The cryptic impacts of invasion: functional homogenization of tropical ant communities by invasive fire ants

The diversity and distribution of traits in an ecological community shapes its responses to change and the ecosystem processes it modulates. This ‘functional diversity’, however, is not necessarily a direct outcome of taxonomic diversity. Invasions by exotic insects occur in ecosystems worldwide, but there is limited understanding of how they impact functional diversity. We present the first comprehensive trait‐based investigation of the impacts of an ant invasion, and the first incorporating intraspecific polymorphisms in species‐level functional diversity. The fire ant Solenopsis invicta is an invasive species with a global distribution. Focusing on invaded and uninvaded plots in tropical grasslands of Hong Kong, we investigated how the presence of S. invicta affects the diversity and distribution of ant species and traits within and across communities, the functional identities of communities, and functionally unique species. Using trait probability density functions, we built trait spaces for 29 different species, and scaled up these components to calculate functional diversity at community and landscape levels. We found that invasion had limited effects on species and functional richness but pronounced effects on functional composition. Specifically, invaded communities had fewer functionally‐unique individuals, and were characterized by species with narrower heads and bodies and shorter mandibles. Moreover, invaded communities showed substantially higher levels of functional redundancy (+56%) due to a clustering of trait values. Consequently, across the landscape, invaded communities displayed 23% less functional turnover than uninvaded communities despite showing comparable levels of taxonomic turnover – a result confirming theoretical predictions of the effects of high local functional redundancy. In sum, the presence of S. invicta alters the functional properties of multiple local communities selectively, resulting in functional homogenization across the landscape. The disparities between taxonomic and functional impacts of invasion highlight the need to consider how trait diversity across ecological scales shapes biodiversity and its responses to change

Knowledge Extraction From PV Power Generation With Deep Learning Autoencoder and Clustering-Based Algorithms

The unpredictable nature of photovoltaic solar power generation, caused by changing weather conditions, creates challenges for grid operators as they work to balance supply and demand. As solar power continues to become a larger part of the energy mix, managing this intermittency will be increasingly important. This paper focuses on identifying daily photovoltaic power production patterns to gain new knowledge of the generation patterns throughout the year based on unsupervised learning algorithms. The proposed data-driven model aims to extract typical daily photovoltaic power generation patterns by transforming the high dimensional temporal features of the daily PV power output into a lower latent feature space, which is learned by a deep learning autoencoder. Subsequently, the Partitioning Around Medoids (PAM) clustering algorithm is employed to identify the six distinct dominant patterns. Finally, a new algorithm is proposed to reconstruct these patterns in their original subspace. The proposed model is applied to two distinct datasets for further analysis. The results indicate that four out of the identified patterns in both datasets exhibit high correlation (over 95%) and temporal trends. These patterns correspond to distinct weather conditions, such as entirely sunny, mostly sunny, cloudy, and negligible power generation days, which were observed approximately 61% of the analyzed period. These typical patterns can be expected to be observed in other locations as well. Identified PV power generation patterns can improve forecasting models, optimize energy management systems, and aid in implementing energy storage or demand response programs and scheduling efficiently

Learning in Dynamic Data-Streams with a Scarcity of Labels

Analysing data in real-time is a natural and necessary progression from traditional data mining. However, real-time analysis presents additional challenges to batch-analysis; along with strict time and memory constraints, change is a major consideration. In a dynamic stream there is an assumption that the underlying process generating the stream is non-stationary and that concepts within the stream will drift and change over time. Adopting a false assumption that a stream is stationary will result in non-adaptive models degrading and eventually becoming obsolete. The challenge of recognising and reacting to change in a stream is compounded by the scarcity of labels problem. This refers to the very realistic situation in which the true class label of an incoming point is not immediately available (or will never be available) or in situations where manually labelling incoming points is prohibitively expensive. The goal of this thesis is to evaluate unsupervised learning as the basis for online classification in dynamic data-streams with a scarcity of labels. To realise this goal, a novel stream clustering algorithm based on the collective behaviour of ants (Ant Colony Stream Clustering (ACSC)) is proposed. This algorithm is shown to be faster and more accurate than comparative, peer stream-clustering algorithms while requiring fewer sensitive parameters. The principles of ACSC are extended in a second stream-clustering algorithm named Multi-Density Stream Clustering (MDSC). This algorithm has adaptive parameters and crucially, can track clusters and monitor their dynamic behaviour over time. A novel technique called a Dynamic Feature Mask (DFM) is proposed to ``sit on top’’ of these stream-clustering algorithms and can be used to observe and track change at the feature level in a data stream. This Feature Mask acts as an unsupervised feature selection method allowing high-dimensional streams to be clustered. Finally, data-stream clustering is evaluated as an approach to one-class classification and a novel framework (named COCEL: Clustering and One class Classification Ensemble Learning) for classification in dynamic streams with a scarcity of labels is described. The proposed framework can identify and react to change in a stream and hugely reduces the number of required labels (typically less than 0.05% of the entire stream)

