Albatrosses employ orientation and routing strategies similar to yacht racers.

The way goal-oriented birds adjust their travel direction and route in response to wind significantly affects their travel costs. This is expected to be particularly pronounced in pelagic seabirds, which utilize a wind-dependent flight style called dynamic soaring. Dynamic soaring seabirds in situations without a definite goal, e.g. searching for prey, are known to preferentially fly with crosswinds or quartering-tailwinds to increase the speed and search area, and reduce travel costs. However, little is known about their reaction to wind when heading to a definite goal, such as homing. Homing tracks of wandering albatrosses (Diomedea exulans) vary from beelines to zigzags, which are similar to those of sailboats. Here, given that both albatrosses and sailboats travel slower in headwinds and tailwinds, we tested whether the time-minimizing strategies used by yacht racers can be compared to the locomotion patterns of wandering albatrosses. We predicted that when the goal is located upwind or downwind, albatrosses should deviate their travel directions from the goal on the mesoscale and increase the number of turns on the macroscale. Both hypotheses were supported by track data from albatrosses and racing yachts in the Southern Ocean confirming that albatrosses qualitatively employ the same strategy as yacht racers. Nevertheless, albatrosses did not strictly minimize their travel time, likely making their flight robust against wind fluctuations to reduce flight costs. Our study provides empirical evidence of tacking in albatrosses and demonstrates that man-made movement strategies provide a new perspective on the laws underlying wildlife movement.

Biodiversity modeling advances will improve predictions of nature's contributions to people.

Accurate predictions of ecosystem functions and nature's contributions to people (NCP) are needed to prioritize environmental protection and restoration in the Anthropocene. However, our ability to predict NCP is undermined by approaches that rely on biophysical variables and ignore those describing biodiversity, which have strong links to NCP. To foster predictive mapping of NCP, we should harness the latest methods in biodiversity modeling. This field advances rapidly, and new techniques with promising applications for predicting NCP are still underutilized. Here, we argue that employing recent advances in biodiversity modeling can enhance the accuracy and scope of NCP maps and predictions. This enhancement will contribute significantly to the achievement of global objectives to preserve NCP, for both the present and an unpredictable future.

Inferring causal impacts of extreme water-level drawdowns on lake water clarity using long-term monitoring data.

Although long-term ecosystem monitoring provides essential knowledge for practicing ecosystem management, analyses of the causal effects of ecological impacts from large-scale observational data are still in an early stage of development. We used causal impact analysis (CIA)-a synthetic control method that enables estimation of causal impacts from unrepeated, long-term observational data-to evaluate the causal impacts of extreme water-level drawdowns during summer on subsequent water quality. We used more than 100 years of transparency and water level monitoring data from Lake Biwa, Japan. The results of the CIA showed that the most extreme drawdown in recorded history, which occurred in 1994, had a significant positive effect on transparency (a maximum increase of 1.75 m on average over the following year) in the north basin of the lake. The extreme drawdown in 1939 was also shown to be a trigger for an increase in transparency in the north basin, whereas that in 1984 had no significant effects on transparency. In the south basin, contrary to the pattern in the north basin, the extreme drawdown had a significant negative effect on transparency shortly after the extreme drawdown. These different impacts of the extreme drawdowns were considered to be affected by the timing and magnitude of the extreme drawdowns and the depths of the basins. Our approach of inferring the causal impacts of past events on ecosystems will be helpful in implementing water-level management for ecosystem management and improving water quality in lakes.

Estimating fish population abundance by integrating quantitative data on environmental DNA and hydrodynamic modelling.

Molecular analysis of DNA left in the environment, known as environmental DNA (eDNA), has proven to be a powerful and cost-effective approach to infer occurrence of species. Nonetheless, relating measurements of eDNA concentration to population abundance remains difficult because detailed knowledge on the processes that govern spatial and temporal distribution of eDNA should be integrated to reconstruct the underlying distribution and abundance of a target species. In this study, we propose a general framework of abundance estimation for aquatic systems on the basis of spatially replicated measurements of eDNA. The proposed method explicitly accounts for production, transport and degradation of eDNA by utilizing numerical hydrodynamic models that can simulate the distribution of eDNA concentrations within an aquatic area. It turns out that, under certain assumptions, population abundance can be estimated via a Bayesian inference of a generalized linear model. Application to a Japanese jack mackerel (Trachurus japonicus) population in Maizuru Bay revealed that the proposed method gives an estimate of population abundance comparable to that of a quantitative echo sounder method. Furthermore, the method successfully identified a source of exogenous input of eDNA (a fish market), which may render a quantitative application of eDNA difficult to interpret unless its effect is taken into account. These findings indicate the ability of eDNA to reliably reflect population abundance of aquatic macroorganisms; when the "ecology of eDNA" is adequately accounted for, population abundance can be quantified on the basis of measurements of eDNA concentration.

Integrating multiple sources of ecological data to unveil macroscale species abundance.

