PDF Population Viability in Plants: Conservation, Management, and Modeling of Rare Plants

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Shipped from UKs. Book Description Springer. Seller Inventory ING KG, Germany, Language: English. Brand new Book. Softcover reprint of the original 1st ed. Seller Inventory AAV Ships with Tracking Number! Buy with confidence, excellent customer service! Seller Inventory n. Items related to Population Viability in Plants: "Conservation, Christy A. Publisher: Springer , This specific ISBN edition is currently not available. Mouillot, D. PLOS Biology, 11 5 , e Potter, K.

Trends over time in tree and seedling phylogenetic diversity indicate regional differences in forest biodiversity change. Ecological Applications, 22 2 , — Rabotnov, T.

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K metodike nablyudeniya nad travyanistyimi rasteniyami na postoyannyih ploschadkah. Botanicheskiy zhurnal, 36 6 , — In Russian. Zhiznennyiy tsikl mnogoletnih travyanistyih rasteniy v lugovyih tsenozah. Rai, U. Minimum size for viable population and conservation biology.

Our Nature, 1, 3—9. Shaffer M. Population viability analysis and conservation policy. In: Beissinger S. Chicago Univ. Press, Chicago, — Uranov, A. Vozrastnoy spektr fitopopulyatsiy kak funktsiya vremeni energeticheskih volnovyih protsessov. Nauchnyie dokladyi vyisshey shkolyi. Biologicheskie nauki, 2, 7—33 in Russian. Williams, M. In: Sample V.

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A, Bixler R. Forest conservation and management in the Anthropocene: Conference proceedings. Zhivotovskiy, L.

Population viability analyses in COSEWIC status reports - juasumoche.cf

Ontogeneticheskie sostoyaniya, effektivnaya plotnost i klassifikatsiya populyatsiy rasteniy. Ecologiya, 1, 3—7 In Russian. Zhu, K. Failure to migrate: lack of tree range expansion in response to climate change. Global Change Biology, 18 3 , — Dual impacts of climate change: forest migration and turnover through life history. Global Change Biology, 20 1 , — Zlobin, Yu.

Populyatsionnaya ekologiya rasteniy: sovremennoe sostoyanie, tochki rosta [Population ecology of plants: current state, growing points]. Universitetskaya kniga, Sumyi in Russian. This work is licensed under a Creative Commons Attribution 4. ISSN User Username Password Remember me.

Current Issue. Hide Show all. Article Tools Print this article. Indexing metadata. How to cite item. Finding References. Email this article Login required. Email the author Login required. About Current navigation. We analysed relationships between the occurrence of S. We conducted our analysis using boosted regression trees which combine decision trees and a boosting algorithm with a form of logistic regression Friedman ; De'ath ; Elith et al.

For boosted regression trees, the probability P of S.

Population Viability in Plants

The optimal model was determined following the recommendations of Elith et al. Once the optimal combination of learning rate and tree complexity was found, model performance was evaluated using a fold cross-validation procedure with resubstitution. We calculated the response variance explained, the area under the receiver operator characteristic curve AUC , the overall accuracy, the omission error rate and the commission error rate based on the aggregated CV results.

We evaluated the reliability and validity of our models as fair 0. We then used the gbm library to derive the relative influence of each potential explanatory variable in the model and constructed partial dependence plots for the most influential variables Elith et al.

Finally, we used this optimal model to calculate probability of S. Analyses of combinations of tree complexity ranging from 3 to 7 and learning rate ranging from 0. The optimal model had a tree complexity of 5, a learning rate of 0. Model predictive deviance was 0.

The AUC score was 0. The commission false positive error rate was 6.

Recursive feature elimination tests showed that 45 variables could be removed from the model before the resulting predictive deviance exceeded the initial predictive deviance of the model with all variables. Of the four most influential model variables, three were climatic conditions and one was a landscape feature. Mean annual precipitation, mean annual minimum temperature and mean annual maximum temperature were the first, third and fourth most influential variables, contributing Mean elevation was the second most important variable contributing Abbreviations, descriptions and descriptive statistics for the climatic conditions and landscape features included in the final model.

Partial dependence plots indicated that S.

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Occurrences also were associated with landscape features characterized by i an altitude between 50 and 80 m Fig. Partial dependence plots for the 13 most influential variables included in the final model.