Michael Dorman

Iris atrofusca genetic and phenotypic variation, the role of habitat-specific selection in this variation structuring, and conservation implications using quasi in situ guidelines

Knowing the extent and structure of genetic variation in an endangered species is essential for establishing efficient conservation practices. However, the proper use of this information requires understanding the role of habitat-specific selection in genetic structuring. We present a study of population differentiation in an endangered species that utilizes guidelines of recently a proposed quasi in situ conservation approach, i.e. taking into account the scale and spatial pattern of local adaptation since if local adaptation is important, the introduced genotypes must be matched to the local biotic/abiotic conditions. Following this approach, we examined the extent and structure of genetic (AFLP) and phenotypic variation and tested for adaptive significance of this variation in critically endangered Iris atrofusca growing in Israel and Jordan. From these results we propose a sampling design that would (i) preserve species adaptive potential and (ii) insure environmental match of the plant material for r...

Estimating daily minimum, maximum, and mean near surface air temperature using hybrid satellite models across Israel

ABSTRACT Meteorological stations measure air temperature (Ta) accurately with high temporal resolution, but usually suffer from limited spatial resolution due to their sparse distribution across rural, undeveloped or less populated areas. Remote sensing satellite‐based measurements provide daily surface temperature (Ts) data in high spatial and temporal resolution and can improve the estimation of daily Ta. In this study we developed spatiotemporally resolved models which allow us to predict three daily parameters: Ta Max (day time), 24 h mean, and Ta Min (night time) on a fine 1 km grid across the state of Israel. We used and compared both the Aqua and Terra MODIS satellites. We used linear mixed effect models, IDW (inverse distance weighted) interpolations and thin plate splines (using a smooth nonparametric function of longitude and latitude) to first calibrate between Ts and Ta in those locations where we have available data for both and used that calibration to fill in neighboring cells without surface monitors or missing Ts. Out‐of‐sample ten‐fold cross validation (CV) was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with and without available Ts observations for both Aqua and Terra (CV Aqua R2 results for min 0.966, mean 0.986, and max 0.967; CV Terra R2 results for min 0.965, mean 0.987, and max 0.968). Our research shows that daily min, mean and max Ta can be reliably predicted using daily MODIS Ts data even across Israel, with high accuracy even for days without Ta or Ts data. These predictions can be used as three separate Ta exposures in epidemiology studies for better diurnal exposure assessment. HighlightsWe Estimated Min (night time), Max (day time) and 24 h means air temperature.We provide three discrete daily 1 km air temperature estimations across Israel.We compared Aqua and Terra performance for estimating means air temperature.

Factors affecting dormancy of Oncocyclus iris seeds.

Oncocyclus irises (Iridaceae) are endangered plants in Israel, yet with high potential for cultivation as ornamental flowers. However, their high seed dormancy level prevents fast development of germplasm for horticultural reproduction. In this paper we describe in-vitro and in-vivo germination experiments with seeds of Oncocyclus irises from Israel. We examined the effects of (1) mechanical scarification and different growing media on in-vitro seed germination; and (2) soil type, covering, and water amount on in-vivo germination. Seeds showed high dormancy, as hardly any seed germinated in the first year after sowing, and only in the second growing season the germinating fraction was considerable (up to 37%), still only under high humidity conditions. We also report an effective in-vitro forced germination protocol, which employs seed scarification. Following these results for in-vivo germination, and based on the protocol developed for in-vitro germination, we recommend two methods for artificial seed germination. For fast germination, good results from a modest quantity of seeds can be obtained by an in-vitro forced germination. For mass seed propagation, when time is not a limiting factor, the in-vivo procedure can be used, using an artificial soil seed bank and treating those seeds during (at least) two seasons under shade and continuous watering.

