Lanternfish (Myctophidae) Zoogeography off Eastern Australia: A Comparison with Physicochemical Biogeography

In this first attempt to model the distributions of a mesopelagic fish family at this scale in the eastern Australian region (10°S to 57°S), lanternfish species occurrence data spanning a period from 1928 to 2010 were modelled against environmental covariates. This involved: (1) data collation and taxonomic quality checking, (2) classification of trawls into “horizontal” (presence-absence) and “oblique” (presence-only) types, and classification of vertical migration patterns using existing literature and the species occurrence database, (3) binomial GAMs using presence-absence data for representative temperate, subtropical and tropical species to examine depth interactions with environmental covariates and refine the selection of environmental layers for presence-only MAXENT models, (4) Presence-only MAXENT modelling using data from all trawls and the reduced environmental layers, and (5) Multivariate analysis (area-wise and species-wise) of the resulting matrix of logistic score by geographic pixel. We test the hypothesis that major fronts in the region (Tasman Front, Subtropical Convergence, Subantarctic Front) represent zoogeographic boundaries. A four-region zoogeographic scheme is hypothesised: Coral Sea region, Subtropical Lower Water region, Subtropical Convergence/South Tasman region and Subantarctic region. The Tasman Front, Subtropical Convergence and Subantarctic Front represented zoogeographic boundaries. An additional boundary at ∼25°S (coined the ‘Capricorn’ boundary) was adopted to delineate the Coral Sea from Subtropical Lower Water regions. Lanternfish zoogeographic regions are congruent with some aspects of two prevailing physicochemical biogeographic schema in the region, but neither of these schema alone accurately predicts lanternfish distributions. As lanternfishes integrate vertical ocean processes, the hypothesised lanternfish zoogeography may represent a useful model for a generalised pelagic biogeography that should be tested for other oceanic groups.     

Novel Three-Step Pseudo-Absence Selection Technique for Improved Species Distribution Modelling

Pseudo-absence selection for spatial distribution models (SDMs) is the subject of ongoing investigation. Numerous techniques continue to be developed, and reports of their effectiveness vary. Because the quality of presence and absence data is key for acceptable accuracy of correlative SDM predictions, determining an appropriate method to characterise pseudo-absences for SDM’s is vital. The main methods that are currently used to generate pseudo-absence points are: 1) randomly generated pseudo-absence locations from background data; 2) pseudo-absence locations generated within a delimited geographical distance from recorded presence points; and 3) pseudo-absence locations selected in areas that are environmentally dissimilar from presence points. There is a need for a method that considers both geographical extent and environmental requirements to produce pseudo-absence points that are spatially and ecologically balanced. We use a novel three-step approach that satisfies both spatial and ecological reasons why the target species is likely to find a particular geo-location unsuitable. Step 1 comprises establishing a geographical extent around species presence points from which pseudo-absence points are selected based on analyses of environmental variable importance at different distances. This step gives an ecologically meaningful explanation to the spatial range of background data, as opposed to using an arbitrary radius. Step 2 determines locations that are environmentally dissimilar to the presence points within the distance specified in step one. Step 3 performs K-means clustering to reduce the number of potential pseudo-absences to the desired set by taking the centroids of clusters in the most environmentally dissimilar class identified in step 2. By considering spatial, ecological and environmental aspects, the three-step method identifies appropriate pseudo-absence points for correlative SDMs. We illustrate this method by predicting the New Zealand potential distribution of the Asian tiger mosquito (Aedes albopictus) and the Western corn rootworm (Diabrotica virgifera virgifera).     

Is more data always better? A simulation study of benefits and limitations of integrated distribution models

Species distribution models are popular and widely applied ecological tools. Recent increases in data availability have led to opportunities and challenges for species distribution modelling. Each data source has different qualities, determined by how it was collected. As several data sources can inform on a single species, ecologists have often analysed just one of the data sources, but this loses information, as some data sources are discarded. Integrated distribution models (IDMs) were developed to enable inclusion of multiple datasets in a single model, whilst accounting for different data collection protocols. This is advantageous because it allows efficient use of all data available, can improve estimation and account for biases in data collection. What is not yet known is when integrating different data sources does not bring advantages. Here, for the first time, we explore the potential limits of IDMs using a simulation study integrating a spatially biased, opportunistic, presence-only dataset with a structured, presence–absence dataset. We explore four scenarios based on real ecological problems; small sample sizes, low levels of detection probability, correlations between covariates and a lack of knowledge of the drivers of bias in data collection. For each scenario we ask; do we see improvements in parameter estimation or the accuracy of spatial pattern prediction in the IDM versus modelling either data source alone? We found integration alone was unable to correct for spatial bias in presence-only data. Including a covariate to explain bias or adding a flexible spatial term improved IDM performance beyond single dataset models, with the models including a flexible spatial term producing the most accurate and robust estimates. Increasing the sample size of presence–absence data and having no correlated covariates also improved estimation. These results demonstrate under which conditions integrated models provide benefits over modelling single data sources.     

