Browsing by Author "Altwegg, Andreas"

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    A population dynamics model for analysing the effect of rainfall seasonality on vegetation in the Karoo
    (2025) Govender, Serayen; Altwegg, Andreas
    The flora of the Cape Floristic Region is amongst the most diverse and unique on the planet, due mainly to the unique climate of this region. The effects of climate change are threatening many sensitive ecosystems around the world and so it is important to understand how factors of climate change may affect the Cape Floristic Region. This paper investigates the effect of changing rainfall seasonality on certain important species of plants in the Cape Floristic Region. The species are selected from different biomes and I focus on two growth forms, namely reseeder and resprouter. Data from an experiment conducted between two biomes in the Cape Floristic Region is used to model the growth of the two growth forms post- fire. Rainfall in this experiment is artificially manipulated on replicated plots at the two experiment sites. The population growth is modelled using state-space models, incorporating both an ecological process model and an observation model. This allows us to account for errors both in the observation of the data as well as in the natural variability in the biological state process that generated the data in order to account for both measurement and process error. My results suggest that increased summer rainfall in the Cape Floristic Region has a positive effect on the populations of reseeder species in both biomes and has little effect on the populations of resprouter species. A multivariate state-space model is also proposed to investigate the effects of interactions of species growth, within the replicated plots.
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    Fast and efficient approaches to large-scale occupancy models
    (2022) Clark, Allan Ernest; Altwegg, Andreas
    Bayesian occupancy models are important statistical tools that are used to investigate species range dynamics, species interactions as well as undercover key biological processes that drive occupancy (and detection) in a particular region. The results from these models are used to answer pressing conservation questions and are often used to develop responses to them. My original contribution to knowledge is the development of statistical methods that use detection-nondetection data commonly collected when undertaking occupancy modelling. The efficacy of these methods are investigated and applied to various South African detection-nondetection data sets. I developed two Variational Bayes approximations to the posterior distribution of the parameters of a single season site occupancy model that uses logistic or probit link functions to model the probability of species occurrence at sites and species detection probabilities. The results suggest that under certain circumstances, the variational distributions provide accurate approximations to the true posterior distributions of the parameters of the model when the survey occasions are as low as three and the accuracy of the approximations improves as the sampling occasions increase. Approximate methods could be implemented when the detection probability is at least 0.5 and when there are at least three sampling occasions. The link between logistic regression and occupancy modelling was exploited to develop a Gibbs sampler required to obtain posterior samples from the posterior distribution of the parameters of various occupancy model types (nonspatial, spatial and multi-species) when logit link functions are used to model the regression effects of the detection and occupancy processes. For the nonspatial occupancy model, the Gibbs sampling algorithm developed produces posterior samples that are identical to those obtained when using JAGS and Stan and that in certain cases the posterior chains mix faster than those obtained when using JAGS, stocc, and Stan. The Gibbs sampling algorithm developed for the multi-species occupancy model produces posterior samples that are identical to those obtained when using Stan, resulting in faster implementation times and a larger expected sampling rate than Stan. The algorithms are implemented in the R package Rcppocc and MSO which is freely available on GitHub (https://github.com/AllanClark/Rcppocc, https://github.com/ AllanClark/MSO).
