Find research datasets worth reusing

opencc-zeroJun 2019View details →

Data from: The demographic effects of functional traits: an integral projection model approach reveals population-level consequences of reproduction-defense tradeoffs

Quantitatively linking individual variation in functional traits to demography is a necessary step to advance our understanding of trait-based ecological processes. We constructed a population model for Asclepias syriaca to identify how functional traits affect vital rates and population growth and whether tradeoffs in chemical defense and demography alter population growth. Plants with higher foliar cardenolides had lower fiber, cellulose, and lignin levels, as well as decreased sexual and clonal reproduction. Average cardenolide concentrations had the strongest effect on population growth. In both the sexual and clonal pathway, the tradeoff between reproduction and defense affected population growth. We found that both increasing the mean of the distribution of individual plant values for cardenolides and herbivory decreased population growth. However, increasing the variance in both defense and herbivory increased population growth. Functional traits can impact population growth and quantifying individual-level variation in traits should be included in assessments of population-level processes.

Data from: Disentangling elevational richness: a multi-scale hierarchical Bayesian occupancy model of Colorado ant communities

Understanding the forces that shape the distribution of biodiversity across spatial scales is central in ecology and critical to effective conservation. To assess effects of possible richness drivers, we sampled ant communities on four elevational transects across two mountain ranges in Colorado, USA, with seven or eight sites on each transect and twenty repeatedly sampled pitfall trap pairs at each site each for a total of 90 days. With a multi-scale hierarchical Bayesian community occupancy model, we simultaneously evaluated the effects of temperature, productivity, area, habitat diversity, vegetation structure, and temperature variability on ant richness at two spatial scales, quantifying detection error and genus-level phylogenetic effects. We fit the model with data from one mountain range and tested predictive ability with data from the other mountain range. In total, we detected 105 ant species, and richness peaked at intermediate elevations on each transect. Species-specific thermal preferences drove richness at each elevation with marginal effects of site-scale productivity. Trap-scale richness was primarily influenced by elevation-scale variables along with a negative impact of canopy cover. Soil diversity had a marginal negative effect while daily temperature variation had a marginal positive effect. We detected no impact of area, land cover diversity, trap-scale productivity, or tree density. While phylogenetic relationships among genera had little influence, congeners tended to respond similarly. The hierarchical model, trained on data from the first mountain range, predicted the trends on the second mountain range better than multiple regression, reducing root mean squared error up to 65%. Compared to a more standard approach, this modeling framework better predicts patterns on a novel mountain range and provides a nuanced, detailed evaluation of ant communities at two spatial scales.

opencc-zeroFeb 2020View details →

Data from: Assessing cetacean populations using integrated population models: an example with Cook Inlet beluga whales

Effective conservation and management of animal populations requires knowledge of abundance and trends. For many species, these quantities are estimated using systematic visual surveys. Additional individual-level data are available for some species. Integrated population modelling (IPM) offers a mechanism for leveraging these datasets into a single estimation framework. IPMs that incorporate both population- and individual-level data have previously been developed for birds, but have rarely been applied to cetaceans. Here, we explore how IPMs can be used to improve the assessment of cetacean populations. We combined three types of data that are typically available for cetaceans of conservation concern: population-level visual survey data, individual-level capture-recapture data, and data on anthropogenic mortality. We used this IPM to estimate the population dynamics of the Cook Inlet population of beluga whales (CIBW; Delphinapterus leucas) as a case study. Our state-space IPM included a population process model and three observational submodels: 1) a group detection model to describe group size estimates from aerial survey data; 2) a capture-recapture model to describe individual photographic capture-recapture data; and 3) a Poisson regression model to describe historical hunting data. The IPM produces biologically plausible estimates of population trajectories consistent with all three datasets. The estimated population growth rate since 2000 is less than expected for a recovering population. The estimated juvenile/adult survival rate is also low compared to other cetacean populations, indicating that low survival may be impeding recovery. This work demonstrates the value of integrating various data sources to assess cetacean populations and serves as an example of how multiple, imperfect datasets can be combined to improve our understanding of a population of interest. The model framework is applicable to other cetacean populations and to other taxa for which similar data types are available.

