for all three species (area under the receiving operator characteristics curve, AUC > 0.7). Growth level variables were the most important predictors of mortality probability for two species (A. alba, N. dombeyi), while growth-trend variables need to be considered for Q. petraea. In addition, the length of the time window used to calculate each growth variable was highly uncertain and depended on the sampling scheme, as some growth–mortality relationships varied with tree age. The present study accounts for the main sampling-related biases to determine reliable species-specific growth–mortality relationships. Our results highlight the importance of using a sampling strategy that is consistent with the research question. Moving towards a common methodology for developing reliable growth–mortality relationships is an important step towards improving our understanding of tree mortality across species and its representation in dynamic vegetation models.
Data from: Paleoecological and stratigraphic controls on eurypterid Lagerstätten: a model for preservation in the mid-Paleozoic
Recent studies of eurypterid paleoecology suggest that formation of eurypterid Lagerstätten in the mid-Paleozoic of Laurentia was controlled by the presence of an ecological–taphonomic window that recurred predictably in nearshore, marginal environments during transgressions. We tested this hypothesis by performing a high-resolution taxonomic, environmental, and stratigraphic survey and quantitative analysis of all Silurian–Lower Devonian eurypterid-bearing intervals in the Appalachian basin, the most prolific region for eurypterid remains in the world. Canonical correspondence analysis of sedimentological and faunal associations revealed a strong lithologic gradient between groupings of eurypterid genera and associated taxa across the basin, and a significant association of eurypterids with microbialites (thrombolites, stromatolites) and evaporitic structures. Field observations confirmed that, stratigraphically, eurypterids in the basin frequently occur above the microbialite structures and beneath evaporites and other indicators of increased salinity or subaerial exposure. Following interpretation of these features within a sequence stratigraphic framework, we present a preservational model in which (1) eurypterids inhabited nearshore settings following freshening conditions concomitant with minor transgressions, (2) their remains were subsequently buried by storms or microbialite sediment baffling, and (3) subsequent long-term preservation of tissues was facilitated by regression and cyclical shallowing-up successions that promoted hypersalinity and anoxia. In the central and southern region of the basin, where microbial structures and evidence for hypersalinity are less common, a similar pattern of cyclical shallowing-upward deposition within eurypterid-bearing units holds. Thus, eurypterid preservation appears to reflect a combination of ecological preferences and abiotic conditions that promoted inhabitation and eventual preservation within the same setting. This study provides the first quantitative support for a sea level–based control on preservation of eurypterids and adds to the growing body of evidence that suggests that analysis of exceptional preservation in the fossil record benefits from interpretation within a sequence stratigraphic framework.
Data from: Cell death and survival due to cytotoxic exposure modeled as a two-state Ising system
<p>Cancer chemotherapy agents are assessed for their therapeutic utility primarily by their ability to cause apoptosis of cancer cells and their potency is given by an IC50 value. Chemotherapy uses both target-specific and systemic-action drugs and drug combinations to treat cancer. It is important to judiciously choose a drug type, its dosage, and schedule for optimized drug selection and administration. Consequently, the precise mathematical formulation of cancer cells response to chemotherapy may assist in the selection process. In this paper, we propose a mathematical description of the cancer cell response to chemotherapeutic agent exposure based on a time-tested physical model of two-state multiple-component systems near criticality. We describe the Ising model methodology and apply it to a diverse panel of cytotoxic drugs administered against numerous cancer cell lines in a dose-response manner. The analyzed dataset was generated by the Netherlands Translational Research Center B.V.(Oncolines). This approach allows for an accurate and consistent analysis of cytotoxic agents' effects on cancer cell lines and reveals the presence or absence of the bystander effect through the interaction constant. By calculating the susceptibility function, we see the value of IC50 coinciding with the peak of this measure of the system's sensitivity to external perturbations.</p>
Data from: Input matters matter: bioclimatic consistency to map more reliable species distribution models
