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Aim Many management programs that are based on the needs of rare or threatened species are ineffective because they fail to collect enough data to reliably estimate abundance and map distributions for their target species. Information that does exist for rare species is often based on presence‐only data, because it is difficult to collect sufficient data on abundance for such species. We targeted 10 rare bird species that were excluded from a recent study due to insufficient data. For these species, we aimed to (a) collect sufficient abundance data, (b) identify important locations and (c) estimate population sizes. Location A large reserve system (~1 M‐ha) in south‐eastern Australia. Methods We undertook intensive field surveys, using repeat area searches of 660 independent 25‐ha sites, totalling 2640 hours of surveys (2‐h surveys; two surveys per site). We used N‐mixture models to estimate abundance whilst accounting for imperfect detection. Results This survey effort returned enough high‐quality data on nine rare bird species to identify important locations and estimate their population sizes. To illustrate potential applications of mapped important locations, we used our results to assess the likely impact of a planned burn program in part of the study region. We identified planned burns that are likely to have a significant impact on important locations for rare species that may not have otherwise been identified. Populations were generally larger than previously estimated using expert opinion. For example, our population estimate for the threatened Red‐lored Whistler (Pachycephala rufogularis) was ~16 times larger than the previous estimate. Main Conclusions Our results show (a) the benefits of using abundance to identify important locations for rare species, (b) the value of developing bespoke survey methods for estimating abundance of rare species with low detectability and (c) a pathway for the application of mapped important locations in conservation land management.

Data from environmental DNA (eDNA) may revolutionize environmental monitoring and management, providing increased detection sensitivity at reduced cost and survey effort. However, eDNA data are rarely used in decision-making contexts, mainly due to uncertainty around (1) data interpretation and (2) whether and how molecular tools dovetail with existing management efforts. We address these challenges by jointly modeling eDNA detection via qPCR and traditional trap data to estimate the density of invasive European green crab (Carcinus maenas), a species for which, historically, baited traps have been used for both detection and control. Our analytical framework simultaneously quantifies uncertainty in both detection methods and provides a robust way of integrating different data streams into management processes. Moreover, the joint model makes clear the marginal information benefit of adding eDNA (or any other) additional data type to an existing monitoring program, offering a path to optimizing sampling efforts for species of management interest. Here, we document green crab eDNA beyond the previously known invasion front and find that the value of eDNA data dramatically increases with low population densities and low traditional sampling effort, as is often the case at leading-edge locations. We also highlight the detection limits of the molecular assay used in this study, as well as scenarios under which eDNA sampling is unlikely to improve existing management efforts.

Gaussian mixture models (GMMs) are a popular and versatile tool for exploring heterogeneity in multivariate continuous data. Arguably the most popular way to estimate GMMs is via the expectation–maximization (EM) algorithm combined with model selection using the Bayesian information criterion (BIC). If the GMM is correctly specified, this estimation procedure has been demonstrated to have high recovery performance. However, in many situations, the data are not continuous but ordinal, for example when assessing symptom severity in medical data or modeling the responses in a survey. For such situations, it is unknown how well the EM algorithm and the BIC perform in GMM recovery. In the present paper, we investigate this question by simulating data from various GMMs, thresholding them in ordinal categories and evaluating recovery performance. We show that the number of components can be estimated reliably if the number of ordinal categories and the number of variables is high enough. However, the estimates of the parameters of the component models are biased independent of sample size. Finally, we discuss alternative modeling approaches which might be adopted for the situations in which estimating a GMM is not acceptable.

Trajectory modelling techniques have been developed to determine subgroups within a given population and are increasingly used to better understand intra- and inter-individual variability in health outcome patterns over time. The objectives of this narrative review are to explore various trajectory modelling approaches useful to epidemiological research and give an overview of their applications and differences. Guidance for reporting on the results of trajectory modelling is also covered. Trajectory modelling techniques reviewed include latent class modelling approaches, ie, growth mixture modelling (GMM), group-based trajectory modelling (GBTM), latent class analysis (LCA), and latent transition analysis (LTA). A parallel is drawn to other individual-centered statistical approaches such as cluster analysis (CA) and sequence analysis (SA). Depending on the research question and type of data, a number of approaches can be used for trajectory modelling of health outcomes measured in longitudinal studies. However, the various terms to designate latent class modelling approaches (GMM, GBTM, LTA, LCA) are used inconsistently and often interchangeably in the available scientific literature. Improved consistency in the terminology and reporting guidelines have the potential to increase researchers' efficiency when it comes to choosing the most appropriate technique that best suits their research questions.

