Females are attracted to the hollow material in trap nests.
When thinking of bees and wasps, most people have social insects living in colonies in mind. But most species are actually solitary. In these species, every female builds her own nest and does not care for the offspring once nest construction is completed. Most of those species nest in the ground. Several thousand species of bees and wasps use pre-existing above-ground cavities though (such as hollow twigs and stems, cracks under bark, or empty galleries of wood-boring insects).
To keep you in suspense, I’ll resolve the importance of studying cavity-nesting species later in this blog post. First, I’ll introduce you to one of the more elegant research methods in ecology: trap nests. To study and collect these cavity-nesting species, you can take advantage of their nesting preferences. By exposing artificial cavities and offering access to an otherwise restricted nesting resource, you can attract females searching for suitable nesting sites.
Building these trap nests is simple, but the design can vary greatly. Many designs and materials can be used to build the artificial nesting sites, such as drilling holes in wooden blocks or packing hollow plant material (e.g. reeds) in plastic tubes. Once females find the trap nest and finish their nest construction, the developing offspring are literally ‘trapped’ in their nests. They can then be collected, their trophic interactions (e.g. food and natural enemies) observed, and the specimens can be reared for identification. Continue reading →
All of the articles in this month’s issue of Methods in Ecology and Evolution are free for the whole year. You will not need a subscription to access or download any of them throughout 2017.
Our first issue of this year contains three Applications articles and two Open Access articles. These five papers will be freely available permanently.
– CDMetaPOP: Cost–Distance Meta-POPulation provides a novel tool for questions in landscape genetics by incorporating population viability analysis, while linking directly to conservation applications.
– Rphylopars: An R implementation of PhyloPars, a tool for phylogenetic imputation of missing data and estimation of trait covariance across species (phylogenetic covariance) and within species (phenotypic covariance). Rphylopars provides expanded capabilities over the original PhyloPars interface including a fast linear-time algorithm, thus allowing for extremely large data sets (which were previously computationally infeasible) to be analysed in seconds or minutes rather than hours.
– ggtree: An R package that provides programmable visualisation and annotation of phylogenetic trees. ggtree can read more tree file formats than other software and allows colouring and annotation of a tree by numerical/categorical node attributes, manipulating a tree by rotating, collapsing and zooming out clades, highlighting user selected clades or operational taxonomic units and exploration of a large tree by zooming into a selected portion.
To understand how species survive in nature, demographers pair field-collected life history data on survival, growth and reproduction with statistical inference. Demographic approaches have significantly contributed to our understanding of population biology, invasive species dynamics, community ecology, evolutionary biology and much more.
As ecologists begin to ask questions about demography at broader spatial and temporal scales and collect data at higher resolutions, demographic analyses and new statistical methods are likely to shed even more light on important ecological mechanisms.
Traditionally, demographers collect life history data on species in the field under one or more environmental conditions. This approach has significantly improved our understanding of basic biological processes. For example, rosette size is a significant predictor of survival for plants like wild teasel (Werner 1975 – links to all articles are at the end of the post), and desert annual plants hedge their bets against poor years by optimizing germination strategies (Gremer & Venable 2014).
Demographers also include temporal and spatial variability in their models to help make realistic predictions of population dynamics. We now know that temporal variability in carrying capacity dramatically improves population growth rates for perennial grasses and provides a better fit to data than models with varying growth rates because of this (Fowler & Pease 2010). Moreover, spatial heterogeneity and environmental stochasticity have similar consequences for plant populations (Crone 2016). Continue reading →