Inferring node dates from tip dates in fossil Canidae: the importance of tree priors

Tip-dating methods are becoming popular alternatives to traditional node calibration approaches for building time-scaled phylogenetic trees, but questions remain about their application to empirical datasets. We compared the performance of the most popular methods against a dated tree of fossil Canidae derived from previously published monographs. Using a canid morphology dataset, we performed tip-dating using BEAST v. 2.1.3 and M r B ayes v. 3.2.5. We find that for key nodes ( Canis, approx. 3.2 Ma, Caninae approx. 11.7 Ma) a non-mechanistic model using a uniform tree prior produces estimates that are unrealistically old (27.5, 38.9 Ma). Mechanistic models (incorporating lineage birth, death and sampling rates) estimate ages that are closely in line with prior research. We provide a discussion of these two families of models (mechanistic versus non-mechanistic) and their applicability to fossil datasets.

Inferring node dates from tip dates in fossil Canidae: the importance of tree priors

Tip-dating methods are becoming popular alternatives to traditional node calibration approaches for building time-scaled phylogenetic trees, but questions remain about their application to empirical datasets. We compared the performance of the most popular methods against a dated tree of fossil Canidae derived from previously published monographs. Using a canid morphology dataset, we performed tip-dating using Beast 2.1.3 and MrBayes 3.2.5. We find that for key nodes (Canis, ~3.2 Ma, Caninae ~11.7 Ma) a non-mechanistic model using a uniform tree prior produces estimates that are unrealistically old (27.5, 38.9 Ma). Mechanistic models (incorporating lineage birth, death, and sampling rates) estimate ages that are closely in line with prior research. We provide a discussion of these two families of models (mechanistic vs. non-mechanistic) and their applicability to fossil datasets.

Non-minimal tree actions and the existence of non-uniform tree lattices

A uniform tree is a tree that covers a finite connected graph. Let X be any locally finite tree. Then G = Aut(X) is a locally compact group. We show that if X is uniform, and if the restriction of G to the unique minimal G-invariant subtree X0 ⊆ X is not discrete then G contains non-uniform lattices; that is, discrete subgroups Γ for which Γ/G is not compact, yet carries a finite G-invariant measure. This proves a conjecture of Bass and Lubotzky for the existence of non-uniform lattices on uniform trees.

646 Applications of Site-Specific Management for Tree Fruit Production

Our efforts are concentrated on quantifying spatial variability for tree vigor, yield, fruit quality, and profit. We use aerial photography to quantify tree vigor. For mechanically harvested hazelnuts, a prototype weight based yield monitor has been evaluated. This approach may also work for quantifying yield in mechanically harvested sweet cherries. For perishable hand-harvested crops, the GPS locations for individual bar-coded bins can be used to calculate bin density and estimate yield. Bar codes can also be used to track quality in the packing-house. Since profit depends on yield, size, and packout, it is not always intuitively obvious which areas of an orchard are most profitable. Defining which areas are most profitable, and identifying the problems associated with low-profit areas (poor yield, small size, storage loss, bruising, culls, etc) is an important step. Identifying areas producing fruit that stores poorly is a high priority. An evaluation of low- and high-profit areas may lead to alternate management plans. Anything from investing in more supervision of harvest labor and initiating different pruning regimes to attempts to obtain more uniform tree vigor can be evaluated. By delineating test areas with GPS boundaries, profit data in future years can quantify the success of different management approaches. For example, concentrating expensive inputs on the portion of trees (30% of total) that may produce the majority of gross returns, while not even harvesting fruit from regions (1% to 5% of total) that consistently produce poor quality fruit may be a sound strategy.