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Fermentative digestion by ruminant livestock is one of the main ways enteric methane enters the atmosphere, although recent studies have identified that including red macroalgae as a feed ingredient can drastically reduce methane produced by cattle. Here, we utilize ecological modelling to identify suitable sites for establishing aquaculture development to support sustainable agriculture and Sustainable Development Goals 1 and 2. We used species distributions models (SDMs) parameterized using an ensemble of multiple statistical and machine learning methods, accounting for novel methodological and ecological artefacts that arise from using such approaches on non-native and cultivated species. We predicted the current distribution of two Asparagopsis species to high accuracy around the coast of Ireland. The environmental drivers of each species differed depending on where the response data was sourced from (i.e., native vs. non-native), suggesting that the length of time A. armata has been present in Ireland may mean it has undergone a niche shift. Subsequently, researchers looking to adopt SDMs to support aquaculture development need to acknowledge emerging conceptual issues, and here we provide the code needed to implement such research, which should support efforts to effectively choose suitable sites for aquaculture development that account for the unique methodological steps identified in this research.
James O’Mahony; Rubén De La Torre Cerro; Paul Holloway. Modelling the Distribution of the Red Macroalgae Asparagopsis to Support Sustainable Aquaculture Development. AgriEngineering 2021, 3, 251 -265.
AMA StyleJames O’Mahony, Rubén De La Torre Cerro, Paul Holloway. Modelling the Distribution of the Red Macroalgae Asparagopsis to Support Sustainable Aquaculture Development. AgriEngineering. 2021; 3 (2):251-265.
Chicago/Turabian StyleJames O’Mahony; Rubén De La Torre Cerro; Paul Holloway. 2021. "Modelling the Distribution of the Red Macroalgae Asparagopsis to Support Sustainable Aquaculture Development." AgriEngineering 3, no. 2: 251-265.
Phenological events play a key role modulating ecosystem services; however, the complex and interlinked nature of ecosystems means interactions among different taxa during phenological events can have consequences for the entire ecosystem. Currently, there is a lack of a unified criteria on the methodologies studying phenology and biotic interactions. We performed an extensive integrative review of works evaluating phenology and biotic interactions. We identified four broad categories of studies that have explored biotic interactions within phenology research: 1) spatial and temporal asynchronies, 2) biotic factors as covariates, 3) simulation studies, and 4) interaction indices. We found that spring phenology has received much more attention than any other seasons, while mutualistic and obligated interactions, as well as trophic interactions and networks have been explored more routinely than facilitation or competition. Authors tend to interpret co‐existence among species as biotic interactions without any direct measurement of these, particularly in spatial and temporal asynchrony studies, but this also occurs to a certain extent in all categories. We also found a lack of formal examination in most studies exploring phenological mismatches in response to climate change. We propose a conceptual framework for the inclusion of phenology in the study of biotic interactions that apportions research into the conceptualisation and modelling of biotic interactions. Conceptualisation explores phenological data, types of interactions, and the spatiotemporal dimensions, which all determine the representation for biotic interactions within the modelling framework, and the type of models that are applicable. Finally, we identify emerging opportunities to investigate biotic interactions in phenology research, including spatially and temporally explicit species distribution models as proxies for phenological events and the combination of novel technologies (e.g., acoustic recorders, telemetry data) to quantify interactions.
Rubén De La Torre Cerro; Paul Holloway. A review of the methods for studying biotic interactions in phenological analyses. Methods in Ecology and Evolution 2020, 12, 227 -244.
AMA StyleRubén De La Torre Cerro, Paul Holloway. A review of the methods for studying biotic interactions in phenological analyses. Methods in Ecology and Evolution. 2020; 12 (2):227-244.
Chicago/Turabian StyleRubén De La Torre Cerro; Paul Holloway. 2020. "A review of the methods for studying biotic interactions in phenological analyses." Methods in Ecology and Evolution 12, no. 2: 227-244.
