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Figure 4 in Distribution and composition of Dragonfly and Damselfly species (Odonata) of the upper Rio das Velhas, Ouro Preto, Minas Gerais State, Brazil
Figure 4. Odonata species accumulation curves: (A) collector method; (B) rarefaction curve. (vertical bars represent ± 2 standard deviations).
Figure 2 in Distribution and composition of Dragonfly and Damselfly species (Odonata) of the upper Rio das Velhas, Ouro Preto, Minas Gerais State, Brazil
Figure 2. Some species of the suborder Anisoptera collected in the upper Rio das Velhas, Parque Natural Municipal Cachoeira das Andorinhas, Ouro Preto Municipality, Minas Gerais State, Brazil: (a) Erythrodiplax acantha, (b) Elasmothemis alcebiadesi, (c) Zonophora campanulata machadoi, (d) Castoraeschna colorata, (e) Coryphaeschna perrensi and (f) Macrothemis heteronycha.
Figure 1 in Distribution and composition of Dragonfly and Damselfly species (Odonata) of the upper Rio das Velhas, Ouro Preto, Minas Gerais State, Brazil
Figure 1. Map showing the sampled sites in the, Parque Natural Municipal Cachoeira das Andorinhas (green line), Ouro Preto Municipality, Minas Gerais State, Brazil.
Figure 3 in Distribution and composition of Dragonfly and Damselfly species (Odonata) of the upper Rio das Velhas, Ouro Preto, Minas Gerais State, Brazil
Figure 3. Some species of the suborder Zygoptera collected in the upper Rio das Velhas, Parque Natural Municipal Cachoeira das Andorinhas, Ouro Preto Municipality, Minas Gerais State, Brazil: (a) Heteragrion cauei, (b) Heteragrion rogertaylori, (c) Telebasis carmesina, (d) Heteragrion gracile, (e) Oxyagrion basale, (f) Hetaerina longipes, (g) Minagrion waltheri and (h) Mnesarete guttifera.
Data from: Is there a disease-free halo at species range limits? The co-distribution of anther-smut disease and its host species
1. While disease is widely recognized as affecting host population size, it has rarely been considered to play a role in determining host range limits. Many diseases may not be able to persist near the range limit if host population density falls below the critical threshold level for pathogen invasion. However, in vector- and sexually-transmitted diseases, pathogen transmission may be largely independent of host density and theory demonstrates that diseases with frequency-dependent transmission may persist in small populations near the range limit. 2. Empirical studies of disease at species range limits have lagged behind the theory, and to date, no previous study has tested the hypothesis that vector or sexually transmitted diseases can be maintained at host range limits. 3. We studied the distribution of anther-smut disease, a sterilizing pollinator-transmitted disease, on four alpine plant species to determine whether disease was present at the host range limits. 4. We found that host abundance declined towards the elevational range limits, and disease extended to the most extreme elevational range limits in three of the four host species. Maximum likelihood estimation of the magnitude of the disease-free halo showed that it was small or non-existent for all host species. Moreover, disease prevalence within populations was often higher nearer the host's range limit than in the range center and was independent of host density. 5. Synthesis: Our results show that diseases where transmission is frequency-dependent have the potential to affect host distributions not just in theory, but also in real world populations.
