Synthesis reveals approximately balanced biotic differentiation and homogenization.

It is commonly thought that the biodiversity crisis includes widespread declines in the spatial variation of species composition, called biotic homogenization. Using a typology relating homogenization and differentiation to local and regional diversity changes, we synthesize patterns across 461 metacommunities surveyed for 10 to 91 years, and 64 species checklists (13 to 500+ years). Across all datasets, we found that no change was the most common outcome, but with many instances of homogenization and differentiation. A weak homogenizing trend of a 0.3% increase in species shared among communities/year on average was driven by increased numbers of widespread (high occupancy) species and strongly associated with checklist data that have longer durations and large spatial scales. At smaller spatial and temporal scales, we show that homogenization and differentiation can be driven by changes in the number and spatial distributions of both rare and common species. The multiscale perspective introduced here can help identify scale-dependent drivers underpinning biotic differentiation and homogenization.

Parental care results in a greater mutation load, for which it is also a phenotypic antidote.

Benevolent social behaviours, such as parental care, are thought to enable mildly deleterious mutations to persist. We tested this prediction experimentally using the burying beetle Nicrophorus vespilloides, an insect with biparental care. For 20 generations, we allowed replicate experimental burying beetle populations to evolve either with post-hatching care ('Full Care' populations) or without it ('No Care' populations). We then established new lineages, seeded from these experimental populations, which we inbred to assess their mutation load. Outbred lineages served as controls. We also tested whether the deleterious effects of a greater mutation load could be concealed by parental care by allowing half the lineages to receive post-hatching care, while half did not. We found that inbred lineages from the Full Care populations went extinct more quickly than inbred lineages from the No Care populations-but only when offspring received no post-hatching care. We infer that Full Care lineages carried a greater mutation load, but that the associated deleterious effects on fitness could be overcome if larvae received parental care. We suggest that the increased mutation load caused by parental care increases a population's dependence upon care. This could explain why care is seldom lost once it has evolved.

Regional occupancy increases for widespread species but decreases for narrowly distributed species in metacommunity time series.

While human activities are known to elicit rapid turnover in species composition through time, the properties of the species that increase or decrease their spatial occupancy underlying this turnover are less clear. Here, we used an extensive dataset of 238 metacommunity time series of multiple taxa spread across the globe to evaluate whether species that are more widespread (large-ranged species) differed in how they changed their site occupancy over the 10-90 years the metacommunities were monitored relative to species that are more narrowly distributed (small-ranged species). We found that on average, large-ranged species tended to increase in occupancy through time, whereas small-ranged species tended to decrease. These relationships were stronger in marine than in terrestrial and freshwater realms. However, in terrestrial regions, the directional changes in occupancy were less extreme in protected areas. Our findings provide evidence for systematic decreases in occupancy of small-ranged species, and that habitat protection could mitigate these losses in the face of environmental change.

Local biodiversity change reflects interactions among changing abundance, evenness, and richness.

Biodiversity metrics often integrate data on the presence and abundance of multiple species. Yet our understanding of covariation between changes to the numbers of individuals, the evenness of species relative abundances, and the total number of species remains limited. Using individual-based rarefaction curves, we show how expected positive relationships among changes in abundance, evenness and richness arise, and how they can break down. We then examined interdependencies between changes in abundance, evenness and richness in more than 1100 assemblages sampled either through time or across space. As predicted, richness changes were greatest when abundance and evenness changed in the same direction, and countervailing changes in abundance and evenness acted to constrain the magnitude of changes in species richness. Site-to-site differences in abundance, evenness, and richness were often decoupled, and pairwise relationships between these components across assemblages were weak. In contrast, changes in species richness and relative abundance were strongly correlated for assemblages varying through time. Temporal changes in local biodiversity showed greater inertia and stronger relationships between the component changes when compared to site-to-site variation. Overall, local variation in assemblage diversity was rarely due to repeated passive samples from an approximately static species abundance distribution. Instead, changing species relative abundances often dominated local variation in diversity. Moreover, how changing relative abundances combined with changes to total abundance frequently determined the magnitude of richness changes. Embracing the interdependencies between changing abundance, evenness and richness can provide new information to better understand biodiversity change in the Anthropocene.