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    Satellite change detection in the albany thicket biome
    (2025) Mahlasi, Craig; Moncrief, Glenn; Visser, Vernon; Altwegg, Andreas; Slingsby, Jasper
    The Albany Thicket Biome has been subject to widespread transformation, with as much as 63% of the biome being severely degraded. The primary land use activity responsible for much of the transformation of the biome has been pastoralism and commercial agriculture land expansion (Mills et al., 2005; Powell, 2008; Stickler and Shackleton, 2015). There are primarily four traditional remote sensing based change detection frameworks: algebra, transformations, classification, and advanced models (van Oort, 2007, Asokan and Anitha, 2019). While these methods are able to detect changes in bi-temporal datasets they are inherently limited in that they are based on the assumption that each pixel's spectral signature is a linear combination of the features on the corresponding physical surface (Salih et al., 2017, Sun et al., 2015). These methods also suffer from the propensity for false positives resulting from differences in atmospheric conditions, viewing angles and illumination and soil moistures between the two images; another limitation of these methods is the observation interval between the initial and post-change or successive observations are often weeks or up to years apart. which makes the detection of transient changes difficult. Finally they are unable to provide information on changes in land cover that allows for timely intervention by the authorities. Continuous change detection on the other hand uses all available and usable observations to detect changes. Continuous change detection classifiers allocate pixels throughout a time series to predefined classes using labelled training data. The majority of tools that seek to perform continuous land cover change detection have been developed for forests and thus tend to perform poorly in thicket ecosystems. This study aims to use multi-temporal satellite imagery to detect transformation of Albany Thicket in near-real time. The first chapter seeks to generate a Thicket transformation map documenting the changes in the Thicket biome between 2016 and 2019 and to produce an online application to visualise and interpret these changes. Chapter two focuses on developing a change detection protocol for identifying clearings of Thickets using Temporal Convolution Neural Networks and comparing it against the Continuous Change Detection and Classification (CCDC) algorithm. Finally chapter three sets out to develop a Domain adaptive Temporal Convolution Neural Network for continuous change detection in the Albany Thicket biome. The study concluded that using medium resolution satellite imagery changes in Albany Thicket vegetation can be reliably detected and discerned from changes in other land cover types. The ability to continuously detect changes using TempCNNs was shown to outperform a state of art algorithm namely, CCDC. Albany Thicket cover dynamics were shown to be embedded within geographical contexts and that geographical gradients in biophysical variables influence the contextual representations learned by the TempCNNs and therefore fusing TempCNNs with biophysical variables such as surface dryness information can improve their performance. Finally, it was shown that using meta-learning the TempCNN can be adapted to be robust in shifting domains by learning the most optimal parameter initializations that allow for capturing the invariant embeddings that facilitate generalisation across domains possible.
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    The IUCN red list for ecosystems: how does it compare to South Africa's approach to listing threatened ecosystems?
    (2021) Monyeki, Maphale Stella; Altwegg, Andreas; Slingsby, J A; Skowno, A L
    The publication of the International Union for Conservation of Nature (IUCN) Red List of Ecosystems (RLE) standards is an important development that has received broad acceptance globally. More than 100 countries across the globe including South Africa and Myanmar have adopted the IUCN RLE standards as their national framework for assessing the risk of ecosystem collapse. The strongest motivations for the alignment include: (i) elimination of confusion and reducing the administrative burden for maintaining multiple lists of threatened ecosystems, (ii) increased legitimacy of the ecosystem threat status assessment by basing them on a body of sound scientific literature, (iii) comparable assessments across different environments and countries across the globe, (iv) for the threatened national ecosystems to be recorded under the IUCN RLE registry. Furthermore, the IUCN Red List makes it easier for countries to secure funding from international donors to achieve national biodiversity conservation objectives, and address knowledge and data gaps through focused research. The IUCN RLE standards only became available after many countries including South Africa and Australia each independently tailor-developed national indicators or standards for assessing threats to ecosystems. The Ecosystem Threat statuses (ETS) standards are developed to aid biodiversity monitoring efforts, and many have progressed into the legislated national list of the threatened ecosystems. In South Africa, the gazetted list of threatened ecosystems is ratified to inform policy development and land-use planning tools that mainstream biodiversity considerations into economic development activities. Considering the strong links between the gazetted list of threatened ecosystems and many of the policy and spatial planning tools, the change and/or update to the IUCN RLE standards may