Habitat suitability modelling for red deer (Cervus elaphus L.) in South-central Slovenia with classification trees

Debeljak, Marko; Džeroski, Sašo; Jerina, Klemen; Kobler, Andrej; Adamič, Miha

doi:10.1016/s0304-3800(00)00411-7

Cited by 84 publications

(48 citation statements)

References 13 publications

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“…Alcuni autori hanno riscontrato una preferenza per i boschi in cui si realizza la compresenza di conifere e latifoglie, selezionati in funzione della proporzione di conifere in modo differente nelle stagioni. Durante l'inverno i boschi con elevata proporzione di conifere offrirebbero condizioni microclimatiche migliori (Debeljak et al, 2001) mentre d'estate boschi caratterizzati da una minor proporzione di resinose sarebbero preferiti dalle femmine in allattamento, in virtù della quantità e della qualità delle risorse trofiche rappresentata dalla disponibilità di erba e cespugli con rigenerazione (Clutton-Brock et al, 1982). Nella regione alpina, dove l'inverno rappresenta il principale fattore limitante, assumono particolare importanza le aree di svernamento, che si identificano con versanti esposti a sud e sono caratterizzate da aree boscate in grado di offrire riparo dal freddo e dal vento.…”

Section: Le Potenzialità Del Territorio Per Le Diverse Specieunclassified

Spring spotlight counts provide reliable indices to track changes in population size of mountain‐dwelling red deer Cervus elaphus

2016

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Informazioni legaliL'istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA) e le persone che agiscono per conto dell'Istituto non sono responsabili per l'uso che può essere fatto delle informazioni contenute in questo rapporto. ISPRA - AutoriElisabetta Raganella Pelliccioni, Francesco Riga e Silvano Toso Luca Pedrotti ha collaborato alla stesura del capitolo dedicato al monitoraggio delle popolazioni. Stefano Mattioli e Vito Mazzarone hanno collaborato alla stesura del paragrafo relativo al conteggio dei cervi maschi al bramito. Revisione dei testiStefano Focardi e Vittorio Guberti hanno fornito utili commenti per l'elaborazione del capitolo 2. Marco Apollonio, Riccardo Fontana, Sandro Lovari, Luca Mattioli, Stefano Mattioli, Vito Mazzarone, Piergiuseppe. Meneguz, Chiara Mercuriali, Sandro Nicoloso, Luca Pedrotti, Franco Perco, Maurizio Ramanzin e Maria Luisa Zanni hanno provveduto ad una revisione critica dei capitoli dedicati alla gestione venatoria ed al controllo delle popolazioni. Autori delle illustrazioniA.M. De Marinis ha elaborato le sagome degli Ungulati utilizzate nel testo e per la quarta di copertina. RingraziamentiSi ringraziano gli autori delle foto e delle schede di approfondimento (menzionati nel testo). Si ringrazia il Corpo Forestale dello Stato -Ufficio per la Biodiversità, Riserva Naturale dello Stato Bosco della Mesola per la foto del Cervo della Mesola realizzata da G. de Socio (ISPRA). .3.1 Il Capriolo italico ............................................................................................................ 10 1.3.2 Il Cervo sardo.................................................................................................................. 13 1.3.3 Il Cervo della Mesola ...................................................................................................... 15 1.3.4 Il Camoscio appenninico ................................................................................................. 17 1.3.5 Lo Stambecco................................................................................................................... 20 1.3.6 La Capra di Montecristo ................................................................................................. 22 1.3.7 Il Muflone (popolazione sarda PresentazioneL'evoluzione del popolamento degli Ungulati rappresenta senza dubbio uno dei più notevoli mutamenti del quadro faunistico italiano negli ultimi decenni. I profondi cambiamenti socio-economici, culturali e normativi intervenuti a partire dalla metà del secolo scorso hanno determinato condizioni favorevoli ad una progressiva riconquista degli antichi areali ed oggi gli Ungulati hanno rioccupato una parte consistente del territorio nazionale dopo che per almeno trecento anni l'azione dell'uomo aveva provocato la loro sostanziale scomparsa; essi sono tornati dunque a svolgere un importante ruolo strutturale e funzionale nelle biocenosi italiane, con indubbie ricadute positive anche per la conservazione di altri taxa, in particolare i grandi Carnivo...