Long-term changes in temperate marine fish assemblages are driven by a small subset of species.

The species composition of plant and animal assemblages across the globe has changed substantially over the past century. How do the dynamics of individual species cause this change? We classified species into seven unique categories of temporal dynamics based on the ordered sequence of presences and absences that each species contributes to an assemblage time series. We applied this framework to 14,434 species trajectories comprising 280 assemblages of temperate marine fishes surveyed annually for 20 or more years. Although 90% of the assemblages diverged in species composition from the baseline year, this compositional change was largely driven by only 8% of the species' trajectories. Quantifying the reorganization of assemblages based on species shared temporal dynamics should facilitate the task of monitoring and restoring biodiversity. We suggest ways in which our framework could provide informative measures of compositional change, as well as leverage future research on pattern and process in ecological systems.

Detecting multiple spatial disease clusters: information criterion and scan statistic approach.

BACKGROUND: Detecting the geographical tendency for the presence of a disease or incident is, particularly at an early stage, a key challenge for preventing severe consequences. Given recent rapid advancements in information technologies, it is required a comprehensive framework that enables simultaneous detection of multiple spatial clusters, whether disease cases are randomly scattered or clustered around specific epicenters on a larger scale. We develop a new methodology that detects multiple spatial disease clusters and evaluates its performance compared to existing other methods. METHODS: A novel framework for spatial multiple-cluster detection is developed. The framework directly stands on the integrated bases of scan statistics and generalized linear models, adopting a new information criterion that selects the appropriate number of disease clusters. We evaluated the proposed approach using a real dataset, the hospital admission for chronic obstructive pulmonary disease (COPD) in England, and simulated data, whether the approach tends to select the correct number of clusters. RESULTS: A case study and simulation studies conducted both confirmed that the proposed method performed better compared to conventional cluster detection procedures, in terms of higher sensitivity. CONCLUSIONS: We proposed a new statistical framework that simultaneously detects and evaluates multiple disease clusters in a large study space, with high detection power compared to conventional approaches.

Detecting Changes and Avoiding Catastrophic Forgetting in Dynamic Partially Observable Environments.

The ability of an agent to detect changes in an environment is key to successful adaptation. This ability involves at least two phases: learning a model of an environment, and detecting that a change is likely to have occurred when this model is no longer accurate. This task is particularly challenging in partially observable environments, such as those modeled with partially observable Markov decision processes (POMDPs). Some predictive learners are able to infer the state from observations and thus perform better with partial observability. Predictive state representations (PSRs) and neural networks are two such tools that can be trained to predict the probabilities of future observations. However, most such existing methods focus primarily on static problems in which only one environment is learned. In this paper, we propose an algorithm that uses statistical tests to estimate the probability of different predictive models to fit the current environment. We exploit the underlying probability distributions of predictive models to provide a fast and explainable method to assess and justify the model's beliefs about the current environment. Crucially, by doing so, the method can label incoming data as fitting different models, and thus can continuously train separate models in different environments. This new method is shown to prevent catastrophic forgetting when new environments, or tasks, are encountered. The method can also be of use when AI-informed decisions require justifications because its beliefs are based on statistical evidence from observations. We empirically demonstrate the benefit of the novel method with simulations in a set of POMDP environments.

A balance of winners and losers in the Anthropocene.

Scientists disagree about the nature of biodiversity change. While there is evidence for widespread declines from population surveys, assemblage surveys reveal a mix of declines and increases. These conflicting conclusions may be caused by the use of different metrics: assemblage metrics may average out drastic changes in individual populations. Alternatively, differences may arise from data sources: populations monitored individually, versus whole-assemblage monitoring. To test these hypotheses, we estimated population change metrics using assemblage data. For a set of 23 241 populations, 16 009 species, in 158 assemblages, we detected significantly accelerating extinction and colonisation rates, with both rates being approximately balanced. Most populations (85%) did not show significant trends in abundance, and those that did were balanced between winners (8%) and losers (7%). Thus, population metrics estimated with assemblage data are commensurate with assemblage metrics and reveal sustained and increasing species turnover.

Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models.

The spatial scan statistic is commonly used to detect spatial and/or temporal disease clusters in epidemiological studies. Although multiple clusters in the study space can be thus identified, current theoretical developments are mainly based on detecting a 'single' cluster. The standard scan statistic procedure enables the detection of multiple clusters, recursively identifying additional 'secondary' clusters. However, their p-values are calculated one at a time, as if each cluster is a primary one. Therefore, a new procedure that can accurately evaluate multiple clusters as a whole is needed. The present study focuses on purely temporal cases and then proposes a new test procedure that evaluates the p-value for multiple clusters, combining generalized linear models with an information criterion approach. This framework encompasses the conventional, currently widely used detection procedure as a special case. An application study adopting the new framework is presented, analysing the Japanese daily incidence of out-of-hospital cardiac arrest cases. The analysis reveals that the number of the incident increases around New Year's Day in Japan. Further, simulation studies undertaken confirm that the proposed method possesses a consistency property that tends to select the correct number of clusters when the truth is known.

On species richness and rarefaction: size- and coverage-based techniques quantify different characteristics of richness change in biodiversity.

Changes in biodiversity today shape the future patterns of biodiversity. This fact underlines the importance of understanding changes in biodiversity through time and space. The number of species, known as species richness, has long been studied as a key indicator that quantifies the state of biodiversity, and standardisation techniques, called rarefaction, have also been used to undertake a fair comparison of the richness observed at different times or locations. The present study asks whether utilising different rarefaction techniques attains comparable results when investigating changes in species richness. The study framework presents the statistical nature of two commonly adopted rarefaction techniques: size-based and coverage-based rarefaction. The key finding is that the rarefied richness results calculated by these two different rarefaction methods reflect different aspects of biodiversity change, the shift in community size and/or composition. This fact illuminates that richness analyses based on different rarefaction techniques can reach different conclusions that may be contradictory. The study also investigates the mechanism creating such divergence. As such, special care is required when evaluating biodiversity change using species richness as an indicator.

Divergent biodiversity change within ecosystems.

The Earth's ecosystems are under unprecedented pressure, yet the nature of contemporary biodiversity change is not well understood. Growing evidence that community size is regulated highlights the need for improved understanding of community dynamics. As stability in community size could be underpinned by marked temporal turnover, a key question is the extent to which changes in both biodiversity dimensions (temporal α- and temporal ß-diversity) covary within and among the assemblages that comprise natural communities. Here, we draw on a multiassemblage dataset (encompassing vertebrates, invertebrates, and unicellular plants) from a tropical freshwater ecosystem and employ a cyclic shift randomization to assess whether any directional change in temporal α-diversity and temporal ß-diversity exceeds baseline levels. In the majority of cases, α-diversity remains stable over the 5-y time frame of our analysis, with little evidence for systematic change at the community level. In contrast, temporal ß-diversity changes are more prevalent, and the two diversity dimensions are decoupled at both the within- and among-assemblage level. Consequently, a pressing research challenge is to establish how turnover supports regulation and when elevated temporal ß-diversity jeopardizes community integrity.

Community-level regulation of temporal trends in biodiversity.

Many theoretical models of community dynamics predict that species richness (S) and total abundance (N) are regulated in their temporal fluctuations. We present novel evidence for widespread regulation of biodiversity. For 59 plant and animal assemblages from around the globe monitored annually for a decade or more, the majority exhibited regulated fluctuations compared to the null hypothesis of an unconstrained random walk. However, there was little evidence for statistical artifacts, regulation driven by correlations with average annual temperature, or local-scale compensatory fluctuations in S or N. In the absence of major environmental perturbations, such as urbanization or cropland transformation, species richness and abundance may be buffered and exhibit some resilience in their temporal trajectories. These results suggest that regulatory processes are occurring despite unprecedented environmental change, highlighting the need for community-level assessment of biodiversity trends, as well as extensions of existing theory to address open source pools and shifting environmental conditions.

Estimates of local biodiversity change over time stand up to scrutiny.