Abundance of mule deer (Odocoileus hemionus) in western North America is often considered lower than desirable for hunting. Some coastal populations of Columbian black-tailed deer (O. h. columbianus) in California, USA, near urban development, however, are perceived as a nuisance and may be overabundant. To determine the density of a potential nuisance population in Marin County, California, we used a combination of fecal DNA surveys, camera stations, and 2 sources of ancillary data on wildlife observations. We estimated an average density of 18.3 deer/km² (90% CI=15.8–20.7) throughout Marin County during late summer and early fall, 2015 and 2016. Within the county, areas with intermediate human density (885 people/km², 90% CI=125–1,646) were associated with the highest deer densities (25–44/km²). Our estimate of average deer density was 1.7–6.1 times higher than published density estimates for deer from elsewhere in California and on the low end of densities reported for mule and white-tailed (O. virginianus) deer in regions where they routinely cause a nuisance to humans. High black-tailed deer densities in Marin County may be partially attributed to a paucity of large predators, but more investigation is warranted to evaluate the effects of a recent increase in coyotes (Canis latrans) on the deer population. Analyses of highway road kill rates and citizen science surveys suggest that the deer population in Marin County has been stable over the past 10 years. Our results demonstrate how robust estimation of deer density can inform human–wildlife conflict issues, not just managed hunting.

In statistics, tools have been developed to estimate individual change over time. Also, the existence of latent trajectories, where individuals are captured by trajectories that are unobserved (latent), can be evaluated (Muthén & Muthén, 2000 ). The method used to evaluate such trajectories is called Latent Growth Mixture Modeling (LGMM) or Latent Class Growth Modeling (LCGA). The difference between the two models is whether variance within latent classes is allowed for (Jung & Wickrama, 2008 ). The default approach most often used when estimating such models begins with estimating a single cluster model, where only a single underlying group is presumed. Next, several additional models are estimated with an increasing number of clusters (latent groups or classes). For each of these models, the software is allowed to estimate all parameters without any restrictions. A final model is chosen based on model comparison tools, for example, using the BIC, the bootstrapped chi-square test, or the Lo-Mendell-Rubin test. To ease the use of LGMM/LCGA step by step in this symposium (Van de Schoot, 2015 ) guidelines are presented which can be used for researchers applying the methods to longitudinal data, for example, the development of posttraumatic stress disorder (PTSD) after trauma (Depaoli, van de Schoot, van Loey, & Sijbrandij, 2015 ; Galatzer-Levy, 2015 ). The guidelines include how to use the software Mplus (Muthén & Muthén, 1998 -2012) to run the set of models needed to answer the research question: how many latent classes exist in the data? The next step described in the guidelines is how to add covariates/predictors to predict class membership using the three-step approach (Vermunt, 2010 ). Lastly, it described what essentials to report in the paper. When applying LGMM/LCGA models for the first time, the guidelines presented can be used to guide what models to run and what to report.

Quantitative estimates of the abundance of surface materials and of water ice particle grain sizes at five widely separated locations on the surface of Europa have been obtained by two independent methods in order to search for possible discrepancies that may be attributed to differences in the methods employed. Results of radiative transfer (RT) compositional modeling (also known as intimate mixture modeling) from two prior studies are here employed without modification. Areal (or “checkerboard”) mixture modeling, also known as linear mixture (LM) modeling, was performed to allow direct comparisons. The failure to model scattering processes (whose effects may be strongly nonlinear) in the LM approach is recognized as a potential source of errors. RT modeling accounts for nonlinear spectral responses due to scattering but is subject to other uncertainties. By comparing abundance estimates for H2SO4 · nH2O and water ice, obtained through both methods as applied to identical spectra, we may gain some insight into the importance of “volume scattering” effects for investigations of Europa's surface composition. We find that both methods return similar abundances for each location analyzed; linear correlation coefficients of ≥ 0.98 are found between the derived H2SO4 · nH2O and water ice abundances returned by both methods. We thus find no evidence of a significant influence of volume scattering on the compositional solutions obtained by LM modeling for these locations. Some differences in the results obtained for water ice grain sizes are attributed to the limited selection of candidate materials allowed in the RT investigations. Plain Language Summary Estimates of the surface composition of Europa, one of Jupiter's moons, derived from remote sensing observations are obtained by radiative transfer modeling and linear mixture modeling. We compare and contrast the results and assess the advantages and disadvantages of each method. The solutions obtained by the two methods for Europa are consistently in very good agreement, which is unlike the case for Mars and for some other outer solar system satellites Key Points Surface compositions for Europa obtained by intimate mixture modeling are compared with compositional results from linear mixture modeling No evidence of a nonlinear spectral response due to volume scattering in intimate mixtures is detected for the locations analyzed Compositional results obtained by the two methods are in generally good agreement

