Recent Trends In Laurentian Great Lakes Ice Cover

A 39-winter (1963–2001) record of annual maximum ice concentration (AMIC), the maximum fraction of lake surface area covered by ice each year, is analyzed for each Great Lake. Lake Erie has the largest median AMIC (94%) followed by Lakes Superior (80%), Huron(63%), Michigan (33%), and Ontario (21%). The frequency distributionof AMICs is negatively skewed for Lakes Superior and Erie and positively skewed for Lakes Michigan and Ontario. Temporal and spatial patterns of typical and extreme AMICs is presented within the context of long-term average air temperatures and lake bathymetry. The variation of spatially averaged ice concentration with discrete depth ranges are discussed for each lake for the upper and lower end of the typical range of AMIC values. In general, ice concentration decreases with increasing depth ranges for a given winter. A decrease in the gradient of ice concentration with depths was also observed with an increase in the AMIC from winter 1983 to winter 1984. A temporal trend in the AMICs supports the hypothesis of three ice cover regimes over the past 39 winters. Approximately 44% of the highest quartile (10 highest) AMICs for the Great Lakes occurred during the 6-winter period:1977–1982 providing evidence of a higher ice cover regime during thisperiod relative to the 14 winters before them (1963–1976) and the 19 winters after them (1983–2001). Winter 1998 established new low AMIC extremes,and the AMIC averaged over the 1998–2001 winters is the lowest for theperiod of record on four of the five Great Lakes. These recent trends taken together are noteworthy as they may be harbingers of a period of even lower AMICs in the 21st Century.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42580/1/10584_2004_Article_5095423.pd

Tradeoffs between the strength of conformity and number of conformists in variable environments

Organisms often respond to environmental change phenotypically, through learning strategies that enhance fitness in variable and changing conditions. But which strategies should we expect in population exposed to those conditions? We address this question by developing a mathematical model that specifies the consequences of different mixtures of individual and social learning strategies on the frequencies of different cultural variants in temporally and spatially changing environments. Assuming that alternative cultural variants are differently well-adapted to diverse environmental conditions, we are able to evaluate which mixture of learning strategies maximises the mean fitness of the population. We find that, even in rapidly changing environments, a high proportion of the population will always engage in social learning. In those environments, the highest adaptation levels are achieved through relatively high fractions of individual learning and a strong conformist bias. We establish a negative relationship between the proportion of the population learning socially and the strength of conformity operating in a population: strong conformity requires fewer conformists (i.e. larger proportion of individual learning), while many conformists can only be found when conformist transmission is weak. Investigations of cultural diversity show that in frequently changing environments high levels of adaptation require high level of cultural diversity. Finally, we demonstrate how the developed mathematical framework can be applied to time series of usage or occurrence data of cultural traits. Using Approximate Bayesian Computation we are able to infer information about the underlying learning processes that could have produced observed patterns of variation in the dataset

The bii4africa dataset of faunal and floral population intactness estimates across Africa’s major land uses

Sub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species’ population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate ‘intactness scores’: the remaining proportion of an ‘intact’ reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the reference) and, in rare cases, to 2 (populations that thrive in human-modified landscapes). The resulting bii4africa dataset contains intactness scores representing terrestrial vertebrates (tetrapods: ±5,400 amphibians, reptiles, birds, mammals) and vascular plants (±45,000 forbs, graminoids, trees, shrubs) in sub-Saharan Africa across the region’s major land uses (urban, cropland, rangeland, plantation, protected, etc.) and intensities (e.g., large-scale vs smallholder cropland). This dataset was co-produced as part of the Biodiversity Intactness Index for Africa Project. Additional uses include assessing ecosystem condition; rectifying geographic/ taxonomic biases in global biodiversity indicators and maps; and informing the Red List of Ecosystems

The bii4africa dataset of faunal and floral population intactness estimates across Africa’s major land uses