We present new data and analyses revealing fundamental flaws in a critique of two recent meta-analyses of local-scale temporal biodiversity change. First, the conclusion that short-term time series lead to biased estimates of long-term change was based on two errors in the simulations used to support it. Second, the conclusion of negative relationships between temporal biodiversity change and study duration was entirely dependent on unrealistic model assumptions, the use of a subset of data, and inclusion of one outlier data point in one study. Third, the finding of a decline in local biodiversity, after eliminating post-disturbance studies, is not robust to alternative analyses on the original data set, and is absent in a larger, updated data set. Finally, the undebatable point, noted in both original papers, that studies in the ecological literature are geographically biased, was used to cast doubt on the conclusion that, outside of areas converted to croplands or asphalt, the distribution of biodiversity trends is centered approximately on zero. Future studies may modify conclusions, but at present, alternative conclusions based on the geographic-bias argument rely on speculation. In sum, the critique raises points of uncertainty typical of all ecological studies, but does not provide an evidence-based alternative interpretation.

The daily incidence of out-of-hospital cardiac arrest unexpectedly increases around New Year's Day in Japan.

BACKGROUND: Over 100,000 patients are diagnosed every year as out-of-hospital cardiac arrest (OHCA) cases in Japan and their number has continued to rise for the last decade, presenting a challenge for preventive public health research as well as emergency medical care. The purpose of this study was to identify whether there are any temporal patterns in daily OHCA presentations in Japan. METHODS: Records of OHCA patients (n=701,651) transported by ambulance over the course of six years (1st January 2005 to 10th March 2011) in Japan were obtained from the All-Japan Utstein registry data of cardiopulmonary arrest patients. Time periods within which the incidence of OHCA significantly increased were identified by a temporal cluster detection test using scan statistics. The risk ratios of OHCA for the detected periods were calculated and adjusted according to a Poisson regression model accounting for effects of other factors. RESULTS: The risk of OHCA significantly rises 1.3-1.6 times around New Year's Day in Japan. CONCLUSIONS: Our analysis revealed the increased daily incidence of OHCA around every New Year's Day in Japan.

Attenuation of species abundance distributions by sampling.

Quantifying biodiversity aspects such as species presence/ absence, richness and abundance is an important challenge to answer scientific and resource management questions. In practice, biodiversity can only be assessed from biological material taken by surveys, a difficult task given limited time and resources. A type of random sampling, or often called sub-sampling, is a commonly used technique to reduce the amount of time and effort for investigating large quantities of biological samples. However, it is not immediately clear how (sub-)sampling affects the estimate of biodiversity aspects from a quantitative perspective. This paper specifies the effect of (sub-)sampling as attenuation of the species abundance distribution (SAD), and articulates how the sampling bias is induced to the SAD by random sampling. The framework presented also reveals some confusion in previous theoretical studies.

Spillover effects of a community-managed marine reserve.

The value of no-take marine reserves as fisheries-management tools is controversial, particularly in high-poverty areas where human populations depend heavily on fish as a source of protein. Spillover, the net export of adult fish, is one mechanism by which no-take marine reserves may have a positive influence on adjacent fisheries. Spillover can contribute to poverty alleviation, although its effect is modulated by the number of fishermen and fishing intensity. In this study, we quantify the effects of a community-managed marine reserve in a high poverty area of Northern Mozambique. For this purpose, underwater visual censuses of reef fish were undertaken at three different times: 3 years before (2003), at the time of establishment (2006) and 6 years after the marine reserve establishment (2012). The survey locations were chosen inside, outside and on the border of the marine reserve. Benthic cover composition was quantified at the same sites in 2006 and 2012. After the reserve establishment, fish sizes were also estimated. Regression tree models show that the distance from the border and the time after reserve establishment were the variables with the strongest effect on fish abundance. The extent and direction of the spillover depends on trophic group and fish size. Poisson Generalized Linear Models show that, prior to the reserve establishment, the survey sites did not differ but, after 6 years, the abundance of all fish inside the reserve has increased and caused spillover of herbivorous fish. Spillover was detected 1 km beyond the limit of the reserve for small herbivorous fishes. Six years after the establishment of a community-managed reserve, the fish assemblages have changed dramatically inside the reserve, and spillover is benefitting fish assemblages outside the reserve.