A multi‐data ensemble approach for predicting woodland type distribution: Oak woodland in Britain

Interactions between soil, topography, and climatic site factors can exacerbate and/or alleviate the vulnerability of oak woodland to climate change. Reducing climate‐related impacts on oak woodland habitats and ecosystems through adaptation management requires knowledge of different site interactions in relation to species tolerance. In Britain, the required thematic detail of woodland type is unavailable from digital maps. A species distribution model (SDM) ensemble, using biomod2 algorithms, was used to predict oak woodland. The model was cross‐validated (50%:50% ‐ training:testing) 30 times, with each of 15 random sets of absence data, matching the size of presence data, to maximize environmental variation while maintaining data prevalence. Four biomod2 algorithms provided stable and consistent TSS‐weighted ensemble mean results predicting oak woodland as a probability raster. Biophysical data from the Ecological Site Classification (forest site classification) for Britain were used to characterize oak woodland sites. Several forest datasets were used, each with merits and weaknesses: public forest estate subcompartment database map (PFE map) for oak‐stand locations as a training dataset; the national forest inventory (NFI) “published regional reports” of oak woodland area; and an “NFI map” of indicative forest type broad habitat. Broadleaved woodland polygons of the NFI map were filled with the biomod2 oak woodland probability raster. Ranked pixels were selected up to the published NFI regional area estimate of oak woodland and matched to the elevation distribution of oak woodland stands, from “NFI survey” sample squares. Validation using separate oak woodland data showed that the elevation filter significantly improved the accuracy of predictions from 55% (p = .53) to 83% coincidence success rate (p < .0001). The biomod2 ensemble, with masking and filtering, produced a predicted oak woodland map, from which site characteristics will be used in climate change interaction studies, supporting adaptation management recommendations for forest policy and practice.     

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.     

Non-Listening and Self Centered Leadership – Relationships to Socioeconomic Conditions and Employee Mental Health

Background

The way in which leadership is experienced in different socioeconomic strata is of interest per se, as well as how it relates to employee mental health.

Methods

Three waves of SLOSH (Swedish Longitudinal Occupational Survey of Health, a questionnaire survey on a sample of the Swedish working population) were used, 2006, 2008 and 2010 (n = 5141). The leadership variables were: “Non-listening leadership” (one question: “Does your manager listen to you?” - four response categories), “Self centered leadership” (sum of three five-graded questions – “non-participating”, “asocial” and “loner”). The socioeconomic factors were education and income. Emotional exhaustion and depressive symptoms were used as indicators of mental health.

Results

Non-listening leadership was associated with low income and low education whereas self-centered leadership showed a weaker relationship with education and no association at all with income. Both leadership variables were significantly associated with emotional exhaustion and depressive symptoms. “Self centered” as well as “non-listening” leadership in 2006 significantly predicted employee depressive symptoms in 2008 after adjustment for demographic variables. These predictions became non-significant when adjustment was made for job conditions (demands and decision latitude) in the “non-listening” leadership analyses, whereas predictions of depressive symptoms remained significant after these adjustments in the “self-centered leadership” analyses.

Conclusions

Our results show that the leadership variables are associated with socioeconomic status and employee mental health. “Non-listening” scores were more sensitive to societal change and more strongly related to socioeconomic factors and job conditions than “self-centered” scores.     

Silvicolous on a Small Scale: Possibilities and Limitations of Habitat Suitability Models for Small,Elusive Mammals in Conservation Management and Landscape Planning

Species distribution and endangerment can be assessed by habitat-suitability modelling. This study addresses methodical aspects of habitat suitability modelling and includes an application example in actual species conservation and landscape planning. Models using species presence-absence data are preferable to presence-only models. In contrast to species presence data, absences are rarely recorded. Therefore, many studies generate pseudo-absence data for modelling. However, in this study model quality was higher with null samples collected in the field. Next to species data the choice of landscape data is crucial for suitability modelling. Landscape data with high resolution and ecological relevance for the study species improve model reliability and quality for small elusive mammals like Muscardinus avellanarius. For large scale assessment of species distribution, models with low-detailed data are sufficient. For regional site-specific conservation issues like a conflict-free site for new wind turbines, high-detailed regional models are needed. Even though the overlap with optimally suitable habitat for M. avellanarius was low, the installation of wind plants can pose a threat due to habitat loss and fragmentation. To conclude, modellers should clearly state the purpose of their models and choose the according level of detail for species and environmental data.     