Hybrid approaches to optimization and machine learning methods: a systematic literature review

Notably, real problems are increasingly complex and require sophisticated models and algorithms capable of quickly dealing with large data sets and finding optimal solutions. However, there is no perfect method or algorithm; all of them have some limitations that can be mitigated or eliminated by combining the skills of different methodologies. In this way, it is expected to develop hybrid algorithms that can take advantage of the potential and particularities of each method (optimization and machine learning) to integrate methodologies and make them more efficient. This paper presents an extensive systematic and bibliometric literature review on hybrid methods involving optimization and machine learning techniques for clustering and classification. It aims to identify the potential of methods and algorithms to overcome the difficulties of one or both methodologies when combined. After the description of optimization and machine learning methods, a numerical overview of the works published since 1970 is presented. Moreover, an in-depth state-of-art review over the last three years is presented. Furthermore, a SWOT analysis of the ten most cited algorithms of the collected database is performed, investigating the strengths and weaknesses of the pure algorithms and detaching the opportunities and threats that have been explored with hybrid methods. Thus, with this investigation, it was possible to highlight the most notable works and discoveries involving hybrid methods in terms of clustering and classification and also point out the difficulties of the pure methods and algorithms that can be strengthened through the inspirations of other methodologies; they are hybrid methods.Open access funding provided by FCT|FCCN (b-on). This work has been supported by FCT— Fundação para a Ciência e Tecnologia within the R &D Units Project Scope: UIDB/00319/2020. Beatriz Flamia Azevedo is supported by FCT Grant Reference SFRH/BD/07427/2021 The authors are grateful to the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/ MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021).info:eu-repo/semantics/publishedVersio

Epiphytic diatom community structure and richness is determined by macroalgal host and location in the South Shetland Islands (Antarctica)

The marine waters around the South Shetland Islands are paramount in the primary production of this Antarctic ecosystem. With the increasing effects of climate change and the annual retreat of the ice shelf, the importance of macroalgae and their diatom epiphytes in primary production also increases. The relationships and interactions between these organisms have scarcely been studied in Antarctica, and even less in the volcanic ecosystem of Deception Island, which can be seen as a natural proxy of climate change in Antarctica because of its vulcanism, and the open marine system of Livingston Island. In this study we investigated the composition of the diatom communities in the context of their macroalgal hosts and different environmental factors. We used a non-acidic method for diatom digestion, followed by slidescanning and diatom identification by manual annotation through a web-browser-based image annotation platform. Epiphytic diatom species richness was higher on Deception Island as a whole, whereas individual macroalgal specimens harboured richer diatom assemblages on Livingston Island. We hypothesize this a possible result of a higher diversity of ecological niches in the unique volcanic environment of Deception Island. Overall, our study revealed higher species richness and diversity than previous studies of macroalgae-inhabiting diatoms in Antarctica, which could however be the result of the different preparation methodologies used in the different studies, rather than an indication of a higher species richness on Deception Island and Livingston Island than other Antarctic localities

Integrated assessment of ecosystem connectivity and functioning: coastal forest avifauna of northeast Australia