1. Accuracy of global bioclimatic databases is essential to understand biodiversity-environment relationships. Many studies have explored biases and uncertainties related to species distribution models (SDMs) but the effect of choosing a specific database among the different alternatives has not been previously assessed. 2. The lack of bioclimatic congruence (degree of agreement) between different databases is a main concern in distribution modelling and it is critical in single-source models, for which the database choice is decisive. In order to prevent unreliable predictions derived from distorted input data, SDMs accuracy can be assessed by mapping model predictions according to a bioclimatic congruence measure derived from the comparison of multiple databases, which can be achieved with the bioclimatic consistency maps that we propose in this study. Here, i) we present the first global-scale bioclimatic congruence map to analyse environmental mismatches between recently updated bioclimatic databases. We also test the importance of input matters on the reliability of distribution models of sixteen mammals, by addressing ii) inconsistencies among species response curves (temperature and precipitation), and iii) discrepancies among SDMs predictions depending on the chosen bioclimatic database. Finally, iv) we propose a strategy to assess bioclimatic consistency of model predictions, showing its application to the specific case of Litocranius walleri. 3. Our results confirm that the single-source modelling approach greatly influences the estimation of species-environment relationship and consequently, bias spatial predictions derived from SDMs. This is especially true for studies conducted in polar and mountainous regions which showed the smallest bioclimatic congruence. We show that by adding bioclimatic congruence to SDMs projections, we can build a bioclimatic consistency map that enables the detection of both risky and consistent areas, as revealed for the case of L. walleri. 4. Assessing uncertainty in bioclimatic input data is key to avoid erroneous conclusions in macroecological and biogeographical studies. The spatial characterisation of bioclimatic consistency provides an adequate empirical framework which effectively illustrates bioclimatic data limitations. We strongly recommend that this new strategy should be formally and systematically incorporated into distribution modelling to build more reliable SDMs, which are essential to develop successful biodiversity conservation programmes.
Data from: Complementary responses of morphology and physiology enhance the stand-scale production of a model invasive species under elevated CO2 and nitrogen
1. Elevated atmospheric carbon dioxide (eCO2) concentrations and nitrogen (N) enrichment are known to enhance plant productivity and invasion. However, the implications of their interactive effects for plant productivity are not well understood, especially at the stand scale, presumably because morphological and physiological responses to these global change factors are rarely studied together in the field or assessed at the stand-level. 2. We first determined how leaf-level morphological and physiological traits responded to factorial combinations of ambient and elevated CO2 and N. We collected trait data from the model invasive species Phragmites australis (common reed) that were measured over three years in a long-term global change field experiment. We then combined the trait data and additional descriptions of P. australis canopies in a simulation model of carbon assimilation to determine how morphology and physiology contribute to P. australis' stand scale productivity. 3. At the leaf level, we found that light-saturated rates of photosynthesis were strongly stimulated by eCO2 (37%) and that this effect was enhanced by increasing salinity. N had a smaller effect (17% stimulation) on physiological responses than eCO2, but leaf morphological traits responded primarily to N; plant height increased by 27% and leaf area increased by 47%. 4. Stand scale simulations demonstrated that that morphological and physiological adjustments induced approximately additive responses when P. australis experienced both eCO2 and N enrichment. The simulations also indicated that morphological changes (which were primarily associated with canopy size) influenced stand scale carbon assimilation more than physiological changes. Moreover, 97% of the N response was due to changes in morphology, whereas 62% of the eCO2 response was caused by physiological shifts. 5. Our analysis indicates that morphological and physiological trait responses to elevated CO2 and nitrogen are likely to enhance the productivity of P. australis in complementary ways, potentially accelerating its invasion in North America. Furthermore, our data suggest that changes in morphological traits may have a greater influence on carbon gain than leaf-level physiology under near-future environmental conditions. Our study also highlights the importance of accounting for both morphological and physiological responses when attempting to infer global change responses from leaf-level data.
