Dynamic Models for Longitudinal Butterfly Data

Climate-driven variation in biotic interactions provides a narrow and variable window of opportunity for an insect herbivore at its ecological margin

Climate-driven geographic range shifts have been associated with transitions between dietary specialism and generalism at range margins. The mechanisms underpinning these often transient niche breadth modifications are poorly known, but utilization of novel resources likely depends on phenological synchrony between the consumer and resource. We use a climate-driven range and host shift by the butterfly Aricia agestis to test how climate-driven changes in host phenology and condition affect phenological synchrony, and consider implications for host use. Our data suggest that the perennial plant that was the primary host before range expansion is a more reliable resource than the annual Geraniaceae upon which the butterfly has become specialized in newly colonized parts of its range. In particular, climate-driven phenological variation in the novel host Geranium dissectum generates a narrow and variable 'window of opportunity' for larval productivity in summer. Therefore, although climatic change may allow species to shift hosts and colonise novel environments, specialization on phenologically limited hosts may not persist at ecological margins as climate change continues. We highlight the potential role for phenological (a)synchrony in determining lability of consumer-resource associations at range margins and the importance of considering causes of synchrony in biotic interactions when predicting range shifts. This article is part of the theme issue 'Species' ranges in the face of changing environments (Part II)'.

A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators 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.

Supplementary materials accompanying this paper appear online.

Linking inter-annual variation in environment, phenology, and abundance for a montane butterfly community

Climate change has caused widespread shifts in species’ phenology, but the consequences for population and community dynamics remain unclear because of uncertainty regarding the species‐specific drivers of phenology and abundance, and the implications for synchrony among interacting species. Here, we develop a statistical model to quantify inter‐annual variation in phenology and abundance over an environmental gradient, and use it to identify potential drivers of phenology and abundance in co‐occurring species. We fit the model to counts of 10 butterfly species with single annual generations over a mountain elevation gradient, as an exemplar system in which temporally limited availability of biotic resources and favorable abiotic conditions impose narrow windows of seasonal activity. We estimate parameters describing changes in abundance, and the peak time and duration of the flight period, over ten years (2004–2013) and across twenty sample locations (930–2,050 m) in central Spain. We also use the model outputs to investigate relationships of phenology and abundance with temperature and rainfall. Annual shifts in phenology were remarkably consistent among species, typically showing earlier flight periods during years with warm conditions in March or May–June. In contrast, inter‐annual variation in relative abundance was more variable among species, and generally less well associated with climatic conditions. Nevertheless, warmer temperatures in June were associated with increased relative population growth in three species, and five species had increased relative population growth in years with earlier flight periods. These results suggest that broadly coherent interspecific changes to phenology could help to maintain temporal synchrony in community dynamics under climate change, but that the relative composition of communities may vary due to interspecific inconsistency in population dynamic responses to climate change. However, it may still be possible to predict abundance change for species based on a robust understanding of relationships between their population dynamics and phenology, and the environmental drivers of both.

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.