**Abstract**

Wildlife habitat is degrading worldwide due to myriads of biotic and abiotic reasons. The governments across the world countries are trying to halt the degradation through declaring protected areas (PAs) with potential wildlife habitats and strengthening conservation initiatives. These measures are expected to uphold the richness and diversity of the fodder yielding plants. But there is a dire necessity of information on composition and overall status of the fodder yielding plants for continuous monitoring of these habitats. Moreover, the potentiality of the protected areas can also be judged based on the composition and richness of fodder yielding plants. Having all these in mind, we assessed the composition and conservation status of the fodder yielding plants of all habit forms from three recognized protected areas named Chunati Wildlife Sanctuary, Dudhpukuria-Dhopachari Wildlife Sanctuary, and Madhupur National Park. The study indicated the presence of 306 fodder yielding plant species of all habit forms in the three studied protected areas. This chapter describes the composition, status, habit forms, and nature of occurrences of the wild fodder yielding plants which is expected to be highly helpful in wildlife habitat monitoring and undertaking specific measures for multiplication and conservation of fodder yielding plants.

**Keywords:** wildlife habitat, national park, forests, habitat monitoring, wildlife sanctuary


**Author details**

Nicholas M. Ponzio1 , Mili Mandal2 , Stella Elkabes3 , Pan Zhang4 , Junichi Sadoshima5 , Sayantani Basak6 , Peiyong Zhai7 and Robert Donnelly8

\*Address all correspondence to<EMAIL_ADDRESS>
1 Department of Pathology & Laboratory Medicine, University of Medicine and Dentistry of New Jersey – New Jersey Medical School and the Graduate School of Biomedical Sciences, Newark, USA

[4] DiCicco-Bloom, E., et al., *The Developmental Neurobiology of Autism Spectrum Disorder.*

Pro-Inflammatory Phenotype Induced by Maternal Immune Stimulation During Pregnancy

http://dx.doi.org/10.5772/53990

133

[5] Voineagu, I., et al., *Transcriptomic analysis of autistic brain reveals convergent molecular*

[6] Ashwood, P., S. Wills, and J. Van de Water, *The immune response in autism: a new fron‐*

[7] Ashwood, P., et al., *Associations of impaired behaviors with elevated plasma chemokines in*

[8] Ashwood, P., et al., *Altered T cell responses in children with autism.* Brain Behav Immun,

[9] Patterson, P.H., *Maternal infection and immune involvement in autism.* Trends Mol Med,

[10] Ashwood, P., et al., *In search of cellular immunophenotypes in the blood of children with*

[11] Goines, P.E. and P. Ashwood, *Cytokine dysregulation in autism spectrum disorders*

[12] Croonenberghs, J., et al., *Activation of the inflammatory response system in autism.* Neu‐

[13] Deykin, E. and B. MacMahon, *Viral exposure and autism.* Am J Epidemiol, 1979. 109: p.

[14] Hagberg, H. and C. Mallard, *Effect of inflammation on central nervous system develop‐ ment and vulnerability: review.* Current opinions in Neurology, 2005. 18: p. 117-123. [15] Hornig, M., et al., *An infection-based model of neurodevelopmental damage.* Proc Natl

[16] Lipkin, W. and Hornig M, *Microbiology and immunology of autism spectrum disorders.*

[17] Malek-Ahmadi, P., *Cytokines and etiopathogenesis of pervasive developmental disorders.*

[18] Pardo, C. and C. Eberhart, *The neurobiology of autism.* Brain Pathology, 2007. 17: p.

[19] Patterson, P., *Maternal infection: window on neuroimmune interactions in fetal brain devel‐*

[20] Brown, A.S., *Prenatal infection as a risk factor for schizophrenia.* Schizophr Bull, 2006.

[21] Brown, A.S. and E.J. Derkits, *Prenatal infection and schizophrenia: a review of epidemio‐*

*opment and mental illness.* Curr Opin Neurobiology, 2002. 12: p. 115-118.

*logic and translational studies.* Am J Psychiatry, 2010. 167(3): p. 261-80.

J Neurosci, 2006. 26(6): p. 6897-6906.

*autism.* PLoS One, 2011. 6(5): p. e19299.

ropsychobiology, 2002. 45: p. 1-6.

Acad Sci, 1999. 96: p. 12101-12107.

Med Hypotheses, 2001. 56(3): p. 321-324.