Data from: Human commercial models' eye colour shows negative frequency-dependent selection

In this study we investigated the eye colour of human commercial models registered in the UK (400 female and 400 male) and Brazil (400 female and 400 male) to test the hypothesis that model eye colour frequency was the result of negative frequency-dependent selection. The eye colours of the models were classified as: blue, brown or intermediate. Chi-square analyses of data for countries separated by sex showed that in the United Kingdom brown eyes and intermediate colours were significantly more frequent than expected in comparison to the general United Kingdom population (P<0.001). In Brazil, the most frequent eye colour brown was significantly less frequent than expected in comparison to the general Brazilian population. These results support the hypothesis that model eye colour is the result of negative frequency-dependent selection. This could be the result of people using eye colour as a marker of genetic diversity and finding rarer eye colours more attractive because of the potential advantage more genetically diverse offspring that could result from such a choice. Eye colour may be important because in comparison to many other physical traits (e.g., hair colour) it is hard to modify, hide or disguise, and it is highly polymorphic.

Data from: Climate-mediated hybrid zone movement revealed with genomics, museum collection and simulation modeling

Climate-mediated changes in hybridization will dramatically alter the genetic diversity, adaptive capacity and evolutionary trajectory of interbreeding species. Our ability to predict the consequences of such changes will be key to future conservation and management decisions. Here we tested through simulations how recent warming (over a 32-year period) is affecting the geographic extent of a climate-mediated developmental threshold implicated in maintaining a butterfly hybrid zone (Papilio glaucus and Papilio canadensis; Lepidoptera: Papilionidae). These simulations predict a 68 km shift of this hybrid zone. To empirically test this prediction, we assessed genetic and phenotypic changes using contemporary and museum collections and document a 40 km northward shift of this hybrid zone. Interactions between the two species appear relatively unchanged during hybrid zone movement. We found no change in the frequency of hybridization and regions of the genome that experience little to no introgression moved largely in concert with the shifting hybrid zone. Model predictions based on climate scenarios predict this hybrid zone will continue to move northward, but with substantial spatial heterogeneity in the velocity (55-144 km/1°C), shape, and contiguity of movement. Our findings suggest that the presence of non-climatic barriers (e.g., genetic incompatibilities) and/or non-linear responses to climatic gradients may preserve species boundaries as the species shift. Further, we show that variation in the "geography" of hybrid zone movement could result in evolutionary responses that differ for geographically distinct populations spanning hybrid zones and thus have implications for the conservation and management of genetic diversity.

Data from: Distinguishing distribution dynamics from temporary emigration using dynamic occupancy models

1. Dynamic occupancy models are popular for estimating dynamic distribution rates (colonization and extinction) from repeated presence/absence surveys of unmarked animals. This approach assumes closure among repeated samples within primary periods, allowing estimation of dynamic rates between these periods. However, the impact of temporary emigration (reversible changes in sampling availability) on dynamic rate estimates, has not been tested. 2. Using simulated data, we investigated the degree to which temporary emigration could mislead researchers interested in quantifying dynamics. We then compared results from three avian point count datasets to evaluate the likelihood that temporary emigration confounds estimates of dynamics for 19 species under a popular sampling protocol. 3. Simulated experiments indicated that when secondary periods were open to temporary emigration, presence of dynamics was correctly identified ≥ 95.1% of the time, and dynamic rate estimates were accurate. However, dynamic rate estimates were biased when secondary periods were closed to temporary emigration. In empirical datasets, dynamic occupancy models had greater support than closed models for all species when secondary sampling periods occurred in immediate succession (i.e., 3 samples within 10 minutes); however, our results suggest that this is because these estimates were heavily influenced by temporary emigration. When counts within a primary period were separated by 24-48 hours, we found evidence of dynamics for less than half of these species. We recommend an alternative sampling approach that allows accurate estimation of dynamic rates when temporary emigration is of no interest, and introduce a novel model for estimating both processes simultaneously in rare cases where they are both of biological interest. 4. Concern for violating the occupancy modeling closure assumption has led to widespread recommendations that samples within primary periods be conducted extremely close in time. However, this may not be the best approach when interest is in quantifying dynamic rates. While dynamic occupancy models provide estimates of 'colonization' and 'extinction,' these values do not inherently represent dynamics unless temporary emigration has been explicitly modeled, or accounted for with sampling design. Naiveté to this fact can result in incorrect conclusions about biological processes.

