The traditional root stock breeding programs have produced the interspecific hybrid ‘GF-6770 , GN series, ‘Root-Pac 400 , ‘Vlach’, ‘RX10 , ‘VX2110 , ‘UCB10 , ‘Newberg’, and ‘Apache’ root stocks in different nut trees. However, for tree nut crops, which have long extended juvenility, long productive lives and high heterozygosity, the traditional breeding approaches employed in annual crops are too slow, and costly. Understanding how root stocks and scion interact can provide modern breeders new techniques to improve tree nut crops productivity. Incorporating the newly emerging technologies including high-throughput phenotyping and genotyping as well as genome-wide transcriptome analysis into investigations of the genetic and domestication processes of nut trees root stock species will address pertinent questions for root stock biology and breeding. Among these questions are how the root stock/scion interactions affect graft compatibility, vigor, water and nutrient uptake and efficiency, biotic and abiotic stresses, yield, and quality. Of particular value in root stock breeding programs is germplasm collection and construction of grafting experiments to identify the genes associated with phenotypic variation in both the root stock and the scion. The collection of genomic data for nut trees is accelerating as the cost of next generation sequencing decreases. The almond, hazelnut, walnut, pistachio, and pecan genomes have been fully sequenced and are available. In the near future reliable phenotypic data will be the rate limiting step in root stock improvement. As tree nut crops are highly heterozygous with long juvenility periods and productive lives, genomic based approaches, such as marker-assisted selection , genome-wide association study , genomic selection ,blueberry pot size and genetic transformation offer promise for root stock breeding. Comprehensive germplasm collections, coupled with genomic approaches, has the potential to yield significant advances in grafted tree nut crops.
Predicting the flowering time of angiosperm taxa under projected climate conditions or in locations at which flowering has not been observed is essential to the prediction of a wide array of ecological processes, including risk of frost damage to floral tissues , nectar and pollen availability to pollinators , and the intensity of competition for pollinators among co-flowering taxa . Phenological prediction can also be important to local tourism, and for determining the optimum time for herbicide or pesticide treatment. For example, accurate predictions of flowering time can prevent the planned application of pesticides during flowering, when beneficial insects and birds are visiting flowers. Similarly, the planned use of herbicides to suppress invasive plant species should occur before or during flowering, so as to minimize seed production. Consequently, the ability to predict the flowering times of angiosperm species is relevant not only to ecologists and other researchers, but also to land managers and other professionals across a wide array of disciplines. In recent years, some tools have emerged to predict phenological timing under various climate conditions, such as the phenological forecast maps produced by Phenology Forecasts or univariate phenological models produced by the USA National Phenology Network . To date, however, species-specific phenological models have been developed for only a small number of species, and such models have often required daily growing degree-day or chilling degree-day information, which until recently have not been readily available across the vast majority of locations, and have required significant technical expertise to utilize effectively. Furthermore, the output of such models is rarely bundled in such a way as to facilitate phenological predictions in the absence of extensive calculations or data manipulations on the part of the user.In this paper, we present PhenoForecaster, a software package that allows users to predict quickly and easily the mean flowering date for each of 2320 angiosperm species.
PhenoForecaster uses readily accessible climate data in combination with species-specific phenological models that were generated by the authors using a simplified version of a method previously used to evaluate phenological responses to climate using digital herbarium records . Specifically, PhenoForecaster uses estimates of five climate parameters to predict the day of year on which the selected angiosperm species will reach its mean flowering date at a location experiencing those conditions. These parameters represent the climate cues to which MFD was found to be most sensitive across the majority of these species using similar data and modeling techniques to those used by PhenoForecaster . In order to facilitate PhenoForecaster’s use, all of the phenoclimate models that it uses were limited to these climate parameters, which were sufficient to retain the majority of the predictive power produced by more complicated models . This package allows both manual entry of climate parameters as well as bulk entry of data in cases where phenological predictions are required across multiple locations or climate scenarios. PhenoForecaster has been designed to accept climate input in a comma-separated value format that is compatible with climate data generated by ClimateNA , a freely available software package that produces spatially explicit estimates of historical climate conditions throughout North America, and which utilizes a user-friendly graphical interface and requires only that the user provide the latitude and longitude of all points of interest. Thus, while predictions of phenological timing for a given plant species previously required extensive observation, modeling, and calculation, PhenoForecaster represents a simple-to-use tool through which the phenology of many angiosperm species can be readily predicted under any observed or theoretical climate.To install the package, the user simply needs to download and run the installer. The executable has been successfully tested on Windows 7, 8, and 10.