From species to communities: the signature of recreational use on a tropical river ecosystem.

Disturbance can impact natural communities in multiple ways. However, there has been a tendency to focus on single indicators of change when examining the effects of disturbance. This is problematic as classical diversity measures, such as Shannon and Simpson indices, do not always detect the effects of disturbance. Here, we instead take a multilevel, hierarchical approach, looking for signatures of disturbance in the capacity and diversity of the community, and also in allocation and demography at the population level. Using recreational use as an example of disturbance, and the freshwater streams of Trinidad as a model ecosystem, we repeatedly sampled the fish communities and physical parameters of eight pairs of recreational and nonrecreational sites every 3 months over a 28-month period. We also chose the Trinidadian guppy (Poecilia reticulata) as the subject of our population-level analyses. Regression tree analysis, together with analysis of deviance, revealed that community capacity and community species richness were greater at sites with higher levels of recreational use. Interestingly, measures of community diversity that took into account the proportional abundance of each species were not significantly associated with recreational use. Neither did we find any direct association between recreational use and proportion of guppy biomass in the community. However, population-level differences were detected in the guppy: Sex ratio was significantly more female-biased at more disturbed sites. Our findings emphasize the importance of considering multiple levels when asking how disturbance impacts a community. We advocate the use of a multilevel approach when monitoring the effects of disturbance, and highlight gaps in our knowledge when it comes to interpreting these effects.

Assemblage time series reveal biodiversity change but not systematic loss.

The extent to which biodiversity change in local assemblages contributes to global biodiversity loss is poorly understood. We analyzed 100 time series from biomes across Earth to ask how diversity within assemblages is changing through time. We quantified patterns of temporal α diversity, measured as change in local diversity, and temporal ß diversity, measured as change in community composition. Contrary to our expectations, we did not detect systematic loss of α diversity. However, community composition changed systematically through time, in excess of predictions from null models. Heterogeneous rates of environmental change, species range shifts associated with climate change, and biotic homogenization may explain the different patterns of temporal α and ß diversity. Monitoring and understanding change in species composition should be a conservation priority.

Modelling temperature effects on milk production: a study on Holstein cows at a Japanese farm.

Milk yield and its composition vary according to individual cows as well as to a variety of different environment conditions, such as temperature. Previous studies suggest that heat exerts considerable negative effects on milk production and its composition, especially during summer months. We investigate the production and fat composition of milk from individual dairy cows and develop a modelling framework that investigates the effect of temperature by extending a traditional lactation curve model onto a more flexible statistical modelling framework, a generalised additive model (GAM). The GAM simultaneously copes with multiple different conditions (temperature, parity, days of lactation, etc.), and, importantly, their non-linear relationships. Our analysis of retrospective data suggests that individual cows respond differently to heat; cows producing relatively high quantities of milk tend to be particularly sensitive to heat. Our model also suggests that most dairy cows studied fall into three distinct cases that underpin the variation of the milk fat ratio by different mechanisms.

Diversity is maintained by seasonal variation in species abundance.

BACKGROUND: Some of the most marked temporal fluctuations in species abundances are linked to seasons. In theory, multispecies assemblages can persist if species use shared resources at different times, thereby minimizing interspecific competition. However, there is scant empirical evidence supporting these predictions and, to the best of our knowledge, seasonal variation has never been explored in the context of fluctuation-mediated coexistence. RESULTS: Using an exceptionally well-documented estuarine fish assemblage, sampled monthly for over 30 years, we show that temporal shifts in species abundances underpin species coexistence. Species fall into distinct seasonal groups, within which spatial resource use is more heterogeneous than would be expected by chance at those times when competition for food is most intense. We also detect seasonal variation in the richness and evenness of the community, again linked to shifts in resource availability. CONCLUSIONS: These results reveal that spatiotemporal shifts in community composition minimize competitive interactions and help stabilize total abundance.