A generalised abundance index for seasonal invertebrates

At a time of climate change and major loss of biodiversity, it is important to have efficient tools for monitoring populations. In this context, animal abundance indices play an important role. In producing indices for invertebrates, it is important to account for variation in counts within seasons. Two new methods for describing seasonal variation in invertebrate counts have recently been proposed; one is nonparametric, using generalized additive models, and the other is parametric, based on stopover models. We present a novel generalized abundance index which encompasses both parametric and nonparametric approaches. It is extremely efficient to compute this index due to the use of concentrated likelihood techniques. This has particular relevance for the analysis of data from long-term extensive monitoring schemes with records for many species and sites, for which existing modeling techniques can be prohibitively time consuming. Performance of the index is demonstrated by several applications to UK Butterfly Monitoring Scheme data. We demonstrate the potential for new insights into both phenology and spatial variation in seasonal patterns from parametric modeling and the incorporation of covariate dependence, which is relevant for both monitoring and conservation. Associated R code is available on the journal website

Dynamic models for longitudinal butterfly data

There has been recent interest in devising stochastic models for seasonal insects, which respond rapidly to climate change. Fitted to count data, these models are used to construct indices of abundance, which guide conservation and management. We build upon Dennis et al. (2014, under review) to produce dynamic models, which provide succinct descriptions of data from all years simultaneously. They produce estimates of key life-history parameters such as annual productivity and survival. Analyses for univoltine species, with only one generation each year, extend to bivoltine species, with two annual broods. In the latter case we estimate the productivities of each generation separately, and also devise extended indices which indicate the contributions made from different generations. We demonstrate the performance of the models using count data for UK butterfly species, and compare with current procedures which use generalized additive models. We may incor- orate relevant covariates within the model, and illustrate using northing and measures of temperature. Consistent patterns are demonstrated for multiple species. This generates a variety of hypotheses for further investigation, which have the potential to illuminate features of butterfly phenology and demography which are at present poorly understood

Outstanding challenges and future directions for biodiversity monitoring using citizen science data

1. There is increasing availability and use of unstructured and semi-structured citizen science data in biodiversity research and conservation. This expansion of a rich source of ‘big data’ has sparked numerous research directions, driving the development of analytical approaches that account for the complex observation processes in these datasets. 2. We review outstanding challenges in the analysis of citizen science data for biodiversity monitoring. For many of these challenges, the potential impact on ecological inference is unknown. Further research can document the impact and explore ways to address it. In addition to outlining research directions, describing these challenges may be useful in considering the design of future citizen science projects or additions to existing projects. 3. We outline challenges for biodiversity monitoring using citizen science data in four partially overlapping categories: challenges that arise as a result of (a) observer behaviour; (b) data structures; (c) statistical models; and (d) communication. Potential solutions for these challenges are combinations of: (a) collecting additional data or metadata; (b) analytically combining different datasets; and (c) developing or refining statistical models. 4. While there has been important progress to develop methods that tackle most of these challenges, there remain substantial gains in biodiversity monitoring and subsequent conservation actions that we believe will be possible by further research and development in these areas. The degree of challenge and opportunity that each of these presents varies substantially across different datasets, taxa and ecological questions. In some cases, a route forward to address these challenges is clear, while in other cases there is more scope for exploration and creativity

Mesoscale variability in intact and ghost colonies of Phaeocystis antarctica in the Ross Sea : distribution and abundance