The pattern of species abundance, represented by the number of individuals per species within an ecological community, is one of the fundamental characteristics of biodiversity. However, despite their obvious significance in ecology and biogeography, there is still no clear understanding of these patterns at large spatial scales. Here, we develop a hierarchical modelling approach to estimate macroscale patterns of species abundance. Using this approach, estimates of absolute abundance of 1248 woody plant species at a 10-km-grid-square resolution over East Asian islands across subtropical to temperate biomes are obtained. We provide two examples of the basic and applied use of the estimated species abundance for (1) inference of macroevolutionary processes underpinning regional biodiversity patterns and (2) quantitative community-wide assessment of a national red list. These results highlight the potential of the elucidation of macroscale species abundance that has thus far been an inaccessible but critical property of biodiversity.

Evaluation of detection probabilities at the water-filtering and initial PCR steps in environmental DNA metabarcoding using a multispecies site occupancy model.

Environmental DNA (eDNA) metabarcoding is a recently developed method to assess biodiversity based on a high-throughput parallel DNA sequencing applied to DNA present in the ecosystem. Although eDNA metabarcoding enables a rapid assessment of biodiversity, it is prone to species detection errors that may occur at sequential steps in field sampling, laboratory experiments, and bioinformatics. In this study, we illustrate how the error rates in the eDNA metabarcoding-based species detection can be accounted for by applying the multispecies occupancy modelling framework. We report a case study with the eDNA sample from an aquarium tank in which the detection probabilities of species in the two major steps of eDNA metabarcoding, filtration and PCR, across a range of PCR annealing temperatures, were examined. We also show that the results can be used to examine the efficiency of species detection under a given experimental design and setting, in terms of the efficiency of species detection, highlighting the usefulness of the multispecies site occupancy modelling framework to study the optimum conditions for molecular experiments.

A self-excited threshold autoregressive state-space model for menstrual cycles: Forecasting menstruation and identifying within-cycle stages based on basal body temperature.

The menstrual cycle is divided into hypothermic and hyperthermic phases based on the periodic shift in the basal body temperature (BBT), reflecting events occurring in the ovary. In the present study, we proposed a state-space model that explicitly incorporates the biphasic nature of the menstrual cycle, in which the probability density distributions for the advancement of the menstrual phase and that for the BBT switch depending on a latent state variable. Our model derives the predictive distribution of the day of the next menstruation onset that is adaptively adjusted by accommodating new observations of the BBT sequentially. It also enables us to obtain conditional probabilities of the woman being in the early or late stages of the cycle, which can be used to identify the duration of hypothermic and hyperthermic phases, possibly as well as the day of ovulation. By applying the model to real BBT and menstruation data, we show that the proposed model can properly capture the biphasic characteristics of menstrual cycles, providing a good prediction of the menstruation onset in a wide range of age groups. The application of the proposed model to a large data set containing 25 622 cycles provided by 3533 women further highlighted the between-age differences in the population characteristics of menstrual cycles, suggesting its wide applicability.

The forecasting of menstruation based on a state-space modeling of basal body temperature time series.

Women's basal body temperature (BBT) shows a periodic pattern that associates with menstrual cycle. Although this fact suggests a possibility that daily BBT time series can be useful for estimating the underlying phase state as well as for predicting the length of current menstrual cycle, little attention has been paid to model BBT time series. In this study, we propose a state-space model that involves the menstrual phase as a latent state variable to explain the daily fluctuation of BBT and the menstruation cycle length. Conditional distributions of the phase are obtained by using sequential Bayesian filtering techniques. A predictive distribution of the next menstruation day can be derived based on this conditional distribution and the model, leading to a novel statistical framework that provides a sequentially updated prediction for upcoming menstruation day. We applied this framework to a real data set of women's BBT and menstruation days and compared prediction accuracy of the proposed method with that of previous methods, showing that the proposed method generally provides a better prediction. Because BBT can be obtained with relatively small cost and effort, the proposed method can be useful for women's health management. Potential extensions of this framework as the basis of modeling and predicting events that are associated with the menstrual cycles are discussed. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

Environmental DNA as a 'Snapshot' of Fish Distribution: A Case Study of Japanese Jack Mackerel in Maizuru Bay, Sea of Japan.

Recent studies in streams and ponds have demonstrated that the distribution and biomass of aquatic organisms can be estimated by detection and quantification of environmental DNA (eDNA). In more open systems such as seas, it is not evident whether eDNA can represent the distribution and biomass of aquatic organisms because various environmental factors (e.g., water flow) are expected to affect eDNA distribution and concentration. To test the relationships between the distribution of fish and eDNA, we conducted a grid survey in Maizuru Bay, Sea of Japan, and sampled surface and bottom waters while monitoring biomass of the Japanese jack mackerel (Trachurus japonicus) using echo sounder technology. A linear model showed a high R(2) value (0.665) without outlier data points, and the association between estimated eDNA concentrations from the surface water samples and echo intensity was significantly positive, suggesting that the estimated spatial variation in eDNA concentration can reflect the local biomass of the jack mackerel. We also found that a best-fit model included echo intensity obtained within 10-150 m from water sampling sites, indicating that the estimated eDNA concentration most likely reflects fish biomass within 150 m in the bay. Although eDNA from a wholesale fish market partially affected eDNA concentration, we conclude that eDNA generally provides a 'snapshot' of fish distribution and biomass in a large area. Further studies in which dynamics of eDNA under field conditions (e.g., patterns of release, degradation, and diffusion of eDNA) are taken into account will provide a better estimate of fish distribution and biomass based on eDNA.

