Mammal virus diversity estimates are unstable due to accelerating discovery effort

Host-virus association data underpin research into the distribution and eco-evolutionary correlates of viral diversity and zoonotic risk across host species. However, current knowledge of the wildlife virome is inherently constrained by historical discovery effort, and there are concerns that the reliability of ecological inference from host-virus data may be undermined by taxonomic and geographical sampling biases. Here, we evaluate whether current estimates of host-level viral diversity in wild mammals are stable enough to be considered biologically meaningful, by analysing a comprehensive dataset of discovery dates of 6571 unique mammal host-virus associations between 1930 and 2018. We show that virus discovery rates in mammal hosts are either constant or accelerating, with little evidence of declines towards viral richness asymptotes, even in highly sampled hosts. Consequently, inference of relative viral richness across host species has been unstable over time, particularly in bats, where intensified surveillance since the early 2000s caused a rapid rearrangement of species' ranked viral richness. Our results illustrate that comparative inference of host-level virus diversity across mammals is highly sensitive to even short-term changes in sampling effort. We advise caution to avoid overinterpreting patterns in current data, since it is feasible that an analysis conducted today could draw quite different conclusions than one conducted only a decade ago.

From trend to threat? Assessing the sustainability of wild edible plant foraging by linking local perception to ecological inference

Wild edible plants as culturally-appropriate sources of nutrition and for food security are now well-recognised. In Europe, the use of wild edible plants is shifting from a subsistence activity to an emerging trend in high-end gastronomy. The environmental impacts of this shift are poorly known. Foraging is increasingly popular for personal consumption and commercially, not least in the Nordic countries where popularity is fuelled by the New Nordic Food movement. Here, we evaluate if this trend entails biodiversity conservation risks in Norway. In collaboration with the Norwegian Association for Mycology and Foraging, we conducted 18 face-to-face interviews with key stakeholders and we published an online questionnaire filled by 219 recreational and professional foragers. We enquired on what species are harvested, by whom and how, where do foragers learn and what are their perspectives on the sustainability of foraging. We combined these data with an assessment of foraging impact based on foraging pressure, ecological traits and conservation assessments. Our results show that 272 different wild edible plants are foraged and that this is mostly sustainable.However, some risks arise from the harvest of threatened plants, the potential spread of invasive species, and the overharvesting of extremely popular or ‘fashionable’ species. Foraging fosters a strong connection with the natural environment and the majority of foragers report to forage as part of a sustainable lifestyle. We suggest that careful encouragement to forage and the participatory development of local guidelines for sustainable foraging in Norway can enhance people-nature relationships while safeguarding foraged plant populations.

ei.Datasets: Real Data Sets for Assessing Ecological Inference Algorithms

Ecological inference models aim to infer individual-level relationships using aggregate data. They are routinely used to estimate voter transitions between elections, disclose split-ticket voting behaviors, or infer racial voting patterns in U.S. elections. A large number of procedures have been proposed in the literature to solve these problems; therefore, an assessment and comparison of them are overdue. The secret ballot however makes this a difficult endeavor since real individual data are usually not accessible. The most recent work on ecological inference has assessed methods using a very small number of data sets with ground truth, combined with artificial, simulated data. This article dramatically increases the number of real instances by presenting a unique database (available in the R package ei.Datasets) composed of data from more than 550 elections where the true inner-cell values of the global cross-classification tables are known. The article describes how the data sets are organized, details the data curation and data wrangling processes performed, and analyses the main features characterizing the different data sets.

Mammal virus diversity estimates are unstable due to accelerating discovery effort

Host-virus association data form the backbone of research into eco-evolutionary drivers of viral diversity and host-level zoonotic risk. However, knowledge of the wildlife virome is inherently constrained by historical discovery effort, and there are concerns that the reliability of ecological inference from host-virus data may be undermined by taxonomic and geographical sampling biases. Here, we evaluate whether current estimates of host-level viral diversity in wild mammals are stable enough to be considered biologically meaningful, by analysing a comprehensive dataset of discovery dates of 6,571 unique mammal host-virus associations between 1930 and 2018. We show that virus discovery rates in mammal hosts are still either constant or accelerating, with little evidence of declines towards viral richness asymptotes in even highly-sampled hosts. Consequently, inference of relative viral richness across host species has been unstable over time, particularly in bats, where intensified surveillance since the early 2000s caused a rapid rearrangement of species' ranked viral richness. Our results show that comparative inference of host-level virus diversity across mammals is highly sensitive to even short-term changes in sampling effort. We advise caution to avoid overinterpreting patterns in current data, since our findings suggest that an analysis conducted today could feasibly draw quite different conclusions than one conducted only a decade ago.

Spanish electoral archive. SEA database

This paper introduces the SEA database (acronym for Spanish Electoral Archive). SEA brings together the most complete public repository available to date on Spanish election outcomes. SEA holds all the results recorded from the electoral processes of General (1979–2019), Regional (1989–2021), Local (1979–2019) and European Parliamentary (1987–2019) elections held in Spain since the restoration of democracy in the late 70 s, in addition to other data sets with electoral content. The data are offered for free and is presented in a homogeneous and friendly format. Most of the databases are available for download with data from various electoral levels, including from the ballot box level. This paper details how the information is organized, what the main variables are on offer for each election, which processes were applied to the data for their homogenization, and discusses future areas of work. This data has many applications, for example, as inputs in election prediction models and in ecological inference algorithms, to study determinants of turnout or voting, or for defining marketing strategies.

