Modeling some plant species distribution against environmental gradients using multivariate regression models

Authors

  • Hafsa Bashir Bashir Department of Environmental SCiences, Fatima JInnah Women University, Rawalpindi
  • Sheikh Saeed Ahmad Department of Environmental SCiences, Fatima JInnah Women University, Rawalpindi
  • Muhammad Nawaz Nawaz Department of Environmental SCiences, BZU, Multan

Keywords:

Generalized Additive Model, NMS, Plant species distribution

Abstract

Relationship between plant species response and environmental gradients is becoming a challenging question in Ecology. A study was carried out along the roadsides of Wah Cantt. Data was collected using braun-blanquet approach which identified total 36 species belonging to 18 different families. GAM was employed for plotting non-linear relationship between plant species distribution against environmental gradients while NMS application was carried to highlight the stress in plant species distribution against selected environmental gradients (Zn2+, Cu2+, Fe2+, Mn2+, O.M, EC, pH). Results obtained from GAM had shown that when predictive variable have higher values, species distribution is favorable while some species remain unaffected by higher values of environmental gradients. Similarly, results obtained from NMS confirmed that stress rate was uniform for higher values of selected environmental gradients. The study will helpful in studying plant species response against environmental gradients and in proper management of herbaceous flora growing in Wah Cantt.

References

Ahmad, S. S. & Qurat-ul-Ann. (2011). Vegetation

classification in Ayubia national park, Pakistan using

Ordination methods. Pakistan Journal of Botany,

(5):2315- 2321.

Allan, B. & Drost, D. (2010). Garden cress in the Garden.

Utah State University Cooperative Extension.

Al-turki, R.A. (2004). A prelude to the study of flora the

flora of Jabal Fayfa in Saudi Arabia. Kuwait Journal of

Science and Engineering, 31:77- 145.

Austin, M. (2007). Species distribution models and

ecological theory: A critical assessment and some

possible new approaches (Review). Ecological Modeling,

:1–19.

Austin, M. P., Belbin, L., Meyers, J. A., Doherty, M. D.

& Luoto, M. (2006). Evaluation of statistical models used

for predicting plant species distributions: Role of artificial

data and theory. Ecological Modeling, 199:197- 216.

Austin, M.P. (2002). Spatial prediction of species

distribution: an interface between ecological theory

and statistical modeling. Ecological Modeling, 157(2

-3):101 -118.

Barry, S. C. & Welsh, A. H. (2002). Generalized additive

modeling and zero inflated count data. Ecological

Modeling, 157:179 -188.

Daviesa, K.W., Batesa, J.D, Millerb, R.F. (2007).

Environmental and vegetation relationships of the

Artemisia tridentata spp. wyomingensis alliance. Journal

of Arid Environments,70:478– 494.

Ehi-Eromosel, C.O., A.A. Adaramodu, A.A., Anake,

W.U., Ajanaku, C.O. & Edobor-Osoh, A. (2012).

Comparison of three methods of digestion for trace

metal analysis in surface dust collected from an E-waste

recycling site. Natural Sciences, 10(10):42- 47.

Ejrnaes, R. (2000). Can we trust gradients extracted by

detrended correspondence analysis. Journal of Vegetation

Science, 11:565- 572.

Frescino, T. S., Edwards, T. C. & Moisen, G. G. (2001).

Modeling spatially explicit forest structural attributes

using Generalized Additive Models. Journal of Vegetation

Science, 12:15–26.

Guisan, A. & Zimmermann, N. E. (2000). Predictive

habitat distribution models in ecology. Ecological

Modeling, 135:147–186 .

Guisan, A., Edwards, J.R. & Hastie, T. (2002).

Generalized linear and generalized additive models

in studies of species distributions, setting the scene.

Ecological Modeling, 157:89 -100.

Leathwick J.R., Austin M.P.(2001). Competitive

interactions between forest tree species in New Zealand’s

old-growth indigenous forests. Ecology, 82:2560–2573.

Lehmann, A., Overton, J.C. & Leathweak, J.R. (2002).

GRASP: generalized regression analysis and spatial

prediction. Ecological Modeling, 157:189 -207.

Lisar, S.Y.S., Motafakkerazad, R. Hossain, M.M. &

Rahman, I. M. M. (2012). Water stress in plants: causes,

effects and responses. Water Stress, Prof. Ismail Md.

Mofizur Rahman (Ed.): ISBN-9789-963-307-953-.In

Tech. DOI: 10.577239363/ Available from: https//www.

intechopen.com/book/water stress in plants-causeseffects-

and-responses.

McCune, B. & Grace, J. B. (2002). Analysis of

Ecological communities. MJM software design.

Miller, J. & Franklin, J. (2002). Modeling the distribution

of four vegetation alliances using generalized linear

models and classification trees with spatial dependence.

Ecological Modeling, 157:227–247.

Munoz, J. & Felicisimo, A.M. (2004). Comparison

of statistical methods commonly used in predictive

modeling. Journal of Vegetation Science, 15:285–292.

Riaz, T. & Javaid, A. (2009). Invasion of hostile alien

weed Parthenium Hysterophorus L. in Wah Cantt,

Pakistan. The Journal of Animal & Plant Sciences, 19(1):

-29.

Ter Braak, C.J.F & Smilauer, P. (2002). CANOCO

Reference manual and CanoDraw for Window user guide:

Software for Canonical community ordination (version 4.5),

Microcomputer power (Ithaca, NY, USA), pp 209- 215.

Tutz, G. & Kauermann, G. (2003). Generalized

linear random effects models with varying coefficients.

Computational Statistics and Data Analysis, 42 (1):13 -28.

Urooj, R., Ahmad, S.S., Ahmad, M.N. Ahmad, H.

& Nawaz, M. (2016). Ordination study of vegetation

analysis around wetland area: a case study of Mangla

Dam, Azad Kashmir, Pakistan. Pakistan Journal of

Botany, 48(1):115- 119.

Urooj, R., Ahmad, S.S., Ahmad, M.N. & Khan,

S. (2015). Ordinal classification of vegetation along

Mangla Dam, Mirpur, AJK. Pakistan Journal of Botany,

(4):1423- 1428.

Venn, S., Pickering, C. & Green, K. (2014). Spatial and

temporal functional changes in alpine summit vegetation

are driven by increases in shrubs and graminoids. AoB

PLANTS 6: plu008; doi:10.1093/aobpla/plu008.

Yordanov, I., Velikova, V. & Tsonev, T. (2003). Plant

responses to drought and stress tolerance. Bulgaria.

Journal of Plant Physiology, 187 -206.

Zhang, J., Jia, W., Yang, J. & Ismail, A.M. (2006). Role

of ABA in integrating plant responses to drought and salt

stress. Field Crop Research, 97:11- 119.

Downloads

Published

01-11-2017