Hierarchical multi-grain models improve descriptions of species' environmental associations, distribution, and abundance
<p>The characterization of species' environmental niches and spatial distribution predictions based on them are now central to much of ecology and conservation, but implicitly requires decisions about the appropriate spatial scale (i.e. <i>grain</i>) of analysis. Ecological theory and empirical evidence suggest that range-resident species respond to their environment at two characteristic, hierarchical spatial grains: (i) <i>response grain</i>, the (relatively fine) grain at which an individual uses environmental resources, and (ii) <i>occupancy grain</i>,<i> </i>the (relatively coarse) grain equivalent to a typical home range. We use a multi-grain (MG) occupancy model, aided by fine-grain remotely sensed imagery, to simultaneously estimate species-environment associations at both grains, conduct grain optimization to measure response grain, and apply this analysis framework to an example species: a medium-sized bird (<i>Tockus deckeni</i>) in a heterogeneous East African landscape. Based on home range analysis of movement data, we calculate an occupancy grain of 1km for <i>T. deckeni</i>. Using a grain optimization procedure across 32 grains from 10m to 500m, we identify 60m as the most strongly supported response grain for a suite of environmental variables, slightly coarser than opportunistic behavioral observations would have suggested. Validation confirms that the accuracy of the optimized MG occupancy model substantially exceeds that of equivalent single-grain (SG) occupancy models. We further use a simulation approach to assess the potential impacts of accounting for the multi-scale structure of species' environmental requirements on estimates of population size. We find that the more strongly supported MG approach consistently predicts a minimum population sizes in the study landscape that is much lower than that provided by the SG model. This suggests that SG approaches commonly used in conservation applications could lead to overly optimistic abundance and population estimates and that the MG approach may be more appropriate for supporting species conservation goals. More generally, we conclude that multi-grain approaches of the sort presented, and increasingly enabled by growing high-resolution remotely sensed data, hold great promise for offering a more mechanistic framework for assessing the appropriate grain(s) for population monitoring and management and enable more reliable estimates of abundances and species' distributions.</p>
Data from: Descriptions of four new species of Minyomerus Horn, 1876 sec. Jansen & Franz, 2018 (Coleoptera: Curculionidae), with notes on their distribution and phylogeny
This contribution adopts the taxonomic concept approach, including the use of taxonomic concept labels (name sec. [according to] source) and region connection calculus-5 (RCC–5) articulations and alignments. Prior to this study, the broad-nosed weevil genus Minyomerus Horn, 1876 sec. Jansen & Franz, 2015 (Curculionidae [non-focal]: Entiminae [non-focal]: Tanymecini [non-focal]) contained 17 species distributed throughout the desert and plains regions of North America. In this review of Minyomerus sec. Jansen & Franz, 2018, we describe the following four species as new to science: Minyomerus ampullaceus sec. Jansen & Franz, 2018 (henceforth: [JF2018]), new species, Minyomerus franko [JF2018], new species, Minyomerus sculptilis [JF2018], new species, and Minyomerus tylotos [JF2018], new species. The four new species are added to, and integrated with, the preceding revision, and an updated key and phylogeny of Minyomerus [JF2018] are presented. A cladistic analysis using 52 morphological characters of 26 terminal taxa (5/21 outgroup/ingroup) yielded a single most-parsimonious cladogram (Length = 99 steps, consistency index = 60, retention index = 80). The analysis reaffirms the monophyly of Minyomerus [JF2018] with eight unreversed synapomorphies. The species-group placements, possible biogeographic origins, and natural history of the new species are discussed in detail.
Figures 22-24 in A new species of Neopsis (Hemiptera: Cicadellidae: Tartessinae), with new distribution records from Paraná State, Brazil
Figures 22-24. Habitus, in dorsal view, of the three species of Neopsis recorded from state of Paraná, Brazil. (22) N. aurea Takiya & Dietrich. (23) N. myrceugeniae Takiya & Dietrich. (24) N. robusta Linnavuori.
Figures 2-10 in A new species of Neopsis (Hemiptera: Cicadellidae: Tartessinae), with new distribution records from Paraná State, Brazil
Figures 2-10. Neopsis campestris sp. nov., holotype male. (2) habitus, dorsal view. (3) habitus, lateral view. (4) head, ventral view. (5) genital capsule, lateral view. (6) pygofer, valve, anal tube and subgenital plate, lateral view. (7) valve and subgenital plate, ventral view. (8) connective and style, dorsal view. (9) aedeagus, lateral view. (10) enlarged view of apex of aedeagal shaft. Scale bars: in mm.
Figure 1 in A new species of Neopsis (Hemiptera: Cicadellidae: Tartessinae), with new distribution records from Paraná State, Brazil
Figure 1. Distribution map of Neopsis species on different ecoregions of the Atlantic forest in Paraná State, Brazil.
