Data Integration for Large-Scale Models of Species Distributions.

With the expansion in the quantity and types of biodiversity data being collected, there is a need to find ways to combine these different sources to provide cohesive summaries of species' potential and realized distributions in space and time. Recently, model-based data integration has emerged as a means to achieve this by combining datasets in ways that retain the strengths of each. We describe a flexible approach to data integration using point process models, which provide a convenient way to translate across ecological currencies. We highlight recent examples of large-scale ecological models based on data integration and outline the conceptual and technical challenges and opportunities that arise.

Estimating age-dependent survival from age-aggregated ringing data-extending the use of historical records.

Bird ring-recovery data have been widely used to estimate demographic parameters such as survival probabilities since the mid-20th century. However, while the total number of birds ringed each year is usually known, historical information on age at ringing is often not available. A standard ring-recovery model, for which information on age at ringing is required, cannot be used when historical data are incomplete. We develop a new model to estimate age-dependent survival probabilities from such historical data when age at ringing is not recorded; we call this the historical data model. This new model provides an extension to the model of Robinson, 2010, Ibis, 152, 651-795 by estimating the proportion of the ringed birds marked as juveniles as an additional parameter. We conduct a simulation study to examine the performance of the historical data model and compare it with other models including the standard and conditional ring-recovery models. Simulation studies show that the approach of Robinson, 2010, Ibis, 152, 651-795 can cause bias in parameter estimates. In contrast, the historical data model yields similar parameter estimates to the standard model. Parameter redundancy results show that the newly developed historical data model is comparable to the standard ring-recovery model, in terms of which parameters can be estimated, and has fewer identifiability issues than the conditional model. We illustrate the new proposed model using Blackbird and Sandwich Tern data. The new historical data model allows us to make full use of historical data and estimate the same parameters as the standard model with incomplete data, and in doing so, detect potential changes in demographic parameters further back in time.

Neonicotinoid residues in UK honey despite European Union moratorium.

Due to concerns over negative impacts on insect pollinators, the European Union has implemented a moratorium on the use of three neonicotinoid pesticide seed dressings for mass-flowering crops. We assessed the effectiveness of this policy in reducing the exposure risk to honeybees by collecting 130 samples of honey from bee keepers across the UK before (2014: N = 21) and after the moratorium was in effect (2015: N = 109). Neonicotinoids were present in about half of the honey samples taken before the moratorium, and they were present in over a fifth of honey samples following the moratorium. Clothianidin was the most frequently detected neonicotinoid. Neonicotinoid concentrations declined from May to September in the year following the ban. However, the majority of post-moratorium neonicotinoid residues were from honey harvested early in the year, coinciding with oilseed rape flowering. Neonicotinoid concentrations were correlated with the area of oilseed rape surrounding the hive location. These results suggest mass flowering crops may contain neonicotinoid residues where they have been grown on soils contaminated by previously seed treated crops. This may include winter seed treatments applied to cereals that are currently exempt from EU restrictions. Although concentrations of neonicotinoids were low (<2.0 ng g-1), and posed no risk to human health, they may represent a continued risk to honeybees through long-term chronic exposure.

Bumblebee family lineage survival is enhanced in high-quality landscapes.

Insect pollinators such as bumblebees (Bombus spp.) are in global decline. A major cause of this decline is habitat loss due to agricultural intensification. A range of global and national initiatives aimed at restoring pollinator habitats and populations have been developed. However, the success of these initiatives depends critically upon understanding how landscape change affects key population-level parameters, such as survival between lifecycle stages, in target species. This knowledge is lacking for bumblebees, because of the difficulty of systematically finding and monitoring colonies in the wild. We used a combination of habitat manipulation, land-use and habitat surveys, molecular genetics and demographic and spatial modelling to analyse between-year survival of family lineages in field populations of three bumblebee species. Here we show that the survival of family lineages from the summer worker to the spring queen stage in the following year increases significantly with the proportion of high-value foraging habitat, including spring floral resources, within 250-1,000 m of the natal colony. This provides evidence for a positive impact of habitat quality on survival and persistence between successive colony cycle stages in bumblebee populations. These findings also support the idea that conservation interventions that increase floral resources at a landscape scale and throughout the season have positive effects on wild pollinators in agricultural landscapes.

Efficient occupancy model-fitting for extensive citizen-science data.

Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species' range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists.

Bringing It All Together: Multi-species Integrated Population Modelling of a Breeding Community.