Data from: Objective choice of phylogeographic models
Phylogeography seeks to discover the evolutionary processes that have given rise to organismal and genetic diversity. This requires explicit hypotheses (i.e., models) to be evaluated with genetic data in order to identify those hypotheses that best explain the data. In recent years, advancements in the model-based tools used to estimate phylogeographic parameters of interest such as gene flow, divergence time, and relationships among groups have been made. However, given the complexity of these models, available methods can typically only compare a handful of possible hypotheses, requiring researchers to specify in advance the small set of models to consider. Without formal quantitative approaches to model selection, researchers must rely on their intuition to formulate the model space to be explored.We explore the adequacy of intuitive choices made by researchers during the process of data analysis by reanalyzing 20 empirical phylogeographic datasets using PHRAPL, an objective tool for phylogeographic model selection. We show that the best models for most datasets include both gene flow and population divergence parameters, and that species tree methods (which do not consider gene flow) tend to be overly simplistic for many phylogeographic systems. Objective approaches to phylogeographic model selection offer an important complement to researcher intuition.
Data from: Assessing cumulative impacts of forest development on the distribution of furbearers using expert-based habitat modeling
Cumulative impacts of anthropogenic landscape change must be considered when managing and conserving wildlife habitat. Across the central-interior of British Columbia, Canada, industrial activities are altering the habitat of furbearer species. This region has witnessed unprecedented levels of anthropogenic landscape change following rapid development in a number of resource sectors, particularly forestry. Our objective was to create expert-based habitat models for three furbearer species: fisher (Pekania pennanti), Canada lynx (Lynx canadensis), and American marten (Martes americana) and quantify habitat change for those species. We recruited 10 biologist and 10 trapper experts and then used the analytical hierarchy process to elicit expert knowledge of habitat variables important to each species. We applied the models to reference landscapes (i.e., registered traplines) in two distinct study areas and then quantified the change in habitat availability from 1990 to 2013. There was strong agreement between expert groups in the choice of habitat variables and associated scores. Where anthropogenic impacts had increased considerably over the study period, the habitat models showed substantial declines in habitat availability for each focal species (78% decline in optimal fisher habitat, 83% decline in optimal lynx habitat, and 79% decline in optimal marten habitat). For those traplines with relatively little forest harvesting, the habitat models showed no substantial change in the availability of habitat over time. The results suggest that habitat for these three furbearer species declined significantly as a result of the cumulative impacts of forest harvesting. Results of this study illustrate the utility of expert knowledge for understanding large-scale patterns of habitat change over long time periods.
Data from: A new analytical approach to landscape genetic modeling: least-cost transect analysis and linear mixed models
Landscape genetics aims to assess the effect of the landscape on intraspecific genetic structure. To quantify interdeme landscape structure, landscape genetics mostly uses landscape resistance surfaces and least-cost paths or straight-line transects. However, both approaches have drawbacks. Parameterization of resistance surfaces is a subjective process, and least-cost paths represent a single migration route. A transect-based approach might oversimplify migration patterns by assuming rectilinear migration. To overcome these limitations, we combined these two methods in a new landscape genetic approach: least-cost transect analysis (LCTA). Habitat-matrix resistance surfaces were used to create least-cost paths, which were subsequently buffered to form transects in which the abundance of several landscape elements was quantified. To maintain objectivity, this analysis was repeated so that each landscape element was in turn regarded as migration habitat. The relationship between landscape predictor variables and genetic distances was then assessed following a mixed modeling approach to account for the non-independence of values in distance matrices. Subsequently, predictor variables were selected making use of the R_β^2 statistic. We applied LCTA and the mixed model approach to an empirical genetic dataset on the endangered damselfly, Coenagrion mercuriale. We compared the results to those obtained from traditional least-cost, effective and resistance distance analysis and showed that LCTA not only outperforms existing methods in a statistical way, but also provides more information about the migration ecology of the focal species. Although we believe the statistical approach to be an improvement for the analysis of distance matrices in landscape genetics, more stringent testing is needed.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.