*tier for autism research.* J Leukoc Biol, 2006. 80(1): p. 1-15.

*autism spectrum disorders.* J Neuroimmunol, 2011. 232(1-2): p. 196-9.

*(ASD): Possible role of the environment.* Neurotoxicol Teratol, 2012.

Novartis Foundation Symposium, 2003. 251: p. 129-143.

*pathology.* Nature, 2011.

2011. 25(5): p. 840-9.

2011.

628-638.

434-447.

32(2): p. 200-2.

2 Department of Pathology & Laboratory Medicine, University of Medicine and Dentistry of New Jersey – Graduate School of Biomedical Sciences, Newark, USA

3 Department of Neurological Surgery, University of Medicine and Dentistry of New Jersey – New Jersey Medical School and the Graduate School of Biomedical Sciences, Newark, USA

4 Department of Pathology & Laboratory Medicine, University of Medicine and Dentistry of New Jersey – New Jersey Medical School, Newark, USA

5 Department of Cell Biology & Molecular Medicine, University of Medicine and Dentistry of New Jersey – New Jersey Medical School and the Graduate School of Biomedical Sciences, Newark, USA

6 Department of Pathology & Laboratory Medicine, University of Medicine and Dentistry of New Jersey – New Jersey Medical School, Newark, USA

7 Department of Cell Biology & Molecular Medicine, University of Medicine and Dentistry of New Jersey – New Jersey Medical School, Newark, USA

8 Department of Pathology & Laboratory Medicine, University of Medicine and Dentistry of New Jersey – New Jersey Medical School and the Graduate School of Biomedical Sciences, Newark, USA


**7. Consequences and effects of climate change over the growing season period**

Plants, whether natural plants or crops and horticultural plants, to complete their growth and development, need to spend a period with optimum temperature and humidity conditions.

Various definitions and criteria have been proposed to determine the growing season, the first and last frost has been used in many studies [19–22].

The length of the growing season, the length of the days between the last hard frost and the first hard frost in spring are also defined. Hard frost refers to a minimum daily temperature that 50% of plants exposed to it are destroyed [23].

The length of the growing season, the length of time between the last killing frost in spring and the first killing frost in the fall are also defined (U.S. Army Agronomists, 1987). Killing frost is 28°F (−2.2°C) or colder [24].

According to the definition provided by the variation group of the Climatology Commission of the World Meteorological Organization, in the northern hemisphere, the interval between the first period after July 1st (10th of Tir), which is at least 6 consecutive days, the average daily temperature is more than 5°C and the first 6-day period with an average daily temperature of less than 5°C (in autumn) is considered as the growing period. In the southern hemisphere, this interval is considered from January 1st [25].

The length of plant growing season varies not only in terms of each plant species, but also there are differences in one species. Determining the growing season in each region has an effective role in selecting crops and cultivars and determining planting time and other crop decisions.

Many phenological, meteorological, and satellite researches report an increase in the length of the growing season due to rising temperatures in the northern regions during the twentieth century [26].

Chemielewki [27] investigated the relationship between annual and periodic changes of the growing season in Europe with air temperature changes, the results show that in Europe during 1989–1998 with an increase of 0.8°C the average temperature in the last decade, was occurred 8 days earlier at the beginning of the growing season. Research conducted in the United States [28] Australia, China [29], also shows a decrease in the number of freezing days at high and middle latitudes of the northern hemisphere and an increase in the length of the growing period compared to the twentieth century. Regarding the effects of climate change on growing period, researches such as temporal and spatial variation of phenological seasons in Germany from 1951 to 1996 [30], changes in the growing season in the last century [26] can be noted.

One of the important consequences of climate change is the change during the growing season, which follows the change in the beginning and termination of zero temperature and temperature of 5°C (thermal base of cold crops). Changes in the time and length of the growing season may not only have far-reaching consequences for plant and animal ecosystems, but a steady increase in the length of the growing season may lead to a long-term increase in carbon storage and changes in vegetation cover that may affect the climate system. The decrease in the length of the growing season leads to changes in the cultivation calendar and a decrease in yields of crops that have not yet fully reached their maturity and final growth, while increasing the length of the growing season may provide more opportunities for earlier cultivation, ensuring final growth and maturity, and even the possibility of further harvesting (if water is available) [30].