Data from: Evaluating mechanisms of diversification in a Guineo-Congolian tropical forest frog using demographic model selection

The accumulation of biodiversity in tropical forests can occur through multiple allopatric and parapatric models of diversification, including forest refugia, riverine barriers and ecological gradients. Considerable debate surrounds the major diversification process, particularly in the West African Lower Guinea forests, which contain a complex geographic arrangement of topographic features and historical refugia. We used genomic data to investigate alternative mechanisms of diversification in the Gaboon forest frog, Scotobleps gabonicus, by first identifying population structure and then performing demographic model selection and spatially explicit analyses. We found that a majority of population divergences are best explained by allopatric models consistent with the forest refugia hypothesis and involve divergence in isolation with subsequent expansion and gene flow. These population divergences occurred simultaneously and conform to predictions based on climatically stable regions inferred through ecological niche modelling. Although forest refugia played a prominent role in the intraspecific diversification of S. gabonicus, we also find evidence for potential interactions between landscape features and historical refugia, including major rivers and elevational barriers such as the Cameroonian Volcanic Line. We outline the advantages of using genomewide variation in a model-testing framework to distinguish between alternative allopatric hypotheses, and the pitfalls of limited geographic and molecular sampling. Although phylogeographic patterns are often species-specific and related to life-history traits, additional comparative studies incorporating genomic data are necessary for separating shared historical processes from idiosyncratic responses to environmental, climatic and geological influences on diversification.

opencc-zeroJun 2019View details →

Data from: Impacts of rainfall extremes predicted by climate-change models on major trophic groups in the leaf-litter arthropod community

1. Arthropods in the leaf-litter layer of forest soils influence ecosystem processes such as decomposition. Climate-change models predict both increases and decreases in average rainfall. Increased drought may have greater impacts on the litter arthropod community. In addition to affecting survival or behavior of desiccation-sensitive species, lower rainfall may indirectly lower abundances of consumers that graze drought-stressed fungi, with repercussions for higher trophic levels. 2. We tested the hypothesis that trophic structure will differ between the two rainfall scenarios. In particular, we hypothesized that densities of several broadly defined trophic groupings of arthropods would be lower under reduced rainfall. 3. To test this hypothesis we used sprinklers to impose two rainfall treatments during three growing seasons in roofed, fenced 14-m2 plots; and documented changes in abundance from initial, pre-treatment densities of 39 arthropod taxa. Experimental plots were subjected to either LOW (fortnightly) or HIGH (weekly) average rainfall based upon climate models and the previous 100 years of regional weekly averages. Unroofed open plots, our reference treatment (REF), experienced higher-than-average rainfall during the experiment. 4. The two rainfall extremes produced clear negative effects of lowered rainfall on major trophic groups. Broad categories of fungivores, detritivores and predators were more abundant in HIGH than LOW plots by the final year. Springtails (Collembola), which graze fungal hyphae, were 3x more abundant in the HIGH-rainfall treatment. Taxa of larger-bodied fungivores and detritivores, spiders (Araneae), and non-spider predators were 2x more abundant under HIGH rainfall. Densities of mites (Acari), which include fungivores, detritivores and predators, were 1.5x greater in HIGH rainfall plots. Abundances and community structure of arthropods were similar in REF and experimental plots, showing that effects of rainfall uncovered in the experiment are applicable to nature. 5. This pattern suggests that changes in rainfall will alter bottom-up control processes in a critical detritus-based food web of deciduous forests. Our results, in conjunction with other findings on the impact of desiccation on arthropods and fungal growth, suggest that drier conditions will depress densities of fungal consumers, causing declines in higher trophic levels, with possible impacts on soil processes and the larger forest food web.