Background Research has suggested the positive impact of physical activity on health and wellbeing in older age, yet few studies have investigated the associations between physical activity and heterogeneous trajectories of healthy ageing. We aimed to identify how physical activity can influence healthy ageing trajectories using a harmonised dataset of eight ageing cohorts across the world. Methods Based on a harmonised dataset of eight ageing cohorts in Australia, USA, Mexico, Japan, South Korea, and Europe, comprising 130,521 older adults ( M age  = 62.81, SD age  = 10.06) followed-up up to 10 years ( M follow-up  = 5.47, SD follow-up  = 3.22) , we employed growth mixture modelling to identify latent classes of people with different trajectories of healthy ageing scores, which incorporated 41 items of health and functioning. Multinomial logistic regression modelling was used to investigate the associations between physical activity and different types of trajectories adjusting for sociodemographic characteristics and other lifestyle behaviours. Results Three latent classes of healthy ageing trajectories were identified: two with stable trajectories with high (71.4%) or low (25.2%) starting points and one with a high starting point but a fast decline over time (3.4%). Engagement in any level of physical activity was associated with decreased odds of being in the low stable (OR: 0.18; 95% CI: 0.17, 0.19) and fast decline trajectories groups (OR: 0.44; 95% CI: 0.39, 0.50) compared to the high stable trajectory group. These results were replicated with alternative physical activity operationalisations, as well as in sensitivity analyses using reduced samples. Conclusions Our findings suggest a positive impact of physical activity on healthy ageing, attenuating declines in health and functioning. Physical activity promotion should be a key focus of healthy ageing policies to prevent disability and fast deterioration in health.

A central problem in infection biology is understanding why two individuals exposed to identical infections have different outcomes. We have developed an experimental model where genetically identical, co-housed Drosophila given identical systemic infections experience different outcomes, with some individuals succumbing to acute infection while others control the pathogen as an asymptomatic persistent infection. We found that differences in bacterial burden at the time of death did not explain the two outcomes of infection. Inter-individual variation in survival stems from variation in within-host bacterial growth, which is determined by the immune response. We developed a model that captures bacterial growth dynamics and identifies key factors that predict the infection outcome: the rate of bacterial proliferation and the time required for the host to establish an effective immunological control. Our results provide a framework for studying the individual host-pathogen parameters governing the progression of infection and lead ultimately to life or death. Sick individuals do not all respond to an infection in the same way. One individual may experience mild symptoms and recover easily, while another may suffer devastating illness or even death. A number of factors are often assumed to account for these differences, including the sex, age and genes of the individuals, and differences in the environments the individuals have been exposed to. However, random variations in how an individual’s immune system interacts with the infection could also play an important role in recovery. Duneau et al. have now studied how genetically identical fruit flies who were raised in the same environment respond to different bacterial infections. This enabled them to develop a mathematical model that describes how a bacterial infection develops in an individual. In an initial phase, the bacteria proliferate freely. If the immune defenses activate in time to control the infection, the number of bacteria in the fly decreases to a constant level and the infection enters a long-term, or chronic, phase. The sooner this happens the more likely it is that the fly will survive. If the immune control happens too late, the infection enters a terminal phase and the fly will die once the number of bacteria increases to a certain level. The model therefore reveals that the precise time at which the immune system takes control of the bacterial population – termed the “Time to Control” – determines the outcome of the infection. Duneau et al. confirmed this by injecting bacteria into identical flies. A small variation in the Time to Control was sometimes the difference between a fly living or dying. Understanding what controls this apparently random variation is key to understanding infection and potentially developing more efficient treatments for a wide range of diseases – not just those caused by bacteria, but also those caused by viruses and parasites, like HIV and malaria.

Gaussian Mixture Model (GMM)/Gaussian Mixture Regression (GMR) is a paramount technology of learning from demonstrations to perform human–robotic collaboration. However, GMM/GMR is ineffective in supporting dynamic manufacturing where random obstacles in the applications generate potential safety concerns. In this paper, an improved GMM/GMR-based approach for collaborative robots (cobots) path planning is designed to achieve adaptive obstacle avoidance in dynamic manufacturing. The approach is realised via three innovative steps: (i) new quality assessment criteria for a cobot’s paths produced by GMM/GMR are defined; (ii) based on the criteria, demonstrations and parameters of GMM/GMR are adaptively amended to eliminate collisions and safety issues between a cobot and obstacles; (iii) a fruit fly optimisation algorithm is incorporated into GMM/GMR to expedite the computational efficiency. Case studies with different complexities are used for approach validation in terms of feature retention from demonstrations, regression path smoothness and obstacle avoidance effectiveness. Results of the case studies and benchmarking analyses show that the approach is robust and efficient for dynamic manufacturing applications.