Testing the potential significance of different scion/rootstock genotype combinations on the ecology of old cultivated olive trees in the southeast Mediterranean area

BackgroundA previous multi-locus lineage (MLL) analysis of SSR-microsatellite data of old olive trees in the southeast Mediterranean area had shown the predominance of the Souri cultivar (MLL1) among grafted trees. The MLL analysis had also identified an MLL (MLL7) that was more common among rootstocks than other MLLs. We here present a comparison of the MLL combinations MLL1 (scion)/MLL7 (rootstock) and MLL1/MLL1 in order to investigate the possible influence of rootstock on scion phenotype.ResultsA linear regression analysis demonstrated that the abundance of MLL1/MLL7 trees decreases and of MLL1/MLL1 trees increases along a gradient of increasing aridity. Hypothesizing that grafting on MLL7 provides an advantage under certain conditions, Akaike information criterion (AIC) model selection procedure was used to assess the influence of different environmental conditions on phenotypic characteristics of the fruits and oil of the two MLL combinations. The most parsimonious models indicated differential influences of environmental conditions on parameters of olive oil quality in trees belonging to the MLL1/MLL7 and MLL1/MLL1 combinations, but a similar influence on fruit characteristics and oil content. These results suggest that in certain environments grafting of the local Souri cultivar on MLL7 rootstocks and the MLL1/MLL1 combination result in improved oil quality. The decreasing number of MLL1/MLL7 trees along an aridity gradient suggests that use of this genotype combination in arid sites was not favoured because of sensitivity of MLL7 to drought.ConclusionsOur results thus suggest that MLL1/MLL7 and MLL1/MLL1 combinations were selected by growers in traditional rain-fed cultivation under Mediterranean climate conditions in the southeast Mediterranean area.

Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA

Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically used to address the measurement error in AOD. We compare three machine-learning methods: random forests, gradient boosting, and extreme gradient boosting (XGBoost) to characterize and correct measurement error in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 × 1 km AOD product for Aqua and Terra satellites across the Northeastern/Mid-Atlantic USA versus collocated measures from 79 ground-based AERONET stations over 14 years. Models included 52 quality control, land use, meteorology, and spatially-derived features. Variable importance measures suggest relative azimuth, AOD uncertainty, and the AOD difference in 30-210 km moving windows are among the most important features for predicting measurement error. XGBoost outperformed the other machine-learning approaches, decreasing the root mean squared error in withheld testing data by 43% and 44% for Aqua and Terra. After correction using XGBoost, the correlation of collocated AOD and daily PM2.5 monitors across the region increased by 10 and 9 percentage points for Aqua and Terra. We demonstrate how machine learning with quality control and spatial features substantially improves satellite-derived AOD products for air pollution modeling.

Detecting biodiversity refugia using remotely sensed data

ContextHabitats characterized by improved soil moisture availability can function as microrefugia (hereafter referred to as “refugia”) for the persistence of rare plant species in dry environments. Such areas are dominated by Mediterranean woody vegetation (shrubland and woodland). An analysis of these refugia elucidates their spatial distribution at the landscape scale.ObjectivesExplore whether potential refugia, detected using the upper quantile of the normalized difference vegetation index (NDVI), are related, in space and time, with the survivability of rare species in dry environments.MethodsWe used upper NDVI quantile (25%) values to predict potential refugia in nine selected areas in northern parts of Israel from 1992 to 2011. Next, we developed an index based on the ratio of density (number of observations per area) of rare species in non-refugia versus refugia patches, per site (density of rare species index, DRSI). Finally, we examined the temporal stability of the DRSI using ANOVA and Augmented Dickey–Fuller (ADF) tests.ResultsRefugia classifications and DRSI values for all areas were stable over time (1992–2011). The DRSI values were significantly lower than 1; that is, the density of rare species in the predicted refugia areas was higher than in non-refugia areas.ConclusionsWe assumed that patches of dense woody vegetation, determined by the upper 25% quantile of the NDVI, could be used to identify potential biodiversity refugia in dry environments. This assumption was validated by the DRSI results; it confirms that the local conditions in refugia support rare species.