© The Author(s), 2016. This is the author's version of the work and is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of Marine Systems 166 (2017): 97-107, doi:10.1016/j.jmarsys.2016.05.007.Phaeocystis, a genus with a cosmopolitan distribution and a polymorphic life cycle, was observed during summer in the Ross Sea, Antarctica, where large blooms of this haptophyte regularly occur. The mesoscale vertical and horizontal distributions of colonies of P. antarctica were assessed using a towed Video Plankton Recorder (VPR). The mean size of colonies was 1.20 mm, and mean abundances within the three VPR surveys were 4.86, 1.96, and 11.5 mL-1. In addition to the typical spherical, transparent colonies, the VPR quantified an optically dissimilar form of colony that had a distinctive translucent appearance. It also measured the abundance of collapsed colonies, similar to those observed previously from cultures and mesocosms, which we called “ghost colonies”. The translucent colonial form had a different distribution than the more common colonial form, and at times was more abundant. Relative to intact colonies, the ghost colonies occurred less frequently, with mean abundances in the three surveys being 0.01, 0.08, and 0.0004 mL-1. Ghost colonies generally were found below the euphotic zone, where they often were in greater abundance than intact colonies. However, the relationship of ghost colonies to intact P. antarctica colonies was not direct or consistent, suggesting that the formation of ghost colonies from living colonies and their appearance within the water column were not tightly coupled. Given their relative scarcity and low carbon content, it is unlikely that ghost colonies contribute substantially to vertical flux; however, it is possible that we did not sample periods of major flux events, and as a result minimized the importance of ghost colonies to vertical flux. They do, however, represent a poorly documented feature of polar haptophyte life cycles.This research was supported by grants from the National Science Foundation (ANT-0944254 and ANT-0944165). HMS and EEP acknowledge support of the Gordon and Betty Moore Foundation (Grant #2649) for image informatics development.2018-06-0

Functional data analysis of multi-species abundance and occupancy data sets

Multi-species indicators are widely used to condense large, complex amounts of information on multiple separate species by forming a single index to inform research, policy and management. Much detail is typically lost when such indices are constructed. Here we investigate the potential of Functional Data Analysis, focussing upon Functional Principal ComponentAnalysis (FPCA), which can be easily carried out using standard R programs, as a tool for displaying features of the underlying information. Illustrations are provided using data from the UK Butterflies for the New Millennium and UK Butterfly Monitoring Scheme databases. The FPCAs conducted result in a huge simplification in terms of dimensional reduction, allowing species occupancy and abundance to be reduced to two and three dimensions, respectively. We show that a functional principal component arises for both occupancy and abundance analyses that distinguishes between species that increase or decrease over time, and that it differs from percentage trend, which is a simplification of complex temporal changes. We find differences in species patterns of occupancy and abundance, providing a warning against routinely combining both types of index within multi-species indicators, for example when using occupancy as a proxy for abundance when sufficient abundance data are not available. By identifying the differences between species, figures displaying functional principal component scores are much more informative than the simple bar plots of percentages of significant trends that often accompany multi-species indicators. Informed by the outcomes of the FPCA, we make recommendations for accompanying visualisations for multi-species indicators, and discuss how these are likely to be context and audience specific. We show that, in the absence of FPCA, using mean species occupancy and total abundance can provide additional, accessible information to complement species-level trends. At the simplest level, we suggest using jitter plots to display variation in species-level trends. We recommend the routine augmentation of multi-species indicators in the future with additional statistical procedures and figures, to serve as an aid to improve communication and understanding of biodiversity metrics, as well as reveal potentially hidden patterns of behaviourand guide additional directions for investigation

Fast Bayesian inference for large occupancy datasets

In recent years, the study of species’ occurrence has benefited from the increased availability of large-scale citizen-science data. Whilst abundance data from standardized monitoring schemes are biased towards well-studied taxa and locations, opportunistic data are available for many taxonomic groups, from a large number of locations and across long timescales. Hence, these data provide opportunities to measure species’ changes in occurrence, particularly through the use of occupancy models, which account for imperfect detection. These opportunistic datasets can be substantially large, numbering hundreds of thousands of sites, and hence present a challenge from a computational perspective, especially within a Bayesian framework. In this paper, we develop a unifying framework for Bayesian inference in occupancy models that account for both spatial and temporal autocorrelation. We make use of the P´olyaGamma scheme, which allows for fast inference, and incorporate spatio-temporal random effects using Gaussian processes (GPs), for which we consider two efficient approximations: Subset of Regressors and Nearest neighbour GPs. We apply our model to data on two UK butterfly species, one common and widespread and one rare, using records from the Butterflies for the New Millennium database, producing occupancy indices spanning 45 years. Our framework can be applied to a wide range of taxa, providing measures of variation in species’ occurrence, which are used to assess biodiversity change

A generic method for estimating and smoothing multispecies biodiversity indices using intermittent data