Integrated population models (IPMs) combine data on different aspects of demography with time-series of population abundance. IPMs are becoming increasingly popular in the study of wildlife populations, but their application has largely been restricted to the analysis of single species. However, species exist within communities: sympatric species are exposed to the same abiotic environment, which may generate synchrony in the fluctuations of their demographic parameters over time. Given that in many environments conditions are changing rapidly, assessing whether species show similar demographic and population responses is fundamental to quantifying interspecific differences in environmental sensitivity and highlighting ecological interactions at risk of disruption. In this paper, we combine statistical approaches to study populations, integrating data along two different dimensions: across species (using a recently proposed framework to quantify multi-species synchrony in demography) and within each species (using IPMs with demographic and abundance data). We analyse data from three seabird species breeding at a nationally important long-term monitoring site. We combine demographic datasets with island-wide population counts to construct the first multi-species Integrated Population Model to consider synchrony. Our extension of the IPM concept allows the simultaneous estimation of demographic parameters, adult abundance and multi-species synchrony in survival and productivity, within a robust statistical framework. The approach is readily applicable to other taxa and habitats. Supplementary materials accompanying this paper appear on-line. ELECTRONIC SUPPLEMENTARY MATERIAL: Supplementary materials for this article are available at 10.1007/s13253-017-0279-4.

A generalized 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 rôle. 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.

Breeding together: modeling synchrony in productivity in a seabird community.

With environmental conditions changing rapidly, there is a need to move beyond single-species models and consider how communities respond to environmental drivers. We present a modeling approach that allows estimation of multispecies synchrony in productivity, or its components, and the contribution of environmental covariates as synchronizing and desynchronizing agents. We apply the model to long-term breeding success data for five seabird species at a North Atlantic colony. Our Bayesian analysis reveals varying degrees of synchrony in overall productivity, with a common signal indicating a significant decline in productivity between 1986 and 2009. Productivity in seabirds reflects conditions in the marine ecosystem so the estimated synchronous component is a useful indicator of local marine environment health. For the two species for which we have most data, the environmental contribution to overall productivity synchrony is driven principally by effects operating at the chick stage rather than during incubation. Our results emphasize the importance of studying together species that coexist in a community. The framework, which accommodates interspecific clutch-size variation, is readily applicable to any species assemblage in any ecosystem where long-term productivity data are available.

Avoidance of a generalist entomopathogenic fungus by the ladybird, Coccinella septempunctata.

Fungal entomopathogens are ubiquitous within the environment and susceptible insects are predicted to be under strong selection pressure to detect and avoid virulent isolates. Beauveria bassiana is an entomopathogenic fungus with a wide host range including coccinellids. Seven-spot ladybirds, Coccinella septempunctata, overwinter predominantly in leaf litter and B. bassiana is one of their major mortality factors during winter in temperate regions. Behavioural assays were conducted to assess the ability of adult C. septempunctata to avoid lethal densities of B. bassiana conidia in soil or on leaves. Further assays considered avoidance by C. septempunctata of mycosed (B. bassiana) C. septempunctata cadavers compared with uninfected C. septempunctata cadavers or in vitro B. bassiana. Treatments in any bioassays entirely avoided by C. septempunctata were regarded as censored data, to overcome the difficulties associated with zeros in log-ratio analyses. Male and female C. septempunctata avoided contact with leaf surfaces and soil inoculated with B. bassiana and mycosed cadavers. The ability of C. septempunctata to detect and avoid B. bassiana conidia is an adaptation that undoubtedly increases survival and ultimately fitness. We predict that such behavioural responses are widespread and driven by the high cost of fungal infection to a host.

A note on simplifying likelihoods for site occupancy models.

We show how a simple reparameterization can reduce the number of parameters that need to be estimated by numerical maximum likelihood in site occupancy models. Three examples are provided.

On smoothing trends in population index modeling.

In this article, we consider the U.K. Common Birds Census counts and their use in monitoring bird abundance. We use a state-space modeling approach within a Bayesian framework to describe population level trends over time and contribute to the alert system used by the British Trust for Ornithology. We account for potential overdispersion and excess zero counts by modeling the observation process with a zero-inflated negative binomial, while the system process is described by second-order polynomial growth models. In order to provide a biological motivation for the amount of smoothing applied to the observed series the system variance is related to the demographic characteristics of the species, so as to help the specification of its prior distribution. In particular, the available information on productivity and survival is used to formulate prior expectations on annual percentage changes in the population level and then used to constrain the variance of the system process. We discuss an example of how to interpret alternative choices for the degree of smoothing and how these relate to the classification of species, over time, into conservation lists.

Methods for joint inference from panel survey and demographic data.

A number of methods for joint inference from animal abundance and demographic data have been proposed in recent years, each with its own advantages. A new approach to analyzing panel survey and demographic data simultaneously is described. The approach fits population-dynamics models to the survey data, rather than to a single index of abundance derived from them and thus avoids disadvantages inherent in analyzing such an index. The methodology is developed and illustrated with British Lapwing data, and the results are compared with those obtained from existing approaches. The estimates of demographic parameters and population indices are similar for all methods. The results of a simulation study show that the new method performs well in terms of mean squared error.