Daniela J Shebitz

Dr. Daniela J Shebitz

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Academic Career Length

22Years (2005-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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30 June 2006 in Science
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WGWarren Gold
Warren Gold
KEKern Ewing
Kern Ewing
John BanksJohn Banks
John Banks
MGMartha Groom
Martha Groom
THTom Hinckley
Tom Hinckley
DSDaniela J Shebitz
Daniela J Shebitz
Faculty and students at the University of Washington forge interdisciplinary partnerships with the regional community to restore damaged habitats.
3 November 2021 in Earth
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KNKikombo Ilunga Ngoy
Kikombo Ilunga Ngoy
FQFeng Qi
Feng Qi
DSDaniela J Shebitz
Daniela J Shebitz
This study analyzed the changes of land use and land cover (LULC) in New Jersey in the United States from 2007 to 2012. The goal was to identify the driving factors of these changes and to project the five-year trend to 2100. LULC data was obtained from the New Jersey Department of Environmental Protection. The original 86 classes were reclassified to 11 classes. Data analysis and projection were performed using TerrSet 2020. Results from 2007 to 2012 showed that the rate of LULC changes was relatively small. Most changes happened to brush/grasslands, mixed forest lands, farmlands and urban/developed lands. Urban/developed lands and the mixed-forest cover gained while farmlands lost. Using a multi-layer perceptron–Markov chain (MLP–MC) model, we projected the 2015 LULC and validated by actual data to produce a 2100 LULC. Changes from 2012 to 2100 showed that urban/developed lands, as well as brush/grasslands, would continue to gain, while farmlands would lose, although the projected landscape texture would likely be identical to the 2012 landscape. Human and natural factors were discussed. It was concluded that the MLP–MC model could be a useful model to predict short-term LULC change. Unexpected factors are likely to interfere in a long-term projection.
1 January 2005 in Journal of Ecological Anthropology
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DSDaniela J Shebitz
Daniela J Shebitz
This paper focuses on the benefits of incorporating traditional ecological knowledge into the field of ecological restoration. Case studies on indigenous use of sweetgrass in New York State, U.S.A (Haudenosaunee Nation), and beargrass in Washington State, U.S.A (Quinault and Skokomish Nations), are presented. Both studies focus on the restoration of basketry plants by incorporating indigenous knowledge of changes in abundance of culturally significant plants; knowledge of sites appropriate for restoration of culturally significant plants; and knowledge of land management methods to restore species and/or habitats. Open-ended, semi-formal, and informal interviews were conducted with indigenous consultants familiar with the plant and/or habitat of interest. Traditional knowledge of appropriate restoration sites was used in a field experiment to re-establish sweetgrass in an area from which it is believed to have been extirpated. Traditional knowledge of anthropogenic burning was used to reintroduce fire in low-elevation beargrass habitats to manage both the resource and its environment. By incorporating traditional knowledge with published information on sweetgrass biology, it was found that two potential factors influencing its population in cultural gathering sites are unsustainable harvesting and the absence of controlled burns.
17 April 2013 in ISRN Ecology
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DSDaniela J Shebitz
Daniela J Shebitz
WEWilliam Eaton
William Eaton
Succession following deforestation in Neotropical forests has been investigated extensively, yet rarely have studies connected nutrient dynamics with vegetation. This study was conducted in lowland wet forests of Maquenque, Costa Rica. The objectives were (1) to compare carbon (C), nitrogen (N), and phosphorus (P) characteristics and understory vegetation diversity between regenerating forests and primary forests; and (2) to use these variables to evaluate P. macroloba’s successional role. Four 300 m2 plots were established in primary and secondary forests where P. macroloba was the dominant N-fixing tree. Soil and vegetation data were collected from 2008 to 2010. Values of indicators of C, N, and P cycle activity were generally greater in primary than in secondary forest soils. Efficiency of organic C use and the relative contribution of respiration and organic C to soil biomass were also greater in the primary forest. These trends corresponded with greater richness, biomass, and cover of total and leguminous plant species, greater volume of P. macroloba in primary stands, and greater density of P. macroloba in secondary stands. As cleared regions of former primary forest regenerate, P. macroloba is the important dominant N-fixing tree and a critical driver of C, N, and P recuperation and ecosystem recovery.
26 September 2020 in Environments
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KNKikombo Ilunga Ngoy
Kikombo Ilunga Ngoy
DSDaniela J Shebitz
Daniela J Shebitz
We used the Climate Change and Adaptation Modeler (CCAM), a Terrset software toolset, to project the effects of global climate change on crops in New Jersey. We selected two scenarios—A1FI-MI and B1TME. We found that temperatures will increase by the end of this century compared to 1981–2010 normal temperatures baseline downloaded from PRISM. The temperature increase will vary from 3 to 6 °C depending upon the scenario while the precipitation remains relatively the same. These changes will negatively affect the suitability of many economically valuable crops in New Jersey including blueberry, cranberry, squash, sweet corn and tomato. Many crops that are highly or very suitable will move into marginal or very marginal categories.
15 December 2019 in Environments
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KNKikombo Ilunga Ngoy
Kikombo Ilunga Ngoy
DSDaniela J Shebitz
Daniela J Shebitz
The increasing spread of invasive plants has become a critical driver of global environmental change. Once established, invasive species are often impossible to eradicate. Therefore, predicting the spread has become a key element in fighting invasive species. In this study, we examined the efficiency of a logistic regression model as a tool to identify the spatial occurrence of an invasive plant species. We used Eragrostis curvula (Weeping Lovegrass) as the dependent variable. The independent variables included temperature, precipitation, soil types, and the road network. We randomly selected 68 georeferenced points to test the goodness of fit of the logistic regression model to predict the presence of E. curvula. We validated the model by selecting an additional 68 random points. Results showed that the probability to successfully predict the presence of E. Curvula was 82.35%. The overall predictive accuracy of the model for the presence or absence of E. Curvula was 80.88%. Additional tests including the Chi-square test, the Hosmer–Lemeshow (HL) test, and the area under the curve (AUC) values, all indicated that the model was the best fit. Our results showed that E. curvula was associated with the identified variables. This study suggests that the logistic regression model can be a useful tool in the identification of invasive species in New Jersey.