Biodiversity indicators summarise extensive, complex ecological data sets and are important in influencing government policy. Component data consist of time-varying indices for each of a number of different species. However, current biodiversity indicators suffer from multiple statistical shortcomings. We describe a state-space formulation for new multispecies biodiversity indicators, based on rates of change in the abundance or occupancy probability of the contributing individual species. The formulation is flexible and applicable to different taxa. It possesses several advantages, including the ability to accommodate the sporadic unavailability of data, incorporate variation in the estimation precision of the individual species’ indices when appropriate, and allow the direct incorporation of smoothing over time. Furthermore, model fitting is straightforward in Bayesian and classical implementations, the latter adopting either efficient Hidden Markov modelling or the Kalman filter. Conveniently, the same algorithms can be adopted for cases based on abundance or occupancy data—only the subsequent interpretation differs. The procedure removes the need for bootstrapping which can be prohibitive. We recommend which of two alternatives to use when taxa are fully or partially sampled. The performance of the new approach is demonstrated on simulated data, and through application to three diverse national UK data sets on butterflies, bats and dragonflies. We see that uncritical incorporation of index standard errors should be avoided

Opinions of citizen scientists on open access to UK butterfly and moth occurrence data

Citizen science plays an increasingly important role in biodiversity research and conservation, enabling large volumes of data to be gathered across extensive spatial scales in a cost-effective manner. Open access increases the utility of such data, informing land-use decisions that may affect species persistence, enhancing transparency and encouraging proliferation of research applications. However, open access provision of recent, fine-scale spatial information on the locations of species may also prompt legitimate concerns among contributors regarding possible unintended negative conservation impacts, violations of privacy and commercial exploitation of volunteer-gathered data. Here we canvas the attitudes towards open access of contributors (104 regional co-ordinators and 510 recorders) of species occurrence records to two of the largest citizen science biodiversity recording schemes, the UK’s Butterflies for the New Millennium project and National Moth Recording Scheme. We find that while the majority of participants expressed support for open access in principle, most were more cautious in practice, preferring to limit the spatial resolution of records, particularly of threatened species, and restrict commercial reuse of data. In addition, citizen scientists’ opinions differed between UK countries, taxonomic groups and the level of involvement volunteers had in the schemes. In order to maintain successful and democratic citizen science schemes, organisers, funders and data users must understand and respect participants’ expectations and aspirations regarding open data while seeking to optimise data use for scientific and societal benefits

Sturgeon in the Sacramento–San Joaquin Watershed: New Insights to Support Conservation and Management

The goal of a day-long symposium on March 3, 2015, Sturgeon in the Sacramento–San Joaquin Watershed: New Insights to Support Conservation and Management, was to present new information about the physiology, behavior, and ecology of the green (Acipenser medirostris) and white sturgeon (Acipenser transmontanus) to help guide enhanced management and conservation efforts within the Sacramento–San Joaquin watershed. This symposium identified current unknowns and highlighted new electronic tracking technologies and physiological techniques to address these knowledge gaps. A number of presentations, each reviewing ongoing research on the two species, was followed by a round-table discussion, in which each of the participants was asked to share recommendations for future research on sturgeon in the watershed. This article presents an in-depth review of the scientific information presented at the symposium with a summary of recommendations for future research

Targeted proteomic quantitation of NRF2 signaling and predictive biomarkers in HNSCC

The NFE2L2 (NRF2) oncogene and transcription factor drives a gene expression program that promotes cancer progression, metabolic reprogramming, immune evasion, and chemoradiation resistance. Patient stratification by NRF2 activity may guide treatment decisions to improve outcome. Here, we developed a mass spectrometry-based targeted proteomics assay based on internal standard-triggered parallel reaction monitoring to quantify 69 NRF2 pathway components and targets, as well as 21 proteins of broad clinical significance in head and neck squamous cell carcinoma (HNSCC). We improved an existing internal standard-triggered parallel reaction monitoring acquisition algorithm, called SureQuant, to increase throughput, sensitivity, and precision. Testing the optimized platform on 27 lung and upper aerodigestive cancer cell models revealed 35 NRF2 responsive proteins. In formalin-fixed paraffin-embedded HNSCCs, NRF2 signaling intensity positively correlated with NRF2-activating mutations and with SOX2 protein expression. Protein markers of T-cell infiltration correlated positively with one another and with human papilloma virus infection status. CDKN2A (p16) protein expression positively correlated with the human papilloma virus oncogenic E7 protein and confirmed the presence of translationally active virus. This work establishes a clinically actionable HNSCC protein biomarker assay capable of quantifying over 600 peptides from frozen or formalin-fixed paraffin-embedded archived tissues in under 90 min