Modelling multi-species co-occurrence patterns and processes

Date
2022-04
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Publisher
Stellenbosch : Stellenbosch University
Abstract
ENGLISH ABSTRACT: The structure of ecological communities is determined by the interplay among a range of processes, such as biotic interactions, abiotic filters, and disper- sal. Their effects can be detected by examining patterns of co-occurrence between different species. Using species-by-site matrices, null models that are based on permutations under constraints on row or column sums, have been widely used for comparing the observed values of co-occurrence met- rics (e.g., C-score and the natural metric) against null model expectations. This allows to detect significant signals of species association or dissocia- tion, from which the type of biotic interactions between species (e.g., facil- itative or antagonistic) can be inferred. In such a permutation-based null model test, the levels of co-occurrence between randomly paired species are often pooled to obtain a sampling distribution. However, the level of co-occurrence for three or more species are ignored, which could reflect functional guilds or motifs composed of multiple species within the com- munity. Null model tests without considering multi-species co-occurrence could often lead to false negatives (Type II error) in detecting non-random forces at play. Moreover, variations of co-occurrence have been explored by many models with covariates reflecting between-site environmental filters and distance decay of similarity. This, however, does not allow us to explic- itly explore the role of biotic interactions that could give rise to the observed co-occurrence patterns. An R software package for performing null model testing of multi-species co-occurrence patterns is currently lacking. This dis- sertation focuses on addressing all the above challenges. First, we propose a multi-species co-occurrence index that measures the number of sites jointly occupied by three or more species simultaneously, with the pairwise metric of co-occurrence as a special case for order two. We identify nine archetypes of species co-occurrence and show the majority of real communities con- form to six of these archetypes. Second, we develop a statistical model (gen- eralised B-spline modelling) that can use trait variations among species as a niche-based force and encounter rate as a neutral force to explain the la- tent interaction strength structure. This method decomposes each predictor into a linear combination of B-splines that allow to measure the local sen- sitivity of joint occupancy along the full range of the predictor’s variation. The generalised B-spline modelling can explain the observed co-occurrence and joint occupancy at different orders of joint occupancy. Finally, we im- plement the proposed multi-species co-occurrence index and the associated generalised B-spline modelling in the multi-species co-occurrence (msco) R package for null model testing of multi-species interactions and interference with covariates.
AFRIKAANSE OPSOMMING: Die struktuur van ekologiese gemeenskappe word bepaal deur die wissel- werking tussen ’n reeks prosesse, soos biotiese interaksies, abiotiese filters en verspreiding. Hul effekte kan opgespoor word deur patrone van sa- mekoms tussen verskillende spesies te ondersoek. Deur gebruik te maak van spesie-vir-plek matrikse, is nulmodelle wat gebaseer is op permuta- sies onder beperkings op ry- of kolomsomme, wyd gebruik om die waarge- nome waardes van mede-voorkomsmetrieke (bv. C-telling en die natuur- like metrieke) te vergelyk met nul model verwagtinge. Dit laat toe om betekenisvolle seine van spesieassosiasie of -dissosiasie op te spoor, waar- uit die tipe biotiese interaksies tussen spesies (bv. fasilitatief of antagonis- ties) afgelei kan word. In so ’n permutasie-gebaseerde nulmodeltoets word die vlakke van mede-voorkoms tussen ewekansige gepaarde spesies dik- wels saamgevoeg om ’n steekproefverspreiding te verkry. Die vlak van mede-voorkoms vir drie of meer spesies word egter geïgnoreer, wat funk- sionele gildes of motiewe wat uit veelvuldige spesies binne die gemeen- skap saamgestel is, kan weerspieël. Nulmodeltoetse sonder inagneming van multi-spesie mede-voorkoms kan dikwels lei tot vals negatiewe (tipe II- fout) in die opsporing van nie-ewekansige kragte wat speel. Boonop is vari- asies van mede-voorkoms deur baie modelle ondersoek met kovariate wat tussen-terrein omgewingsfilters en afstandsverval van ooreenkoms weer- spieël. Dit laat ons egter nie toe om die rol van biotiese interaksies wat aanleiding kan gee tot die waargenome mede-voorkomspatrone, eksplisiet te ondersoek nie. ’n R-sagtewarepakket vir die uitvoering van nulmodel- toetsing van multi-spesie mede-voorkomspatrone ontbreek tans. Hierdie proefskrif fokus daarop om al die bogenoemde uitdagings aan te spreek. Eerstens stel ons ’n multi-spesie mede-voorkoms-indeks voor wat die aan- tal terreine meet wat gesamentlik deur drie of meer spesies gelyktydig ge- okkupeer word, met die paarsgewyse metrieke van mede-voorkoms as ’n spesiale geval vir orde twee. Ons identifiseer nege argetipes van spesies saam-voorkoms en wys die meerderheid werklike gemeenskappe voldoen aan ses van hierdie argetipes. Tweedens ontwikkel ons ’n statistiese mo- del (algemene B-spline modellering) wat eienskapvariasies tussen spesies as ’n nisgebaseerde krag en ontmoetingstempo as ’n neutrale krag kan ge- bruik om die latente interaksiesterktestruktuur te verduidelik. Hierdie me- tode ontbind elke voorspeller in ’n lineêre kombinasie van B-splines wat dit moontlik maak om die plaaslike sensitiwiteit van gesamentlike beset- ting langs die volle reeks van die voorspeller se variasie te meet. Die al- gemene B-spline-modellering kan die waargenome mede-voorkoms en ge- samentlike besetting by verskillende ordes van gesamentlike besetting ver- duidelik. Laastens implementeer ons die voorgestelde multi-spesie mede- voorkoms-indeks en die gepaardgaande algemene B-spline-modellering in die multi-spesie mede-voorkoms (msco) R-pakket vir nulmodeltoetsing van multi-spesie interaksies en interferensie met kovariate.
Description
Thesis (PhD)--Stellenbosch University, 2022.
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