disrupt conservation and land-use plans. In addition, South Africa has limited data on ecosystem integrity with to apply the full range of the IUCN RLE functional criteria which may lead to the risk of ecosystem collapse being underestimated. However, the country has comprehensive data on threatened plant species which in many cases contain detailed information on drivers of environmental degradation and biotic disruptions. In addition, extensive efforts have been made to link threatened species and the ecosystem types in which they occur. Such efforts enable the country to look at degradation through species lenses to better understand the degree of underestimation of ecosystem risk. Nonetheless, there is a need to interrogate and holistically understand the implications that may emanate from this shift, hence the importance of this study. This thesis was focused on assessing the origins and history of the IUCN and South Africa's approach to assessing threats to ecosystems. In chapter 1, I reviewed the key concepts including the scientific basis and criteria to understand the purpose and philosophy of the South Africa (SA) ETS and IUCN RLE frameworks. In Chapter 2, I compared and contrasted the SA ETS and IUCN RLE assessment outcomes of ecosystems susceptible to only spatial threats. Finally, in Chapter 3, I tested whether the IUCN RLE is a good proxy for the distribution of threatened species in South Africa. The results revealed that the IUCN RLE and SA ETS standards have overarching similarities (e.g. spatial and functional criteria) as they both share the common ancestry (IUCN Red List of Threatened Species). Equally, there are key differences (e.g. decision thresholds) that explain the misalignments in the ecosystem threat status between the two systems. Meanwhile, the quantitative results alluded that the proportions of matching assessment outcomes are high when the risk categories (Critically Endangered: CR and Endangered: EN versus Vulnerable: VU and Least Concern: LC) are split in accordance with their policy uptake (i.e. National Environmental Management Act (NEMA) EIA regulations) but relatively low per individual risk category. Furthermore, the results suggest that not all ecosystem types undergoing spatial declines entirely reflect the status of threatened plant species they contain. Many of these threatened plant species overlap with ecosystems at immediate risk of collapse (CR and EN). Such species will indirectly benefit from broad-scale conservation interventions that are informed by the list of threatened ecosystems. However, the majority of plant species threatened by either habitat loss and/or land degradation occur within the least threatened ecosystems. These species will not benefit from conservation responses informed by the gazetted national list of threatened because spatial declines within these ecosystems are considered to either be minimal or stable to trigger conservation response. Encouragingly, there are existing legal conservation tools such as stewardship programmes, Key Biodiversity Areas, and Critical Biodiversity Areas that allow threatened and unprotected ecological features including species to be strategically targeted for conservation responses. However, there is a need for South Africa to intensify efforts that ensure that these legal tools are implemented correctly and successfully to maximise conservation impacts and arrest biodiversity loss.
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    Using state-space time series analysis on wetland bird species to formulate effective bioindicators in the Barberspan wetland
    (2022) Edwards, Gareth; Altwegg, Andreas; Erni, Birgit
    The Coordinated Waterbird Count dataset (CWAC) is a dataset containing waterbird counts from wetlands across South Africa, going as far back as 1970. These data contain valuable information on population sizes and their trends over time. This information could be used more widely if it was more easily accessible to users. The aim of this dissertation is to bridge the gap between the CWAC dataset and the end users (for both experts and non-experts). In so doing the report also provides valuable insight into the state of wetlands in South Africa using various biodiversity indices, starting with Barberspan wetland as the pilot study site. A state-space time series model was applied to the waterbird counts in the CWAC dataset to determine waterbird population trends over the years. Statespace models are able to separate observation error from true population process error, thus providing a more accurate estimation of true population size. This qualifies state-space models as an ideal tool for population dynamics. The state-space model produced estimates of true population size for each waterbird per year. Three different indices were applied to the estimates, namely, exponentiated Shannon's index, Simpson's index and a modified Living Planet Index. These indices aggregate the count data to a measure of effective number of waterbirds in an ecosystem, a measure of evenness of an ecosystem, and an abundance index respectively. Using these three indices, in conjunction with each other, and individual waterbird species as bioindicators for various wetland traits, the end user is presented with a broad overview of the state of the Barberspan wetland. The implication of this research is beneficial to various wetland conservation organisations globally (AEWA, Aichi, RAMSAR) and locally (Working for Wetlands), as it provides valuable insight into the state of wetlands of South Africa. Furthermore, it helps managers at a local level in their decision making to enable more evidence-based approaches to protect South African wetlands and its waterbirds.