Effects of spatial structure of population size on the population dynamics of barnacles across their elevational range.

Explanations for why population dynamics vary across the range of a species reflect two contrasting hypotheses: (i) temporal variability of populations is larger in the centre of the range compared to the margins because overcompensatory density dependence destabilizes population dynamics and (ii) population variability is larger near the margins, where populations are more susceptible to environmental fluctuations. In both of these hypotheses, positions within the range are assumed to affect population variability. In contrast, the fact that population variability is often related to mean population size implies that the spatial structure of the population size within the range of a species may also be a useful predictor of the spatial variation in temporal variability of population size over the range of the species. To explore how population temporal variability varies spatially and the underlying processes responsible for the spatial variation, we focused on the intertidal barnacle Chthamalus dalli and examined differences in its population dynamics along the tidal levels it inhabits. Changes in coverage of barnacle populations were monitored for 10.5 years at 25 plots spanning the elevational range of this species. Data were analysed by fitting a population dynamics model to estimate the effects of density-dependent and density-independent processes on population growth. We also examined the temporal mean-variance relationship of population size with parameters estimated from the population dynamics model. We found that the relative variability of populations tended to increase from the centre of the elevational range towards the margins because of an increase in the magnitude of stochastic fluctuations of growth rates. Thus, our results supported hypothesis (2). We also found that spatial variations in temporal population variability were well characterized by Taylor's power law, the relative population variability being inversely related to the mean population size. Results suggest that understanding the population dynamics of a species over its range may be facilitated by taking the spatial structure of population size into account as well as by considering changes in population processes as a function of position within the range of the species.

Markov models for community dynamics allowing for observation error.

Markov models are dynamic models that characterize transitions among discrete ecological states with transition probability matrices. Such models are widely used to infer community dynamics of sessile organisms because transition probabilities (the elements of transition probability matrices) can be estimated with time series data from "grid sampling," where species occupancy states are assessed at multiple fixed points in a quadrat or transect. These estimates, however, are known to be biased when resampling error exists. In this study, we used the perspective of multistate dynamic occupancy models to develop a new Markov model that is structured hierarchically such that transitions among occupancy states and observation processes are considered explicitly at each fixed point. We show that, by adopting a hierarchical Bayesian approach, our model provides estimates for transition probabilities that are robust to sampling error. We also show that error rate may be estimated without additional data obtained from rapid repeated sampling. Considerations for the analysis for the application to real data set and potential extensions of the proposed model are discussed.

Seasonality in the strength and spatial scale of processes determining intertidal barnacle population growth.

1. Population growth rate is determined by both density-dependent and density-independent processes. In the temperate zone, the strength and spatial scale of these processes are likely to differ seasonally, but such differences have rarely been quantitatively examined. 2. Coverage, the area occupied by organisms, is a measure of resource use in sessile marine populations. Population models used for density-based studies should be able to characterize effectively fluctuations in coverage, but few have tried to apply such models to sessile populations. 3. We observed coverage of the intertidal barnacle Chthamalus challengeri at 20 plots on four shores along the Pacific coast of Japan over 8 years. We then fitted a population model that incorporated both a density-dependent process (strength of density dependence) and density-independent processes (intrinsic growth rate and stochastic fluctuation at different spatial scales) to these data to analyse the seasonal variation of these processes and answer the following two questions: (i) How do the effects of density-dependent and density-independent processes on population growth vary seasonally? (ii) At what spatial scale, regional (tens of kilometres), shore (hundreds of metres), or rock (tens of centimetres), does density-independent stochastic fluctuation most strongly affect population size changes? 4. Barnacle population size tended to decrease in summer, when population dynamics were characterized by a relatively lower intrinsic growth rate, weaker density dependence and stronger stochastic fluctuation. In contrast, population size tended to increase in winter, reflecting a higher intrinsic growth rate, strong density dependence and weak stochastic fluctuation. 5. In summer, population growth rate was strongly affected by regional-scale stochastic fluctuation, whereas in winter it was affected more by rock-scale stochastic fluctuation, suggesting that populations were strongly affected by regional-scale processes in summer but not in winter. 6. These results indicate that seasonally variable density-dependent and density-independent processes determine the population dynamics of C. challengeri. Therefore, to understand fluctuation patterns of populations of this species, seasonality should be taken into account. Moreover, this study demonstrates that population models commonly used for density-based studies are also applicable to coverage-based population studies.