The extraordinary diversity of species-environment relationships that occur across space and time can engender a deep curiosity of their mechanistic underpinnings. Moreover, the rapid rate of ecosystem change associated with anthropogenic and climatic pressures makes information regarding species' landscape and resource use ever more important. Without this information, we will be unable to effectively protect landscapes and their constituent species. The coastal ecosystem mosaic of northeast Australia, which is comprised of a high diversity of habitat types, provides a suitable region for investigating how species respond to heterogeneity in habitat and resource availability. The present thesis examined ecosystem functioning in heterogeneous coastal landscapes of northeast Australia for forest avifauna. An array of analytical approaches were employed to establish a comprehensive understanding: 1) spatial assessment to determine relationships between regional landscape connectivity and coastal forest bird assemblages, 2) isotopic assessment to evaluate the local foraging ecology of mangrove bird assemblages, and 3) nutrient assessment of cross-ecosystem connectivity provided by a migratory coastal forest bird species (i.e. the Pied Imperial-Pigeon (Ducula bicolor)). Within the coastal ecosystem mosaic, mangrove forests sit at the land-sea interface. Therefore, to effectively 'set the scene' I review how mangrove birds require and facilitate connectivity through their use of the broader coastal landscape. Next, to specifically assess regional landscape patterns and processes influencing northeast Australia's coastal forest avifauna, I surveyed the composition of bird assemblages in four of the major coastal forest types occurring throughout the region (i.e. Eucalypt, Melaleuca, mangrove, and rainforest). Following this, spatial patterns of habitat configuration within the coastal landscape (i.e. structural connectivity) were quantified to understand broad relationships between coastal forest bird assemblage composition and landscape heterogeneity at multiple spatial scales. Most bird species in coastal northeast Australia occurred in multiple forest types. Spatial assessment suggested that Melaleuca woodlands are a keystone structure that supports use of the entire coastal landscape mosaic by coastal forest generalist species. However, the species composition of mangrove bird assemblages was distinct relative to other coastal forest types. Therefore, to provide more detailed information regarding the response of coastal forest generalists and mangrove specialists to specific forest attributes, functionally connected forest networks were developed to assess the relative importance of forest area, availability, and connectivity to their compositional turnover. This revealed that mangrove specialists and coastal generalists differ in the forest attributes they require (i.e. area vs. availability) to maintain regional beta diversity. Understanding landscape pattern-process relationships that drive bird assemblage composition and turnover can inform the prioritization of regional-scale landscape features for protection. However, species' responses to local-scale spatiotemporal variability in resource availability may also play a role in these relationships. I used isotopic analysis to better understand the foraging ecology of coastal forest birds in a highly dynamic mangrove forest environment. This demonstrated that flexible and opportunistic foraging strategies were prevalent among coastal forest generalist species. However, specialized foraging strategies were employed by some species, primarily for resources that were uniquely available in mangrove forests (i.e. estuarine fish and crabs). Mobile species not only respond to landscape patterns and processes, but can also facilitate connectivity processes through their movement (e.g. nutrient transfer, pollination, genetic linking, etc.). To determine the implications of avian mobility for ecosystem functioning in northeast Australia, I focused on a migratory coastal forest bird species, the Pied Imperial-Pigeon (Ducula bicolor). Nutrient measurements demonstrated that Pied Imperial-Pigeons provide mainland-derived nutrient subsidies to island forests, highlighting their important role as an avian mobile-link species. The integrated analytical approach used in this thesis has provided insight to the complexity of coastal landscapes and their use by forest avifauna. This has broadened our understanding of coastal ecosystem functioning to include a hierarchy of ecosystem components that exist at local and regional scales. The ecosystem properties that emerge from interactions across coastal ecosystem components include: vegetative connectivity, compositional turnover, avian foraging strategy, and nutrient transfer. Results from this thesis can inform the holistic conservation and management strategies that are required to maintain coastal ecosystem functioning in regional northeast Australia

Quantifying responses of ecological communities to bioclimatic gradients

The biotic change along environmental gradients has been the subject of study for well over a century, forming one of the first tools to understand how environment shapes the species and ecosystems that occur. However, gradient studies have historically relied on limited observations on a single transect, limiting their inductive power. Here, I investigate how this limitation can be addressed. I present case studies to illustrate how next-generation transect studies can integrate observations from a wider range of observations of phenotypes, species and communities; together with observations from multiple taxa and gradients. Leaf carbon isotope data from bioclimatic gradients in China, South Australia and Western Australia are integrated to demonstrate a variety of species- and community-level responses to water availability, providing evidence against the previously asserted claim of a simple and universal response. Vegetation data from the same gradient is surveyed with two separate survey methodologies are co-analysed to demonstrate climate is the primary regional determinant of vegetation structure and composition in South Australia, while topographic and edaphic variables are important at a local scale. I find no evidence of ecological disjunctions that may indicate a threshold of vegetation change associated with climate shifts. Comparison of plant and ant species turnover on a spatial gradient suggested that ant communities are ca. 7.5 times more sensitive than plant assemblages to spatial change, providing evidence that future climate change may force community reorganisation and a decoupling of these two taxa, potentially disrupting important interactions and ecosystem function. Well-designed transect studies have the potential to help resolve long-standing questions around the modes of species adaptation to change, as well as improving our understanding of how climate change will shape ecosystems in to the futureThesis (Ph.D.) (Research by Publication) -- University of Adelaide, School of Biological Sciences, 201

Linking community ecology and biogeography: the role of biotic interactions and abiotic gradients in shaping the structure of ant communities.

Understanding what drives variation in species diversity in space and time and limits coexistence in local communities is a main focus of community ecology and biogeography. My doctoral work aims to document patterns of ant diversity and explore the possible ecological mechanisms leading to these patterns. Elucidating the processes by which communities assemble and species coexist might help explain spatial variation in species diversity. Using a combination of manipulative experiments, broad-scale surveys, behavioral assays and phylogenetic analyses, I examine which ecological processes account for the number of species coexisting in ant communities. Ants are found in most terrestrial habitats, where they are abundant, diverse and easy to sample (Agosti et al. 2000). Hölldobler and Wilson (1990) noted that competition was the hallmark of ant ecology, and we know that ant diversity varies along environmental gradients (Kusnezov 1957). Thus ants are an ideal taxon to examine the factors shaping the structure of ecological communities and how the determinants of community structure vary in space