Sub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species’ population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate ‘intactness scores’: the remaining proportion of an ‘intact’ reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the reference) and, in rare cases, to 2 (populations that thrive in human-modified landscapes). The resulting bii4africa dataset contains intactness scores representing terrestrial vertebrates (tetrapods: ±5,400 amphibians, reptiles, birds, mammals) and vascular plants (±45,000 forbs, graminoids, trees, shrubs) in sub-Saharan Africa across the region’s major land uses (urban, cropland, rangeland, plantation, protected, etc.) and intensities (e.g., large-scale vs smallholder cropland). This dataset was co-produced as part of the Biodiversity Intactness Index for Africa Project. Additional uses include assessing ecosystem condition; rectifying geographic/taxonomic biases in global biodiversity indicators and maps; and informing the Red List of Ecosystems

Long-term changes in macroinvertebrate populations and water quality in four urbanized streams of So

Master of ScienceNatural Resources and EnvironmentUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/101637/1/39015052696161.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/101637/2/39015052696161.pd

Data from: aTRAM - automated Target Restricted Assembly Method: a fast method for assembling loci across divergent taxa from next-generation sequencing data

Background: Assembling genes from next-generation sequencing data is not only time consuming but computationally difficult, particularly for taxa without a closely related reference genome. Assembling even a draft genome using de novo approaches can take days, even on a powerful computer, and these assemblies typically require data from a variety of genomic libraries. Here we describe software that will alleviate these issues by rapidly assembling genes from distantly related taxa using a single library of paired-end reads: aTRAM, automated Target Restricted Assembly Method. The aTRAM pipeline uses a reference sequence, BLAST, and an iterative approach to target and locally assemble the genes of interest. Results: Our results demonstrate that aTRAM rapidly assembles genes across distantly related taxa. In comparative tests with a closely related taxon, aTRAM assembled the same sequence as reference-based and de novo approaches taking on average 1,000 genes from six taxa ranging from 25 – 110 million years divergent from the reference taxon. The gene recovery was between 97 – 99% from each taxon. Conclusions: aTRAM can quickly assemble genes across distantly-related taxa, obviating the need for draft genome assembly of all taxa of interest. Because aTRAM uses a targeted approach, loci can be assembled in minutes depending on the size of the target. Our results suggest that this software will be useful in rapidly assembling genes for phylogenomic projects covering a wide taxonomic range, as well as other applications. The software is freely available: http://www.github.com/juliema/aTRA

aTRAM - automated target restricted assembly method: a fast method for assembling loci across divergent taxa from next-generation sequencing data

Background: Assembling genes from next-generation sequencing data is not only time consuming but computationally difficult, particularly for taxa without a closely related reference genome. Assembling even a draft genome using de novo approaches can take days, even on a powerful computer, and these assemblies typically require data from a variety of genomic libraries. Here we describe software that will alleviate these issues by rapidly assembling genes from distantly related taxa using a single library of paired-end reads: aTRAM, automated Target Restricted Assembly Method. The aTRAM pipeline uses a reference sequence, BLAST, and an iterative approach to target and locally assemble the genes of interest. Results: Our results demonstrate that aTRAM rapidly assembles genes across distantly related taxa. In comparative tests with a closely related taxon, aTRAM assembled the same sequence as reference-based and de novo approaches taking on average 1,000 genes from six taxa ranging from 25 – 110 million years divergent from the reference taxon. The gene recovery was between 97 – 99% from each taxon. Conclusions: aTRAM can quickly assemble genes across distantly-related taxa, obviating the need for draft genome assembly of all taxa of interest. Because aTRAM uses a targeted approach, loci can be assembled in minutes depending on the size of the target. Our results suggest that this software will be useful in rapidly assembling genes for phylogenomic projects covering a wide taxonomic range, as well as other applications. The software is freely available http://www.github.com/juliema/aTRAM .Botany, Department ofZoology, Department ofNon UBCScience, Faculty ofReviewedFacult