The Use of a Predictive Habitat Model and a Fuzzy Logic Approach for Marine Management and Planning

Bottom trawl survey data are commonly used as a sampling technique to assess the spatial distribution of commercial species. However, this sampling technique does not always correctly detect a species even when it is present, and this can create significant limitations when fitting species distribution models. In this study, we aim to test the relevance of a mixed methodological approach that combines presence-only and presence-absence distribution models. We illustrate this approach using bottom trawl survey data to model the spatial distributions of 27 commercially targeted marine species. We use an environmentally- and geographically-weighted method to simulate pseudo-absence data. The species distributions are modelled using regression kriging, a technique that explicitly incorporates spatial dependence into predictions. Model outputs are then used to identify areas that met the conservation targets for the deployment of artificial anti-trawling reefs. To achieve this, we propose the use of a fuzzy logic framework that accounts for the uncertainty associated with different model predictions. For each species, the predictive accuracy of the model is classified as ‘high’. A better result is observed when a large number of occurrences are used to develop the model. The map resulting from the fuzzy overlay shows that three main areas have a high level of agreement with the conservation criteria. These results align with expert opinion, confirming the relevance of the proposed methodology in this study.     

DNA Barcoding and Species Boundary Delimitation of Selected Species of Chinese Acridoidea (Orthoptera: Caelifera)

We tested the performance of DNA barcoding in Acridoidea and attempted to solve species boundary delimitation problems in selected groups using COI barcodes. Three analysis methods were applied to reconstruct the phylogeny. K2P distances were used to assess the overlap range between intraspecific variation and interspecific divergence. “Best match (BM)”, “best close match (BCM)”, “all species barcodes (ASB)” and “back-propagation neural networks (BP-based method)” were utilized to test the success rate of species identification. Phylogenetic species concept and network analysis were employed to delimitate the species boundary in eight selected species groups. The results demonstrated that the COI barcode region performed better in phylogenetic reconstruction at genus and species levels than at higher-levels, but showed a little improvement in resolving the higher-level relationships when the third base data or both first and third base data were excluded. Most overlaps and incorrect identifications may be due to imperfect taxonomy, indicating the critical role of taxonomic revision in DNA barcoding study. Species boundary delimitation confirmed the presence of oversplitting in six species groups and suggested that each group should be treated as a single species.     

Sex Differences and Emotion Regulation: An Event-Related Potential Study

Difficulties in emotion regulation have been implicated as a potential mechanism underlying anxiety and mood disorders. It is possible that sex differences in emotion regulation may contribute towards the heightened female prevalence for these disorders. Previous fMRI studies of sex differences in emotion regulation have shown mixed results, possibly due to difficulties in discriminating the component processes of early emotional reactivity and emotion regulation. The present study used event-related potentials (ERPs) to examine sex differences in N1 and N2 components (reflecting early emotional reactivity) and P3 and LPP components (reflecting emotion regulation). N1, N2, P3, and LPP were recorded from 20 men and 23 women who were instructed to “increase,” “decrease,” and “maintain” their emotional response during passive viewing of negative images. Results indicated that women had significantly greater N1 and N2 amplitudes (reflecting early emotional reactivity) to negative stimuli than men, supporting a female negativity bias. LPP amplitudes increased to the “increase” instruction, and women displayed greater LPP amplitudes than men to the “increase” instruction. There were no differences to the “decrease” instruction in women or men. These findings confirm predictions of the female negativity bias hypothesis and suggest that women have greater up-regulation of emotional responses to negative stimuli. This finding is highly significant in light of the female vulnerability for developing anxiety disorders.     