Figures 11-21 in A new species of Neopsis (Hemiptera: Cicadellidae: Tartessinae), with new distribution records from Paraná State, Brazil
Figures 11-21. Neopsis campestris sp. nov., female paratype. (11) habitus, dorsal view. (12) habitus, lateral view. (13) head, ventral view. (14) esternite VII, ventral view. (15) distal portion of abdomen, ventral view. (16) distal portion of abdomen, lateral view. (17) first valvifer and first valvula, lateral view. (18) apical portion of first valvula. (19) second valvula, lateral view. (20) apical portion of second valvula. (21) second valvifer and gonoplac, lateral view. Scale bars: in mm.
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.
Data from: Effectiveness of joint species distribution models in the presence of imperfect detection
<p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p>
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: Distributional shifts – not geographic isolation – as a probable driver of montane species divergence
As biodiversity hotspots, montane regions have been a focus of research to understand the divergence process. Like their oceanic counterparts, the diversity of the "sky islands" might be ascribed to geographic isolation of mountaintops. However, because the sky islands, and especially those in northern latitudes, are subject to extreme climatic events such as the glacial cycles that drove both altitudinal and geographical shifts in species' distributions, the dynamic colonization process is also a possible factor driving divergence. Here we test these two hypotheses (i.e., isolation versus colonization) in a flightless montane grasshopper, Melanoplus oregonensis, which is a member of a diverse group that radiated across the Rocky Mountains of North America. Using approximate Bayesian computation (ABC) and spatially explicit simulations that account for spatial heterogeneity and temporal shifts in species distributions, we show that a colonization model of the sky islands from refugial populations provides a significantly better fit to the empirical genetic data than a model of the geographic isolation among sky islands. Moreover, support for the colonization model holds irrespective of whether the movement of individuals was modeled as a diffusion process or was informed by differences in habitat suitabilities across the landscape. With validation analyses to confirm the models provide a good fit to the data, as well as general power and quality analyses, the research not only adds to a growing body of work on the complex dynamics underlying montane biodiversity, but it also provides much needed evaluation of competing hypotheses based on explicit models of the divergence process, as opposed to inferences about diversification drivers from species diversity patterns.
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.
Fig. 16 in Taxonomy and distribution of the genus Trichomyrmex Mayr, 1865 (Hymenoptera: Formicidae) in the Arabian Peninsula, with the description of two new species
Fig. 16. Distribution map of Trichomyrmex mayri (Forel, 1902).
Fig. 15 in Taxonomy and distribution of the genus Trichomyrmex Mayr, 1865 (Hymenoptera: Formicidae) in the Arabian Peninsula, with the description of two new species
Fig. 15. Distribution map of Trichomyrmex destructor (Jerdon, 1851).
Figure 9. - Distribution of the Epicephala species in Japan. A Epicephalaanthophilia B Epicephalabipollenella C Epicephalalanceolatella (blue) and Epicephalaperplexa (green) D Epicephalaobovatella (blue) and Epicephalacorruptrix (green) E Epicephalavitisidaea F Epicephalaparasitica (blue) and Epicephalanudilingua (green). Information based on this study and Kawakita and Kato (2006).
Figure 9. - Distribution of the Epicephala species in Japan. A Epicephalaanthophilia B Epicephalabipollenella C Epicephalalanceolatella (blue) and Epicephalaperplexa (green) D Epicephalaobovatella (blue) and Epicephalacorruptrix (green) E Epicephalavitisidaea F Epicephalaparasitica (blue) and Epicephalanudilingua (green). Information based on this study and Kawakita and Kato (2006).
Figure 1. from Extending Marine Species Distribution Maps Using Non-Traditional Sources - Biodiversity Data Journal 3: e4900 (17 April 2015) https://doi.org/10.3897/BDJ.3.e4900
Figure 1. - The IUCN Red List Review Process (IUCN 2014a). Steps refer to the DOCUMENTATION STANDARDS AND CONSISTENCY CHECKS FOR IUCN RED LIST ASSESSMENTS AND SPECIES ACCOUNTS (IUCN 2013).
ScienceDex guides
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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.