opencc-zeroDec 2018View details →

Data from: A new null model approach to quantify performance and significance for ecological niche models of species distributions

Aim: Ecological niche modelling requires robust estimation of model performance and significance, but common evaluation approaches often yield biased estimates. Null models provide a solution but are rarely used in this field. We implemented an important modification to existing null-model tests, evaluating null models with the same withheld records that were used to evaluate the real model. We built and evaluated models across a range of modelling scenarios and for various performance measures using the algorithm Maxent and the monk parakeet (Myiopsitta monachus). Location: Native range in Southern America and global invasions predominantly in North/Central America and Europe Methods: We tested the ability of models built under 15 scenarios (five sets of calibration records and three settings that varied the level of model complexity) to predict spatially independent evaluation data in the invaded range (in effect, testing the models under spatial transfer). We quantified performance with measures of discriminatory ability and overfitting based on AUC and the omission error rate. We estimated null distributions of these measures and calculated effect size and significance. We determined how these estimates varied across modelling scenarios, comparing with two tests existing in the literature. Results: Performance varied starkly across modelling scenarios. As expected, the measures of overfitting agreed with each other and provided different information than that of discriminatory ability. However, high performance per se did not show strong association with high effect size and significance. Main Conclusions: Ecological niche models should be assessed with measures of effect size and significance based on appropriate null distributions, in contrast to several approaches existing in the literature. The proposed approach using independent evaluation data, implemented with our accompanying code, allows such estimates for either the same or a different region/time period, and it merits use and continued development.

opencc-zeroJun 2021View details →

Data from: Effectiveness of joint species distribution models in the presence of imperfect detection

Joint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of the first papers to account for imperfect detection when fitting data with JSDMs and to explore the complications that may arise. A multivariate probit JSDM that explicitly accounts for imperfect detection is proposed, and implemented using a Bayesian hierarchical approach. We investigate the performance of the JSDM in the presence of imperfect detection for a range of factors, including varied levels of detection and species occupancy, and varied numbers of survey sites and replications. To understand how effective this JSDM is in practice, we also compare results to those from a JSDM that does not explicitly model detection but instead makes use of "collapsed data". A case study of owls and gliders in Victoria Australia is also illustrated. Using simulations, we found that the JSDMs explicitly accounting for detection can accurately estimate intrinsic correlation between species with enough survey sites and replications. Reducing the number of survey sites decreases the precision of estimates, while reducing the number of survey replications can lead to biased estimates. For low probabilities of detection, the model may require a large number of survey replications to remove bias from estimates. However, JSDMs not explicitly accounting for detection may have a limited ability to disentangle detection from occupancy, which substantially reduces their ability to accurately infer the species distribution spatially. Our case study showed positive correlation between Sooty Owls and Greater Gliders, despite a low number of survey replications. To avoid biased estimates of inter-species correlations and species distributions, imperfect detection needs to be considered. However, for low probability of detection, the JSDMs explicitly accounting for detection is data hungry. Estimates from such models may still be subject to bias. To overcome the bias, researchers need to carefully design surveys and choose appropriate modelling approaches. The survey design should ensure sufficient survey replications for unbiased inferences on species inter-dependencies and occupancy.

Data from: Effects of grain size and niche breadth on species distribution modeling