Amplification Biases and Consistent Recovery of Loci in a Double-Digest RAD-seq Protocol

A growing variety of “genotype-by-sequencing” (GBS) methods use restriction enzymes and high throughput DNA sequencing to generate data for a subset of genomic loci, allowing the simultaneous discovery and genotyping of thousands of polymorphisms in a set of multiplexed samples. We evaluated a “double-digest” restriction-site associated DNA sequencing (ddRAD-seq) protocol by 1) comparing results for a zebra finch (Taeniopygia guttata) sample with in silico predictions from the zebra finch reference genome; 2) assessing data quality for a population sample of indigobirds (Vidua spp.); and 3) testing for consistent recovery of loci across multiple samples and sequencing runs. Comparison with in silico predictions revealed that 1) over 90% of predicted, single-copy loci in our targeted size range (178–328 bp) were recovered; 2) short restriction fragments (38–178 bp) were carried through the size selection step and sequenced at appreciable depth, generating unexpected but nonetheless useful data; 3) amplification bias favored shorter, GC-rich fragments, contributing to among locus variation in sequencing depth that was strongly correlated across samples; 4) our use of restriction enzymes with a GC-rich recognition sequence resulted in an up to four-fold overrepresentation of GC-rich portions of the genome; and 5) star activity (i.e., non-specific cutting) resulted in thousands of “extra” loci sequenced at low depth. Results for three species of indigobirds show that a common set of thousands of loci can be consistently recovered across both individual samples and sequencing runs. In a run with 46 samples, we genotyped 5,996 loci in all individuals and 9,833 loci in 42 or more individuals, resulting in <1% missing data for the larger data set. We compare our approach to similar methods and discuss the range of factors (fragment library preparation, natural genetic variation, bioinformatics) influencing the recovery of a consistent set of loci among samples.     

Niche theory‐based modeling of assembly processes of viral communities in bats

Understanding the assembly processes of symbiont communities, including viromes and microbiomes, is important for improving predictions on symbionts’ biogeography and disease ecology. Here, we use phylogenetic, functional, and geographic filters to predict the similarity between symbiont communities, using as a test case the assembly process in viral communities of Mexican bats. We construct generalized linear models to predict viral community similarity, as measured by the Jaccard index, as a function of differences in host phylogeny, host functionality, and spatial co‐occurrence, evaluating the models using the Akaike information criterion. Two model classes are constructed: a “known” model, where virus–host relationships are based only on data reported in Mexico, and a “potential” model, where viral reports of all the Americas are used, but then applied only to bat species that are distributed in Mexico. Although the “known” model shows only weak dependence on any of the filters, the “potential” model highlights the importance of all three filter types—phylogeny, functional traits, and co‐occurrence—in the assemblage of viral communities. The differences between the “known” and “potential” models highlight the utility of modeling at different “scales” so as to compare and contrast known information at one scale to another one, where, for example, virus information associated with bats is much scarcer.     

Publication Bias in Reports of Animal Stroke Studies Leads to Major Overstatement of Efficacy

The consolidation of scientific knowledge proceeds through the interpretation and then distillation of data presented in research reports, first in review articles and then in textbooks and undergraduate courses, until truths become accepted as such both amongst “experts” and in the public understanding. Where data are collected but remain unpublished, they cannot contribute to this distillation of knowledge. If these unpublished data differ substantially from published work, conclusions may not reflect adequately the underlying biological effects being described. The existence and any impact of such “publication bias” in the laboratory sciences have not been described. Using the CAMARADES (Collaborative Approach to Meta-analysis and Review of Animal Data in Experimental Studies) database we identified 16 systematic reviews of interventions tested in animal studies of acute ischaemic stroke involving 525 unique publications. Only ten publications (2%) reported no significant effects on infarct volume and only six (1.2%) did not report at least one significant finding. Egger regression and trim-and-fill analysis suggested that publication bias was highly prevalent (present in the literature for 16 and ten interventions, respectively) in animal studies modelling stroke. Trim-and-fill analysis suggested that publication bias might account for around one-third of the efficacy reported in systematic reviews, with reported efficacy falling from 31.3% to 23.8% after adjustment for publication bias. We estimate that a further 214 experiments (in addition to the 1,359 identified through rigorous systematic review; non publication rate 14%) have been conducted but not reported. It is probable that publication bias has an important impact in other animal disease models, and more broadly in the life sciences.     

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

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

Point-estimating observer models for latent cause detection

The spatial distribution of visual items allows us to infer the presence of latent causes in the world. For instance, a spatial cluster of ants allows us to infer the presence of a common food source. However, optimal inference requires the integration of a computationally intractable number of world states in real world situations. For example, optimal inference about whether a common cause exists based on N spatially distributed visual items requires marginalizing over both the location of the latent cause and 2^N possible affiliation patterns (where each item may be affiliated or non-affiliated with the latent cause). How might the brain approximate this inference? We show that subject behaviour deviates qualitatively from Bayes-optimal, in particular showing an unexpected positive effect of N (the number of visual items) on the false-alarm rate. We propose several “point-estimating” observer models that fit subject behaviour better than the Bayesian model. They each avoid a costly computational marginalization over at least one of the variables of the generative model by “committing” to a point estimate of at least one of the two generative model variables. These findings suggest that the brain may implement partially committal variants of Bayesian models when detecting latent causes based on complex real world data.     

