fuzzySim: applying fuzzy logic to binary similarity indices in ecology

Binary similarity indices are widely used in ecology, for example for detecting associations between species occurrence patterns, comparing regional and temporal species assemblages, and assessing beta diversity patterns, including spatial and temporal species loss and turnover. Such indices have widespread applications in biogeography, global change biology and biodiversity conservation. Similarity indices are commonly calculated upon binary presence/absence (or sometimes modelled suitable/unsuitable) data, which are generally incomplete and more categorical than their underlying natural patterns. Probable false absences are disregarded, amplifying the effects of data deficiencies and the scale dependence of the results. Fuzzy occurrence data, with a degree of uncertainty attributed to localities where presence or absence cannot be safely assigned, could better reflect species distributions, compensating for incomplete knowledge and methodological errors. Similarity indices would therefore also benefit from accommodating such fuzzy data directly. This study proposes fuzzy versions of the binary similarity indices most commonly used in ecology, so that they can be directly applied to continuous (fuzzy) rather than binary occurrence values, thus producing more realistic similarity assessments. Fuzzy occurrence can be obtained with several methods, some of which are also provided. The procedure is robust to data source disparities, gaps or other errors in species occurrence records, even for restricted species for which slight inaccuracies can affect substantial parts of their range. The method is implemented in a free and open-source software package, fuzzySim, which is available for the R statistical software and under implementation for the QGIS geographic information system. It is provided with sample data and an illustrated tutorial suitable for non-experienced users

Colony size predicts division of labour in Attine ants

Division of labour is central to the ecological success of eusocial insects, yet the evolutionary factors driving increases in complexity in division of labour are little known. The size–complexity hypothesis proposes that, as larger colonies evolve, both non-reproductive and reproductive division of labour become more complex as workers and queens act to maximize inclusive fitness. Using a statistically robust phylogenetic comparative analysis of social and environmental traits of species within the ant tribe Attini, we show that colony size is positively related to both non-reproductive (worker size variation) and reproductive (queen–worker dimorphism) division of labour. The results also suggested that colony size acts on non-reproductive and reproductive division of labour in different ways. Environmental factors, including measures of variation in temperature and precipitation, had no significant effects on any division of labour measure or colony size. Overall, these results support the size–complexity hypothesis for the evolution of social complexity and division of labour in eusocial insects. Determining the evolutionary drivers of colony size may help contribute to our understanding of the evolution of social complexity

Colony size predicts division of labour in Attine ants

Division of labour is central to the ecological success of eusocial insects, yet the evolutionary factors driving increases in complexity in division of labour are little known. The size–complexity hypothesis proposes that, as larger colonies evolve, both non-reproductive and reproductive division of labour become more complex as workers and queens act to maximize inclusive fitness. Using a statistically robust phylogenetic comparative analysis of social and environmental traits of species within the ant tribe Attini, we show that colony size is positively related to both non-reproductive (worker size variation) and reproductive (queen–worker dimorphism) division of labour. The results also suggested that colony size acts on non-reproductive and reproductive division of labour in different ways. Environmental factors, including measures of variation in temperature and precipitation, had no significant effects on any division of labour measure or colony size. Overall, these results support the size–complexity hypothesis for the evolution of social complexity and division of labour in eusocial insects. Determining the evolutionary drivers of colony size may help contribute to our understanding of the evolution of social complexity

Mammalian biogeography and the Ebola virus in Africa

Ebola virus is responsible for the fatal Ebola-virus disease (EVD). Links between EVD outbreaks in Africa and 3 chiropteran species, presumed to be reservoirs for the Ebola virus, have been suggested, but discussions are still on-going. There is also evidence of significant virus spillover among mammal species not suspected to be natural hosts (e.g. chimpanzees, gorillas and duikers). We mapped the potential distribution of the Ebola virus in Africa based on both environmental and zoogeographic descriptors. We employed distribution modelling using the Favourability Function, alongside a complement of biogeographic approaches including chorotype analysis. We obtained a significantly well-calibrated model defining the distribution of environmentally favourable areas for the presence of Ebola virus, which was outperformed by a model determining favourable areas according to mammalian biogeography. Finally, we built a model in which the combined landscape and mammalian distribution types better explained the distribution of Ebola virus independent of human-to-human transmissions. Our findings show that the core area for the virus is associated with infections of known animal origin, but surrounded by areas where human infections of unknown source were found. This difference in the association between human and animals and the virus may offer further insights to better understand how EVD can spread, as well as providing the basis for an early warning system based on where human contact with multiple animal species may occur. We propose a biogeographically-justified list of at least 64 mammal species whose link with Ebola virus is worth investigating