Scale is a vital component to consider in ecological research, and spatial resolution or grain size is one of its key facets. Species distribution models (SDMs) are prime examples of ecological research in which grain size is an important component. Despite this, SDMs rarely explicitly examine the effects of varying the grain size of the predictors for species with different niche breadths. To investigate the effect of grain size and niche breadth on SDMs, we simulated four virtual species with different grain sizes/niche breadths using three environmental predictors (elevation, aspect, and percent forest) across two real landscapes of differing heterogeneity in predictor values. We aggregated these predictors to seven different grain sizes and modeled the distribution of each of our simulated species using MaxEnt and GLM techniques at each grain size. We examined model accuracy using the AUC statistic, Pearson's correlations of predicted suitability with the true suitability, and the binary area of presence determined from suitability above the maximum True Skill Statistic (TSS) threshold. Habitat specialists were more accurately modeled than generalist species, and the models constructed at the grain size from which a species was derived generally performed the best. The accuracy of models in the homogenous landscape deteriorated with increasing grain size to a greater degree than models in the heterogenous landscape. Variable effects on the model varied with grain size, with elevation increasing in importance as grain size increased while aspect lost importance. The area of predicted presence was drastically affected by grain size, with larger grain sizes over predicting this value by up to a factor of 14. Our results have implications for species distribution modeling and conservation planning, and we suggest more studies include analysis of grain size as part of their protocol.

Data from: Demographic model selection using random forests and the site frequency spectrum

Phylogeographic data sets have grown from tens to thousands of loci in recent years, but extant statistical methods do not take full advantage of these large data sets. For example, approximate Bayesian computation (ABC) is a commonly used method for the explicit comparison of alternate demographic histories, but it is limited by the "curse of dimensionality" and issues related to the simulation and summarization of data when applied to next-generation sequencing (NGS) data sets. We implement here several improvements to overcome these difficulties. We use a Random Forest (RF) classifier for model selection to circumvent the curse of dimensionality and apply a binned representation of the multidimensional site frequency spectrum (mSFS) to address issues related to the simulation and summarization of large SNP data sets. We evaluate the performance of these improvements using simulation and find low overall error rates (~7%). We then apply the approach to data from Haplotrema vancouverense, a land snail endemic to the Pacific Northwest of North America. Fifteen demographic models were compared, and our results support a model of recent dispersal from coastal to inland rainforests. Our results demonstrate that binning is an effective strategy for the construction of a mSFS and imply that the statistical power of RF when applied to demographic model selection is at least comparable to traditional ABC algorithms. Importantly, by combining these strategies, large sets of models with differing numbers of populations can be evaluated.

opencc-zeroMar 2020View details →

Data from: Modelling the potential efficacy of treatments for white-nose syndrome in bats

1. The fungal disease white-nose syndrome (WNS) has caused mass mortality in some species of North American bats during hibernation. 2. We use population viability models to test if a hypothetical WNS treatment or management action could facilitate the recovery of WNS-affected little brown myotis (Myotis lucifugus) populations. We modelled scenarios altering three parameters: (1) WNS severity (population growth rate of WNS-affected populations; λWNS); (2) proportion of population treated; and (3) treatment improvement in winter survival (TIWS). 3. Our models predict that a treatment or management action that targets an entire population with a TIWS of 40% (the average TIWS in bat trials to date) will cause a population to stabilize or increase if WNS causes an annual decline of less than 70% (i.e. λWNS>=0.30). However, for severe WNS (λWNS=0.10), the TIWS must be at least 54% to cause the population to stabilize or increase. Where only a proportion of a WNS-affected population is treated, population stability is much harder to achieve unless the impact of WNS attenuates over time. 4. Our models suggest that a treatment or management action only facilitates the recovery of WNS-affected populations if WNS is mild, a large proportion of bats can be treated, TIWS is high, and/or WNS severity attenuates over time. 5. Synthesis and applications. We modelled the predicted abundance trajectory of white-nose syndrome (WNS)-affected little brown myotis (Myotis lucifugus) populations in response to hypothetical treatment or management actions. Our two types of models incorporate the complete range of possible scenarios varying three parameters: (1) population growth rate of the WNS-affected population, (2) the improvement in winter survival associated with the treatment or management action, and (3) the proportion of the population treated. We suggest that our models, which can be explored using online Shiny applications, should be used in the planning phase of treatment or management action programs for WNS.

opencc-zeroJun 2021View details →

Accounting for imperfect detection in data from museums and herbaria when modeling species distributions: Combining and contrasting data-level versus model-level bias correction