PhenoForecaster has an intuitive graphical user interface that allows users with minimal prior experience with phenological prediction or with PhenoForecaster to predict the phenological timing of any targeted species by implementing the following steps. First, the user must select the subset of species-specific models from which they wish to choose, based on the minimum model reliability they desire. By default, only the 490 species-specific models for which expected mean absolute error ≤15 days were considered to be “good” model fits, and are therefore displayed for selection. Depending on user preference, however, this list of species may be expanded to include species-specific models that exhibit higher MAE, or contracted to only display those species for which more accurate phenological models are available . Having filtered the species by the minimum MAE desired, the user must then use the species selection drop down menu to select the species for which phenological predictions are to be generated . Second,grow blueberries in pots the specific climatic conditions for which phenological predictions are desired may then be entered manually or uploaded as a CSV data file . For the latter, the first line of the input file is a header line with column descriptions. The first two columns of the file, labeled ‘ID1’ and ‘ID2’, represent any string data the user desires to include for the purpose of identifying each row of data in a unique fashion. The remaining columns may be in any order, but must include the following: ‘NFFD_wt’, ‘NFFD_sp’, ‘PAS_wt’, ‘PAS_sp’, and ‘BFFP’. Data in the column ‘NFFD_wt’ should consist of a count of the number of frost-free days from January 1 to March 31 in the year for which flowering time is to be estimated. Data in the column ‘NFFD_sp’ should consist of a count of the number of frost free days from April 1 to June 30 in the year for which flowering time is to be estimated. Data in the column ‘PAS_wt’ should consist of the total precipitation that fell as snow from January 1 to March 31 in the year for which flowering time is to be estimated. Data in the column ‘PAS_sp’ should consist of the total precipitation that fell as snow from April 1 to June 30 in the year for which flowering time is to be estimated. Data in the column ‘BFFP’ should consist of the DOY on which the annual frost-free period began. PhenoForecaster allows any number of additional data columns to be placed into the input file. In cases where the user desires that data from such additional columns be preserved in the output file created by PhenoForecaster, they may select the ‘retain all input data’ option in the lower left of the user interface. If this option is selected, PhenoForecaster will preserve all columns from the input data, appending a new column with the header ‘DOY_Predicted’ that consists of the predicted MFD for a given row of data, and output all data as a CSV file. Otherwise, PhenoForecaster will generate output in the form of a CSV file, with the headers ‘ID1’, ‘ID2’, and ‘DOY_Predicted.’ PhenoForecaster utilizes phenoclimate models that were constructed for each species from herbarium-based phenological data using a total of 556,322 digital records of herbarium specimens collected in flower across 72 herbaria throughout North America , collected between 1901 and 2015 and structured in Darwin Core format. Specimens that did not include either the decimal latitude and longitude from which the sample was collected or the precise date of collection were eliminated. Specimens that were not explicitly recorded as being in flower within either the Darwin Core fields ‘reproductive condition’ or ‘life stage’ were eliminated. Specimens that were only listed as ‘in bud’ or ‘fruiting’ were not considered to be in flower for purposes of this analysis. Duplicate specimens were also excluded from analysis. Each remaining specimen therefore represented a single phenological observation. Phenological models derived using herbarium-based observations of flowering phenology have been found to accurately predict shifts in phenological events that were observed in situ in response to climate changes . Species-specific models of MFD for each species were conducted using elastic net regularization, which has previously been demonstrated to be an effective method for predicting the flowering times of angiosperm taxa using herbarium specimens .
For the models used by PhenoForecaster, winter and spring climate conditions at the location and DOY from which each specimen was collected were first estimated using the software package ClimateNA . Each species specific phenoclimate model was then constructed using elastic net regularization, a multivariate regression method that, rather than selecting or removing parameters in a binary fashion as with forward or backward selection, enforces parsimony by penalizing model complexity using two penalty terms: the sum of the absolute value of all parameter coefficients , and the sum of all parameter coefficients squared .This method has substantial advantages over stepwise forward selection or backward elimination regression techniques, particularly when handling data sets in which multiple explanatory factors are likely to exhibit some degree of collinearity, such as is common in climatic data . Elastic net regression has been found to generate models that remain highly stable in cases where multiple explanatory factors exhibit collinearity , while avoiding the variance inflation that often occurs when using stepwise regression techniques . For each angiosperm species that was represented by 100 or more specimens in our herbarium-based data set, phenological models were constructed to predict the MFD of that species from local climate conditions using the elasticCV class contained within Scikit-Learn 0.814-4 in Python, which conducts an internally cross-validated version of elastic net regularization that selects the optimal values for the weighting terms ρ and α in order to minimize both model complexity and standard error . The models used for each species in this study were constructed through iterative fitting along a regularization path, using 100 values of α and 22 values of ρ . The optimal model coefficients were then selected using 25-fold cross-validation. The MAE for each model represents the mean MAE of the 25 iterations in which it was trained and tested using separate data sets; this value therefore represents the expected degree of error that may be expected for phenological predictions of a given species under novel conditions . Additionally, the accuracy of these species-specific models was tested for three species using observations of mean flowering time derived from in situ phenological observations provided by the USA National Phenology Network database. The models used by PhenoForecaster predicted the timing of both in situ and herbarium-based observations of mean flowering with similar accuracy . Species for which phenoclimate models produced MAE values of <15 days were considered to exhibit “good” model fits by default. However, PhenoForecaster allows users to alter the MAE threshold that they consider to represent “good” model performance to accommodate cases where higher or lower predictive accuracy is required.