Mathematically and biologically consistent framework for presence-absence pairwise indices of diversity

Aim A large number of indices that compare two or more assemblages have been proposed or reinvented. The interpretation of the indices varies across the literature, despite efforts for clarification and unification. Most of the effort has focused on interdependence between the indices and the mathematics behind them. At the same time, following issues have been underestimated: (i) the difference between statistical independence of indices and the independence based on their informational value, and (ii) the inferences from the indices about diversity patterns and phenomena. Here we offer an alternative framework for diversity indices. Methods We distinguish different classes of dependence, and show that three indices which are mutually independent in terms of their information content are sufficient for appropriate inferences. This applies regardless of whether the indices are statistically correlated or not. We classify 20 existing indices into three main and four minor mutually independent families, and demonstrate how similarity between assemblages violates the stability of the families, confusing conceptually different patterns. We show what can be inferred about spatial diversity phenomena from different indices, demonstrate problems with most of the indices of nestedness, and show which combinations of indices may be used for meaningful ecological inference. Results and Main conclusions We demonstrate that no single index can properly filter out a single effect of a phenomena because the phenomena inevitably bound each other (e.g. species richness gradient bounds possible values of Jaccard index of community similarity). Consequently, inventing indices which seemingly purify these effect (e.g. pure turnover or pure nestedness) leads to misleading inference. In contrast, a proper inference is obtained by using a combination of classical indices from different, mutually independent families. Our framework provides a practical clue how to compare different indices across the literature.

Ecological inference using data from accelerometers needs careful protocols

1. Accelerometers in animal-attached tags have proven to be powerful tools in behavioural ecology, being used to determine behaviour and provide proxies for movement-based energy expenditure. Researchers are collecting and archiving data across systems, seasons and device types. However, in order to use data repositories to draw ecological inference, we need to establish the error introduced according to sensor type and position on the study animal and establish protocols for error assessment and minimization. 2. Using laboratory trials, we examine the absolute accuracy of tri-axial accelerometers and determine how inaccuracies impact measurements of dynamic body acceleration (DBA), as the main acceleration-based proxy for energy expenditure. We then examine how tag type and placement affect the acceleration signal in birds using (i) pigeons Columba livia flying in a wind tunnel, with tags mounted simultaneously in two positions, (ii) back- and tail-mounted tags deployed on wild kittiwakes Rissa tridactyla. Finally, we (iii) present a case study where two generations of tag were deployed using different attachment procedures on red-tailed tropicbirds Phaethon rubricauda foraging in different seasons. 3. Bench tests showed that individual acceleration axes required a two-level correction (representing up to 4.3% of the total value) to eliminate measurement error. This resulted in DBA differences of up to 5% between calibrated and uncalibrated tags for humans walking at different speeds. Device position was associated with greater variation in DBA, with upper- and lower back-mounted tags in pigeons varying by 9%, and tail- and back-mounted tags varying by 13% in kittiwakes. Finally, DBA varied by 25% in tropicbirds between seasons, which may be attributable to tag attachment procedures. 4. Accelerometer accuracy, tag placement, and attachment details critically affect the signal amplitude and thereby the ability of the system to detect biologically meaningful phenomena. We propose a simple method to calibrate accelerometers that should be used prior to deployments and archived with resulting data, suggest a way that researchers can assess accuracy in previously collected data, and caution that variable tag placement and attachment can increase sensor noise and even generate trends that have no biological meaning.

The Impact of Input Rules and Ballot Options on Voting Error: An Experimental Analysis

When election reforms such as Ranked Choice Voting or the Alternative Vote are proposed to replace plurality voting, they offer lengthier instructions, more opportunities for political expression, and more opportunities for mistakes on the ballot. Observational studies of voting error rely on ecological inference from geographically aggregated data. Here we use an experimental approach instead, to examine the effect of two different ballot conditions at the individual level of analysis: the input rules that the voter must use and the number of ballot options presented for the voter’s choice. This experiment randomly assigned three different input rules (single-mark, ranking, and grading) and two different candidate lists (with six and eight candidates) to over 6,000 online respondents in the USA, during the American presidential primary elections in 2020, simulating a single-winner presidential election. With more expressive input rules (ranking and grading), the distinction between minor mistakes and totally invalid votes—a distinction inapplicable to single‐mark ballots (1MB) voting—assumes new importance. Regression analysis indicates that more complicated input rules and more candidates on the ballot did not raise the probability that a voter would cast a void (uncountable) vote, despite raising the probability of at least one violation of voting instructions.

Improving estimates accuracy of voter transitions. Two new algorithms for ecological inference based on linear programming

The estimation of RxC ecological inference contingency tables from aggregate data defines one of the most salient and challenging problems in the field of quantitative social sciences. From the mathematical programming framework, this paper suggests a new direction for tackling this problem. For the first time in the literature, a procedure based on linear programming is proposed to attain estimates of local contingency tables. Based on this and the homogeneity hypothesis, we suggest two new ecological inference algorithms. These two new algorithms represent an important step forward in the ecological inference mathematical programming literature. In addition to generating estimates for local ecological inference contingency tables and amending the tendency to produce extreme transfer probability estimates previously observed in other mathematical programming procedures, they prove to be quite competitive and more accurate than the current linear programming baseline algorithm. The new algorithms place the linear programming approach once again in a prominent position in the ecological inference toolkit. We use a unique dataset with almost 500 elections, where the real transfer matrices are known, to assess their accuracy. Interested readers can easily use these new algorithms with the aid of the R package lphom.