The digitization of museum collections as well as an explosion in citizen science initiatives has resulted in a wealth of data that can be useful for understanding the global distribution of biodiversity, provided that the well-documented biases inherent in unstructured opportunistic data are accounted for. While traditionally used to model imperfect detection using structured data from systematic surveys of wildlife, occupancy models provide a framework for modelling the imperfect collection process that results in digital specimen data. In this study, we explore methods for adapting occupancy models for use with biased opportunistic occurrence data from museum specimens and citizen science platforms using 7 species of Anacardiaceae in Florida as a case study. We explored two methods of incorporating information about collection effort to inform our uncertainty around species presence: (1) filtering the data to exclude collectors unlikely to collect the focal species and (2) incorporating collection covariates (collection type, time of collection, and history of previous detections) into a model of collection probability. We found that the best models incorporated both the background data filtration step as well as collector covariates. Month, method of collection and whether a collector had previously collected the focal species were important predictors of collection probability. Efforts to standardize meta-data associated with data collection will improve efforts for modeling the spatial distribution of a variety of species.

Data from: Functional traits and community composition: a comparison among community-weighted means, weighted correlations, and multilevel models

1. Of the several approaches that are used to analyze functional trait-environment relationships, the most popular is community-weighted mean regressions (CWMr) in which species trait values are averaged at the site level and then regressed against environmental variables. Other approaches include model-based methods and weighted correlations of different metrics of trait-environment associations, the best known of which is the fourth-corner correlation method. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using four different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios, implying that the significant results for the data could be spurious. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but had lower power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should always be avoided. 2. We investigated these three general statistical approaches for trait-environment associations: CWMr, five weighted correlation metrics (Peres-Neto et al. 2017), and two multilevel models (MLM) using five different methods for computing p-values. We first compared the methods applied to a plant community dataset. To determine the validity of the statistical conclusions, we then performed a simulation study. 3. CWMr gave highly significant associations for both traits, while the other methods gave a mix of support. CWMr had inflated type I errors for some simulation scenarios. The weighted correlation methods had generally good type I error control but had low power. One of the multilevel models, that from Jamil et al. (2013), had both good type I error control and high power when an appropriate method was used to obtain p-values. In particular, if there was no correlation among species in their abundances among sites, a parametric bootstrap likelihood ratio test (LRT) gave the best power. When there was correlation among species in their abundances, a conditional parametric LRT had correct type I errors but suffered from low power. 4. There is no overall best method for identifying trait-environment associations. For the simple task of testing, one-by-one, associations between single environmental variables and single traits, the weighted correlations with permutation tests all had good type I error control, and their ease of implementation is an advantage. For the more complex task of multivariate analyses and model fitting, and when high statistical power is needed, we recommend MLM2 (Jamil et al. 2013); however, care must be taken to ensure against inflated type I errors. Because CWMr exhibited highly inflated type I error rates, it should be avoided.

Data from: Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin‐up

Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state, or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate; this approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one, or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections.

opencc-by-4.0Dec 2014View details →

Questionnaires and Raw Data for the Paper "Operationalised Product Quality Models and Assessment: The Quamoco Approach"

This paper contains the quantitative data from several interviews along with a used questionnaire for empirically investigating the software quality models created in the project Quamoco.

opencc-zeroJan 2015View details →

31 ChEMBL data sets for regression modeling

From ChEMBL version 17, 31 compound data sets have been selected for regression modeling. Compounds had to be active against human targets in a direct inhibition/binding assay with highest ChEMBL confidence score and Ki values below 100 micromolar. Multiple Ki values for the same compound were averaged if they fell into the same order of magnitude, or else they were disregarded. Duplicates, known pan-assay interference, and other reactive molecules were removed. Only sets with at least 500 compounds were considered.   Note: The SD files contain a field "pKi"; note however that this field contains the Ki value in nM units, not the logarithmic value.

opencc-zeroNov 2014View details →

Stellar Model Isochrone data

Stellar model isochrone data that is downloaded and used by the 'isochrones' python module.  Data come from the Dartmouth, Padova, and BASTI stellar model grids.