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Section: Le Potenzialità Del Territorio Per Le Diverse Specieunclassified

Spring spotlight counts provide reliable indices to track changes in population size of mountain‐dwelling red deer Cervus elaphus

2016

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“…Because they provide detailed information on manifold output variables, individual tree growth models give answers to questions from many scientific fields. For example, individual tree growth models have been applied to predict future wood quality (e.g., Maguire et al, 1991;Sterba et al, 2006), to derive decision support with respect to rockfall protection (Brauner et al, 2005) and to assess the forest's habitat suitability (Debeljak et al, 2001;Marzluff et al, 2002;Vospernik and Reimoser, 2008). Due to their high relevance in academics as well as in decision support of forest practice, individual tree growth models have to be carefully evaluated and permanently updated.…”

Section: Individual Tree Growth Modelsmentioning

confidence: 99%

Possibilities and limitations of individual-tree growth models – A review on model evaluations

Vospernik

2017

Die Bodenkultur: Journal of Land Management, Food and Environment

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SummaryIndividual-tree growth models are the new standard for modeling growth and yield. Their main purpose is to simulate future forest management scenarios but they can also be used to predict wood quality, rockfall protection or habitat quality. Individual tree growth models may consist of different models but core models are diameter increment, height increment, crown ratio (often used as a predictor for increment) and mortality. The model differentiation is based on how these four models include tree age (size), competition and site. Four common growth simulators in Central Europe are BWIN, Moses, Prognaus and Silva. These four models are commonly deployed to simulate 30 years of growth, but a prospective application is the simulation of a whole rotation period (80−150 years). It is therefore crucial to understand the possibilities and limitations of these models by evaluating them. This review paper summarizes the statistical and emergent properties' evaluation results for these models. Statistical evaluations focus on individual models of a simulator, whereas the evaluation of emergent properties evaluates the entire simulator, by testing if the models conform to known principles of stand growth. Further, the meaning of these evaluation results for the development and improvement of individual-tree growth models is discussed. Keywords

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“…This technique has been used to identify current suitable habitat for specific taxa, model future species distributions including predicting invasive and rare species presence, and predict biodiversity of an area (Tan & Smeins 1996;Kampichler et al 2000;Cutler et al 2007;Olden et al 2008;. Common tools include Random Forest (Cutler et al 2007;Peters et al 2007), classification and decision trees (Ribic & Ainley 1997;Kobler & Adamic 2000;Bell 1999;Vayssièrs et al 2000;Debeljak et al 2001;Miller & Franklin 2002), neural networks (Mastrorillo et al 1997;Guégan et al 1998;Özesmi et al 2006;Brosse et al 2001;Thuiller 2003;Fielding 1999a;Dedecker et al 2004;Manel et al 2001;Segurado & Araújo 2004), genetic algorithms (D'Angelo et al 1995;Stockwell & Peters 1999;McKay 2001;Wiley et al 2003;Termansen et al 2006;Stockwell 1999;Peterson et al 2002), and Bayesian classifiers (Fischer 1990;Brzeziecki et al 1993;Guisan & Zimmermann 2000).…”

Section: Habitat Modeling and Species Distributionmentioning

confidence: 99%

Adoption of machine learning techniques in Ecology and Earth Science

Thessen

2016

Preprint

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The natural sciences, such as ecology and earth science, study complex interactions between biotic and abiotic systems in order to infer understanding and make predictions. Machine-learning-based methods have an advantage over traditional statistical methods in studying these systems because the former do not impose unrealistic assumptions (such as linearity), are capable of inferring missing data, and can reduce long-term expert annotation burden. Thus, a wider adoption of machine learning methods in ecology and earth science has the potential to greatly accelerate the pace and quality of science. Despite these advantages, machine learning techniques have not had wide spread adoption in ecology and earth science. This is largely due to 1) a lack of communication and collaboration between the machine learning research community and natural scientists, 2) a lack of easily accessible tools and services, and 3) the requirement for a robust training and test data set. These impediments can be overcome through financial support for collaborative work and the development of tools and services facilitating ML use. Natural scientists who have not yet used machine learning methods can be introduced to these techniques through Random Forest, a method that is easy to implement and performs well. This manuscript will 1) briefly describe several popular ML methods and their application to ecology and earth science, 2) discuss why ML methods are underutilized in natural science, and 3) propose solutions for barriers preventing wider ML adoption.

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Habitat suitability modelling for red deer (Cervus elaphus L.) in South-central Slovenia with classification trees

Cited by 84 publications

References 13 publications

Spring spotlight counts provide reliable indices to track changes in population size of mountain‐dwelling red deer Cervus elaphus

Spring spotlight counts provide reliable indices to track changes in population size of mountain‐dwelling red deer Cervus elaphus

Possibilities and limitations of individual-tree growth models – A review on model evaluations

Adoption of machine learning techniques in Ecology and Earth Science

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