Modeling some plant species distribution against environmental gradients using multivariate regression models
Keywords:
Generalized Additive Model, NMS, Plant species distributionAbstract
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.