On the Origins and Control of Community Types in the Human Microbiome

Microbiome-based stratification of healthy individuals into compositional categories, referred to as “enterotypes” or “community types”, holds promise for drastically improving personalized medicine. Despite this potential, the existence of community types and the degree of their distinctness have been highly debated. Here we adopted a dynamic systems approach and found that heterogeneity in the interspecific interactions or the presence of strongly interacting species is sufficient to explain community types, independent of the topology of the underlying ecological network. By controlling the presence or absence of these strongly interacting species we can steer the microbial ecosystem to any desired community type. This open-loop control strategy still holds even when the community types are not distinct but appear as dense regions within a continuous gradient. This finding can be used to develop viable therapeutic strategies for shifting the microbial composition to a healthy configuration.     

Effects of Biogents Sentinel Trap Field Placement on Capture Rates of Adult Asian Tiger Mosquitoes,Aedes albopictus

The Biogents® Sentinel (BGS) trap is the standard tool to monitor adult Aedes (Stegomyia) albopictus (Skuse) (Diptera: Culicidae), the Asian tiger mosquito. BGS traps are commonly placed in residential properties during surveillance operations, but locations within properties may have significant differences in ambient light, temperature, and humidity (e.g. between a sunlit lawn and shady underbrush). We examined the effect of BGS trap placement on Ae. albopictus capture rates in three residential properties in Monmouth County, New Jersey, USA. In each property we visually selected locations as shade, partial shade, and sun. Traps in “partial shade” locations were under vegetation and were exposed to filtered sunlight during some parts of the day while “shaded” locations were never exposed to direct sunlight. Locations defined as “sun” were exposed to direct sunlight for large parts of the day. We placed a BGS trap in each of the three location types and used small data loggers to measure temperature, relative humidity, and light exposure at each trap during a 24-hour deployment. To address temporal variability, we made seven separate measurements from 31 August to 22 September 2010. We found that “partial shade” and “full shade” locations did not differ but that “full sun” locations had significantly higher light exposure, higher temperature, and lower humidity. Importantly, Ae. albopictus catches (males, females, or both) were consistently and significantly over 3 times higher in traps located in shaded locations. To further investigate the effects of local temperature and humidity on surveillance we examined Ae. albopictus collections from 37 BGS traps fitted with data loggers and deployed weekly from August through mid October, during the 2009 season, in three urban sites in Mercer County, NJ. We confirmed that local climate influences capture rates and that Ae. albopictus surveillance projects need to monitor trap placement carefully for maximum efficiency.     

Effects of “Reduced” and “Business-As-Usual” CO2 Emission Scenarios on the Algal Territories of the Damselfish Pomacentrus wardi (Pomacentridae)

Turf algae are a very important component of coral reefs, featuring high growth and turnover rates, whilst covering large areas of substrate. As food for many organisms, turf algae have an important role in the ecosystem. Farming damselfish can modify the species composition and productivity of such algal assemblages, while defending them against intruders. Like all organisms however, turf algae and damselfishes have the potential to be affected by future changes in seawater (SW) temperature and pCO₂. In this study, algal assemblages, in the presence and absence of farming Pomacentrus wardi were exposed to two combinations of SW temperature and pCO₂ levels projected for the austral spring of 2100 (the B1 “reduced” and the A1FI “business-as-usual” CO₂ emission scenarios) at Heron Island (GBR, Australia). These assemblages were dominated by the presence of red algae and non-epiphytic cyanobacteria, i.e. cyanobacteria that grow attached to the substrate rather than on filamentous algae. The endpoint algal composition was mostly controlled by the presence/absence of farming damselfish, despite a large variability found between the algal assemblages of individual fish. Different scenarios appeared to be responsible for a mild, species specific change in community composition, observable in some brown and green algae, but only in the absence of farming fish. Farming fish appeared unaffected by the conditions to which they were exposed. Algal biomass reductions were found under “reduced” CO₂ emission, but not “business-as-usual” scenarios. This suggests that action taken to limit CO₂ emissions may, if the majority of algae behave similarly across all seasons, reduce the potential for phase shifts that lead to algal dominated communities. At the same time the availability of food resources to damselfish and other herbivores would be smaller under “reduced” emission scenarios.