A Descriptive Morphology of the Ant Genus Procryptocerus (Hymenoptera: Formicidae)

Morphology is the most direct approach biologists have to recognize uniqueness of insect species as compared to close relatives. Ants of the genus Procryptocerus possess important morphologic characters yet have not been explored for use in a taxonomic revision. The genus is characterized by the protrusion of the clypeus forming a broad nasus and antennal scrobes over the eyes. The toruli are located right posterior to the flanks of the nasus opposite to each other. The vertex is deflexed posteriorly in most species. An in-group comparison of the external morphology is presented focusing on the workers. A general morphology for gynes and males is also presented. Previously mentioned characters as well as new ones are presented, and their character states in different species are clarified. For the metasoma a new system of ant metasomal somite nomenclature is presented that is applicable to Aculeata in general. Finally, a Glossary of morphological terms is offered for the genus (available online). Most of the terminology can be used in other members of the Formicidae and Aculeata

Improved Phylogenetic Analyses Corroborate a Plausible Position of Martialis heureka in the Ant Tree of Life

Martialinae are pale, eyeless and probably hypogaeic predatory ants. Morphological character sets suggest a close relationship to the ant subfamily Leptanillinae. Recent analyses based on molecular sequence data suggest that Martialinae are the sister group to all extant ants. However, by comparing molecular studies and different reconstruction methods, the position of Martialinae remains ambiguous. While this sister group relationship was well supported by Bayesian partitioned analyses, Maximum Likelihood approaches could not unequivocally resolve the position of Martialinae. By re-analysing a previous published molecular data set, we show that the Maximum Likelihood approach is highly appropriate to resolve deep ant relationships, especially between Leptanillinae, Martialinae and the remaining ant subfamilies. Based on improved alignments, alignment masking, and tree reconstructions with a sufficient number of bootstrap replicates, our results strongly reject a placement of Martialinae at the first split within the ant tree of life. Instead, we suggest that Leptanillinae are a sister group to all other extant ant subfamilies, whereas Martialinae branch off as a second lineage. This assumption is backed by approximately unbiased (AU) tests, additional Bayesian analyses and split networks. Our results demonstrate clear effects of improved alignment approaches, alignment masking and data partitioning. We hope that our study illustrates the importance of thorough, comprehensible phylogenetic analyses using the example of ant relationships

Recruitment Strategies and Colony Size in Ants

Ants use a great variety of recruitment methods to forage for food or find new nests, including tandem running, group recruitment and scent trails. It has been known for some time that there is a loose correlation across many taxa between species-specific mature colony size and recruitment method. Very small colonies tend to use solitary foraging; small to medium sized colonies use tandem running or group recruitment whereas larger colonies use pheromone recruitment trails. Until now, explanations for this correlation have focused on the ants' ecology, such as food resource distribution. However, many species have colonies with a single queen and workforces that grow over several orders of magnitude, and little is known about how a colony's organization, including recruitment methods, may change during its growth. After all, recruitment involves interactions between ants, and hence the size of the colony itself may influence which recruitment method is used—even if the ants' behavioural repertoire remains unchanged. Here we show using mathematical models that the observed correlation can also be explained by recognizing that failure rates in recruitment depend differently on colony size in various recruitment strategies. Our models focus on the build up of recruiter numbers inside colonies and are not based on optimality arguments, such as maximizing food yield. We predict that ant colonies of a certain size should use only one recruitment method (and always the same one) rather than a mix of two or more. These results highlight the importance of the organization of recruitment and how it is affected by colony size. Hence these results should also expand our understanding of ant ecology

