Uporabnost nekaterih okoljskih spremenljivk za modeliranje potencialne razširjenosti izbranih vrst strig

magistrsko delo

Nejc Suban (Author), Rok Kostanjšek (Reviewer), Ivan Kos (Mentor), Ivan Kos (Thesis defence commission member), Rok Kostanjšek (Thesis defence commission member), Tomaž Skrbinšek (Thesis defence commission member)

Abstract

Modeliranje distribucije vrst vključuje široko paleto metodoloških pristopov in programskih orodij. V naši raziskavi smo z različnimi modeli preverjali vplive okoljskih spremenljivk na geografsko distribucijo strig (Chilopoda). Iz podatkovne baze CHILOBIO, ki združuje informacije o najdbah strig na območju Slovenije, smo izbrali primerno izpolnjene zapise, ki so bili rezultat talnih vzorčenj z vzorčnimi valji. Za kanonično korespondenčno analizo smo zbrali podatke o 18 najštevilčnejših vrstah na 29 vzorčnih lokacijah na območju Kočevske regije. Za izvedbo dveh generaliziranih linearnih modelov in modela, ki je bil izveden v okviru Bayesove statistike, smo uporabili podatke o prisotnostih vrste Sigibius anici na 73 vzorčnih lokacijah na območju Slovenije. Podatki o prisotnostih vrste so bili binarnega in kvantitativnega tipa, kjer smo z upoštevanjem števila vzorčnih enot na lokaciji dodali informacijo o vzorčnem naporu. Pred izvedbo modelov smo nabor okoljskih spremenljivk omejili s testiranjem korelacijskih odnosov med njimi ter z uporabo avtomatiziranih postopkov. Za kanonično korelacijsko analizo (CCA) smo izbrali spremenljivke:nadmorsko višino, teksturo tal, vsebnostjo organske snovi v zgornjem horizontu tal ter osončenostjo ob poletnem solsticiju; za ostale modele pa le prve tri od naštetih. Na ordinacijskih diagramih CCA so na gradientih okoljskih spremenljivk izmed preračunanih optimumov za posamezne vrste strig so opazno izstopali le nekateri. Optimumi večine ostalih vrst so bili zbrani v izhodiščnem območju diagrama, za kar je možnih več razlag. Rezultati ostalih treh modelov za vrsto Sigibius anici so nakazovali na slabo ujemanje modela s podatki. Izmed uporabljenih okoljskih spremenljivk smo v generaliziranem linearnem modelu (GLM) kot statistično pomembno določili le spremenljivko z vsebnostjo organske snovi v zgornjem horizontu tal. Ocene napovedi modelov (AUC) so se gibale okoli vrednosti 0,5, kar pomeni, da so bile napovedi vseh modelov blizu naključnim. Neuspešne napovedi modelov so lahko rezultat različnih virov podatkovnega šuma. V našem primeru so potencialni viri šuma nenatančno izračunane gostote osebkov, kršenje predpostavke o naključnosti vzorčenja in neustrezna ločljivost okoljskih spremenljivk, zaradi katere so pogoji na lokacijah vzorčenj nenatančno izraženi.

Keywords

podatkovna baza CHILOBIO;CCA;GLM;model distribucije vrste;testiranje pomembnosti spremenljivk;primerjava napovedi modelov;

Data

Language:	Slovenian
Year of publishing:	2018
Typology:	2.09 - Master's Thesis
Organization:	UL BF - Biotechnical Faculty
Publisher:	[N. Suban]
UDC:	595.62:311(043.2)
COBISS:	4653647
Views:	919
Downloads:	544
Average score:	0 (0 votes)
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Other data

Secondary language:	English
Secondary title:	Applicability of certain environmental variables for modeling the potential distribution of selected centipede species
Secondary abstract:	Species distribution modeling includes a variety of methodological approaches and software tools. In our research we used various models to test the influence of some environmental variables on the geographical distribution of centipedes (Chilopoda). We collected the centipede presence data from the CHILOBIO database that consists of information about centipede findings in the area of Slovenia. The data we used were gathered with a common soil sampling method with the use of sampling cylinders. We used data of 18 most numerous centipede species from 29 sampling locations in the area of Kočevje region to perform a canonical correspondence analysis (CCA). Furtherly we preformed two generalized linear models (GLM) and a model preformed in the framework of Bayesian statistic. We used presence data for centipede species Sigibius anici from 73 sampling locations throughout Slovenia. The presence data was binary and quantitative, where we took into account the sampling effort expressed as number of sampling units on a particular location. We tested the environmental variables for possible correlations before we used them in the models and we made a selection of the most influential variables with an automated selection protocols. We used variables as: terrain elevation, soil texture, content of organic matter in the topmost soil horizont and terrain insolation in summer solstice to execute CCA and only the first three variables listed to perform the other three models. The CCA ordination diagrams showed outlying of some species optimums for the chosen environmental gradients. However, the optimums for the most of the species were located at the center of the diagram, which can be due to various reasons. The results of Sigibius anici presence data modeling showed poor model fit. Among the three of the variables used in the GLM only the variable with, content of organic matter in the topmost soil horizont showed statistical significance. The model prediction estimates (AUC) were around 0,5, which means that the predictions were close to random. Unsuccessful model predictions could be due to some sources of data noise. In our case the most potential noises were: unprecisely calculated population densities, violation of predisposition about sampling randomness and inadequate resolution of environmental variables that failed to precisely express the conditions on the sampling locations.
Secondary keywords:	CHILOBIO database;CCA;GLM;model prediction comparison;species distribution model;testing the significance of variables;
Type (COBISS):	Master's thesis/paper
Study programme:	0
Embargo end date (OpenAIRE):	1970-01-01
Thesis comment:	Univ. Ljubljana, Biotehniška fak.
Pages:	X f., 69, [14] str.
ID:	10915566