Body size affects the spatial scale of habitat–beetle interactions

We used abundance data and the program Focus to determine the spatial scale at which 31 species of longhorned beetles (Coleoptera: Cerambycidae) respond to forest habitat amount. We predicted that the spatial scale at which species respond would increase with body size, and that species using ephemeral larval habitat would respond at larger spatial scales than species using more stable larval habitat. We found that forest cover was a better measure of the amount of habitat for polyphagous species than for oligophagous species. Larger species of longhorned beetles responded to forest cover at larger scales. We did not find evidence that species using more ephemeral larval habitat conditions responded at larger scales than species developing in more stable habitat conditions. Our results highlight the importance of accurately describing habitat in studies of species–environment relationships. While scales of response may be species-specific, some generalizations across species are possible.     

How far do songbirds disperse?

Dispersal distances determine the scales over which many population processes occur. Knowledge of these distances may therefore be crucial in determining the appropriate spatial scales for research and management. However, dispersal distances are difficult to measure, especially for vagile organisms like songbirds. For these species, the use of traditional mark–recapture and radio‐telemetry methods is problematic. We used positive one‐year time‐lagged correlations in abundance to estimate natal dispersal distances. Using the North American Breeding Bird Survey database, we examined one‐year time‐lagged correlations between pairs of North American songbird samples separated by 10–100 km. We submit that consistent positive one‐year time‐lagged correlations reflect the exchange of individuals through dispersal. We found positive one‐year time‐lagged correlations between pairs of samples from 25 different songbird species. The median distances of these correlations ranged from 15 to 95 km, depending on the species. These distances were positively correlated with body size and wing length. Dispersal appears to be the most parsimonious explanation for the time‐lagged correlations we observed in these species. The putative dispersal distances we measured are generally an order of magnitude longer than those reported in the literature.     

The trade-off between housing density and sprawl area: Minimising impacts to forest breeding birds

Increasing housing density is generally assumed to confer negative effects on forest breeding birds. This implies we should build at low density over the landscape to conserve these species. However, for a given human population, low-density development must cover a large area, resulting in sprawl. A pertinent question is then: at what housing density are the impacts of a given human population on forest biodiversity minimised? For a given human population, it is unclear whether the impacts on forest biodiversity are less where housing density is high and sprawl area is small or where housing density is low and sprawl area is large. We addressed this question using the abundance, species richness and evenness of forest birds in Ottawa, Ontario and Gatineau, Quebec, Canada. First, we counted breeding birds at 22 sites representing a range of housing densities. We then used these empirical measurements to estimate forest bird abundance, species richness and evenness in four hypothetical development scenarios representing the trade-off between housing density and sprawl area. With the exception of the Undeveloped scenario (i.e., continuous forest), forest birds and forest interior birds were most abundant in the Compact scenario and most speciose in the Semi-compact scenario, whereas forest edge birds were most abundant and speciose in the Dispersed scenario. All three bird groups were most even in the Compact scenario. We conclude that compact housing development (building at high density over a small area) minimises the impacts of a given human population on forest breeding birds.     

Appropriation and Dementia in India

Biomedical technologies like MRI scans offer a way for carers and people with dementia to ‘see’ pathology, as a means to reorient their perceptions of the body and functionality. Through interpretive and syncretic processes, the MRI and the diagnosis of dementia facilitate the incorporation of the clinical category ‘dementia’ into social understandings of illness and care in India. Complex shifts occur as families and providers move from socio-cultural explanations of disruption to bio-social etiologies of the disease ‘dementia’ and then to socio-ecological frameworks of causality. Both the biomedicalisation of illness and the localisation of illness occur as the clinical category ‘dementia’ is folded into local understandings of illness and care. Through elucidating how the dialectic between biomedical and local knowledge is operationalized, we offer insights into how dementia is absorbed and appropriated into Indian cultural contexts.     

Changing genetic paradigms: creating next-generation genetic databases as tools to understand the emerging complexities of genotype/phenotype relationships

Understanding genotype/phenotype relationships has become more complicated as increasing amounts of inter- and intra-tissue genetic heterogeneity have been revealed through next-generation sequencing and evidence showing that factors such as epigenetic modifications, non-coding RNAs and RNA editing can play an important role in determining phenotype. Such findings have challenged a number of classic genetic assumptions including (i) analysis of genomic sequence obtained from blood is an accurate reflection of the genotype responsible for phenotype expression in an individual; (ii) that significant genetic alterations will be found only in diseased individuals, in germline tissues in inherited diseases, or in specific diseased tissues in somatic diseases such as cancer; and (iii) that mutation rates in putative disease-associated genes solely determine disease phenotypes. With the breakdown of our traditional understanding of genotype to phenotype relationships, it is becoming increasingly apparent that new analytical tools will be required to determine the relationship between genotype and phenotypic expression. To this end, we are proposing that next-generation genetic database (NGDB) platforms be created that include new bioinformatics tools based on algorithms that can evaluate genetic heterogeneity, as well as powerful systems biology analysis tools to actively process and evaluate the vast amounts of both genomic and genomic-modifying information required to reveal the true relationships between genotype and phenotype.