Stridulations Reveal Cryptic Speciation in Neotropical Sympatric Ants

The taxonomic challenge posed by cryptic species underlines the importance of using multiple criteria in species delimitation. In the current paper we tested the use of acoustic analysis as a tool to assess the real diversity in a cryptic species complex of Neotropical ants. In order to understand the potential of acoustics and to improve consistency in the conclusions by comparing different approaches, phylogenetic relationships of all the morphs considered were assessed by the analysis of a fragment of the mitochondrial DNA cytochrome b. We observed that each of the cryptic morph studied presents a morphologically distinct stridulatory organ and that all sympatric morphs produce distinctive stridulations. This is the first evidence of such a degree of specialization in the acoustic organ and signals in ants, which suggests that stridulations may be among the cues used by these ants during inter-specific interactions. Mitochondrial DNA variation corroborated the acoustic differences observed, confirming acoustics as a helpful tool to determine cryptic species in this group of ants, and possibly in stridulating ants in general. Congruent morphological, acoustic and genetic results constitute sufficient evidence to propose each morph studied here as a valid new species, suggesting that P. apicalis is a complex of at least 6 to 9 species, even if they present different levels of divergence. Finally, our results highlight that ant stridulations may be much more informative than hitherto thought, as much for ant communication as for integrative taxonomists

A New (Old), Invasive Ant in the Hardwood Forests of Eastern North America and Its Potentially Widespread Impacts

Biological invasions represent a serious threat for the conservation of biodiversity in many ecosystems. While many social insect species and in particular ant species have been introduced outside their native ranges, few species have been successful at invading temperate forests. In this study, we document for the first time the relationship between the abundance of the introduced ant, Pachycondyla chinensis, in mature forests of North Carolina and the composition, abundance and diversity of native ant species using both a matched pair approach and generalized linear models. Where present, P. chinensis was more abundant than all native species combined. The diversity and abundance of native ants in general and many individual species were negatively associated with the presence and abundance of P. chinensis. These patterns held regardless of our statistical approach and across spatial scales. Interestingly, while the majority of ant species was strongly and negatively correlated with the abundance and presence of P. chinensis, a small subset of ant species larger than P. chinensis was either as abundant or even more abundant in invaded than in uninvaded sites. The large geographic range of this ant species combined with its apparent impact on native species make it likely to have cascading consequences on eastern forests in years to come, effects mediated by the specifics of its life history which is very different from those of other invasive ants. The apparent ecological impacts of P. chinensis are in addition to public health concerns associated with this species due to its sometimes, deadly sting

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19

The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction model using easy-to-obtain parameters to help to identify patients with COVID-19 who are at higher risk of death. The training cohort included all patients admitted to Fondazione Policlinico Gemelli with COVID-19 from March 5, 2020, to November 5, 2020. Afterward, the model was tested on all patients admitted to the same hospital with COVID-19 from November 6, 2020, to February 5, 2021. The primary outcome was in-hospital case-fatality risk. The out-of-sample performance of the model was estimated from the training set in terms of Area under the Receiving Operator Curve (AUROC) and classification matrix statistics by averaging the results of fivefold cross validation repeated 3-times and comparing the results with those obtained on the test set. An explanation analysis of the model, based on the SHapley Additive exPlanations (SHAP), is also presented. To assess the subsequent time evolution, the change in paO2/FiO2 (P/F) at 48 h after the baseline measurement was plotted against its baseline value. Among the 921 patients included in the training cohort, 120 died (13%). Variables selected for the model were age, platelet count, SpO2, blood urea nitrogen (BUN), hemoglobin, C-reactive protein, neutrophil count, and sodium. The results of the fivefold cross-validation repeated 3-times gave AUROC of 0.87, and statistics of the classification matrix to the Youden index as follows: sensitivity 0.840, specificity 0.774, negative predictive value 0.971. Then, the model was tested on a new population (n = 1463) in which the case-fatality rate was 22.6%. The test model showed AUROC 0.818, sensitivity 0.813, specificity 0.650, negative predictive value 0.922. Considering the first quartile of the predicted risk score (low-risk score group), the case-fatality rate was 1.6%, 17.8% in the second and third quartile (high-risk score group) and 53.5% in the fourth quartile (very high-risk score group). The three risk score groups showed good discrimination for the P/F value at admission, and a positive correlation was found for the low-risk class to P/F at 48 h after admission (adjusted R-squared = 0.48). We developed a predictive model of death for people with SARS-CoV-2 infection by including only easy-to-obtain variables (abnormal blood count, BUN, C-reactive protein, sodium and lower SpO2). It demonstrated good accuracy and high power of discrimination. The simplicity of the model makes the risk prediction applicable for patients in the Emergency Department, or during hospitalization. Although it is reasonable to assume that the model is also applicable in not-hospitalized persons, only appropriate studies can assess the accuracy of the model also for persons at home