**3.1 General benefits**

The functions and benefits of green space are plenty in number. Green Space symbolize peace, minimal stress and a cleaner environment for many people, which are considered as important factors in making a city livable, pleasant and attractive for its citizens and guests. The primary functions performed by Urban Green Space are given in **Table 1** below.

The significant benefits obtained through urban green space, are categorized under two perspectives tangible and intangible. Tangible benefits include ecological benefits (absorbing pollutants, clean air, improve the urban climate, etc.), planning benefits (network linking, improving accessibility, noise barrier, visual screening and safe walking and recreational) and economic benefits (Comparing with other landscape elements, neighborhood parks induced the heaviest investment intention in the home buying behavior).

Likewise, the intangible benefits include social benefits (Opportunities for people of all age groups to interact, encourages volunteerism, promotes stewardship, help individuals with disabilities, enhance cultural life by providing venues for local

Providing medical resources

Spiritual and religious information Cultural and artistic inspiration

Scientific and educational information sources

Esthetic information

Providing Raw material for some of the human activity

3 Production function Recharging the ground water table

*Role of Green Spaces for Maintaining Well-Being in Residential Community Development DOI: http://dx.doi.org/10.5772/intechopen.97681*

#### **Table 1.**

*Showing various functions of green spaces.*

*Source: Rao, P, 2017 from Ph.D thesis of SPA, Bhopal.*

4 Information function/ dissemination

festivals, civic celebrations, and theatrical performances, reduces crime, strengthens community by reflecting the different communities they serve and meeting their varying needs) as shown in **Figure 1** were discussed by Grahn P and Stigsdotter U A, [2] P Jain, [3].

## **4. Understanding green space usability and user characteristics (residential community)**

Both green space and housing research are broad fields of study. The paradigm shift observed understands the relationship between green space and its usability in the residential area. In green space research, there is an increasing number of studies that concern people's experience and use, but it has been pointed out that more research is needed due to increasing urbanization issues like safety and security [4, 5]. Many previous models and theories substantiate that the usability of green space is directly associated with its accessibility. The usability of green space differs due to different spatial arrangements within the residential communities and variation in the provision of it with respect to quantity and quality. Physical accessibility and the connection is a major research area in the field of green space use in the residential area because people are associated with the green space usage. Hence, place accessibility and people accessibility represent different research foci in accessibility research [6]. While by 'place accessibility' researchers conceive geographical access as a location attribute, with people accessibilityresearchers, on the other hand, focus on the ability of different groups of individuals to access the

**Figure 1.**

*The benefits of green at community level. Source: Grahn P and StigSdotter U A, 2003 [2].*

open space [6]. This suggests, that place and people accessibility are different representational frameworks that provide different operational definitions for accessibility.


*5.2. Error Analysis*

We also analyze the miss-classification errors for the proposed CNN model. To this end, we identified three types of errors, namely *zero-error*, *one-to-one*, and *many-to-many errors*.

The *zero-type* mistake happens when a model predicts no appliance is running while there is at least one active appliance. It can be observed from Figure 11 that the number of zero-type mistakes is very low for the three feature types with decomposed-current distance making no such type of error.

**Figure 11.** (**a**) Distributions of type errors the model makes. (**b**) Number of correct predictions for single, double and triple activations.

On the other hand, the *one-to-one* is the type of error that the model makes when there is only one active appliance running. We see from Figure 11 that the V-I binary image makes 45 one-type errors while the current based features reduce this to seven for the decomposed current, and six for the decomposed current distance feature. The low error rate when one appliance is running can be attributed to the high number of single activations, over 50%, as presented in Figure 7a. It further shows

the effectiveness of the proposed CNN multi-label learning in recognizing individually operating appliances, with over 98% accuracy, as shown in Figure 11b.

The *many-to-many errors* are confusions that a model makes when several appliances are active. Since the PLAID dataset used in our experiment consists of up to three simultaneous active appliances, we further categorized *many-to-many errors* into *single*, *double*, or *complete-error*. A *single* error occurs when a model confuses only one appliance when two or three appliances are active, whereas in *double* fault, the model confuses two appliances when three appliances are active. The *complete-error* is the case when the model produces incorrect predictions for all the active appliances. It can be inferred from Figure 11 that the proposed CNN multi-label model makes a higher number of *double* errors for the three input feature types used. This is likely to be caused by the fewer numbers of samples with more than two appliances running simultaneously at about 5.8%, as depicted in Figure 7a.