Blue Carbon ecosystems such as mangroves, saltmarshes and seagrasses have been shown to sequester large amounts of carbon, and subsequently are receiving renewed interest from policy experts in light of climate change. Globally, seagrasses remain the most understudied of these ecosystems, with their total geographic extent largely unknown due to challenges in mapping dynamic coastal environments. As such, species distribution models (SDMs) have been used to identify areas of high suitability, in order to inform our understanding of where unmapped meadows may be located or to identify suitable sites for restoration and/or enhancement efforts. However, many SDMs parameterized to project seagrass distributions focus on physical and not anthropogenic variables (i.e., dredging, aquaculture), which can have negative impacts on seagrass meadows. Here we used verified datasets to identify the potential distribution of Zostera marina and Zostera noltei at a national level for the Republic of Ireland, using 19 environmental variables including both physical and anthropogenic. Using the Maximum Entropy method for developing the SDM, we estimated approximately 95 km2 of suitable habitat for Z. marina and 70 km2 for Z. noltei nationally with high accuracy metrics, including Area Under the Curve (AUC) values of 0.939 and 0.931, respectively for the two species. We found that bathymetry, maximum sea-surface temperature (SST) and minimum salinity were the most important environmental variables that explained the distribution of Z. marina and that high standard deviation of SST, mean SST and maximum salinity were the most important variables in explaining the distribution of Z. noltei. At a national level, we noted that it was primarily physical variables that determined the geographic distribution of seagrass, not anthropogenic variables. We unexpectedly modelled areas of high suitability in locations of anthropogenic disturbance (i.e., dredging, high pollution risk), although this may be due to the binary nature of SDMs capturing presence-absence and not the size and condition of the meadows, suggesting a need for future research to explore the finer scale impacts of anthropogenic activity. Subsequently, this research should foster discussion for researchers and practitioners working on sustainability projects related to Blue Carbon.
Ryan Hastings; Valerie Cummins; Paul Holloway. Assessing the Impact of Physical and Anthropogenic Environmental Factors in Determining the Habitat Suitability of Seagrass Ecosystems. Sustainability 2020, 12, 8302 .
AMA StyleRyan Hastings, Valerie Cummins, Paul Holloway. Assessing the Impact of Physical and Anthropogenic Environmental Factors in Determining the Habitat Suitability of Seagrass Ecosystems. Sustainability. 2020; 12 (20):8302.
Chicago/Turabian StyleRyan Hastings; Valerie Cummins; Paul Holloway. 2020. "Assessing the Impact of Physical and Anthropogenic Environmental Factors in Determining the Habitat Suitability of Seagrass Ecosystems." Sustainability 12, no. 20: 8302.
Opportunities to deploy digital technologies to research agendas and active learning in tertiary education are becoming more widespread. Despite this, many research techniques are still taught using traditional “pen-and-paper” methodologies. In this article, we report on a strategy for integrating mobile technology into our large (275+) module GG1015 Applied Geography, via the use of smartphones and the ESRI Collector for ArcGIS app. Focus groups identified three common themes among students in response to using this mobile technology in geographic research. Our findings suggest that digital technologies can enhance active learning in geography for all students. Similarly, such activities should not only be reserved for small groups, and can be up-scaled for larger class sizes, particularly when using new technologies. Finally, we illustrate how the use of technology in a group setting can foster teamwork, peer-to-peer learning, and positively reinforce the uptake of digital technology in geographic fieldwork.
Paul Holloway; Therese Kenna; Denis Linehan; Ray O’Connor; Helen Bradley; Bernadette O’Mahony; Robyn Pinkham. Active learning using a smartphone app: analysing land use patterns in Cork City, Ireland. Journal of Geography in Higher Education 2020, 45, 47 -62.
AMA StylePaul Holloway, Therese Kenna, Denis Linehan, Ray O’Connor, Helen Bradley, Bernadette O’Mahony, Robyn Pinkham. Active learning using a smartphone app: analysing land use patterns in Cork City, Ireland. Journal of Geography in Higher Education. 2020; 45 (1):47-62.
Chicago/Turabian StylePaul Holloway; Therese Kenna; Denis Linehan; Ray O’Connor; Helen Bradley; Bernadette O’Mahony; Robyn Pinkham. 2020. "Active learning using a smartphone app: analysing land use patterns in Cork City, Ireland." Journal of Geography in Higher Education 45, no. 1: 47-62.
Despite the global implementation of rock-rubble groyne structures, there is limited research investigating their ecology, much less than for other artificial coastal structures. Here we compare the intertidal ecology of urban (or semi-urban) rock-rubble groynes and more rural natural rocky shores for three areas of the UK coastline. We collected richness and abundance data for 771 quadrats across three counties, finding a total of 81 species, with 48 species on the groynes and 71 species on the natural rocky shores. We performed three-way analysis of variance (ANOVA) on both richness and abundance data, running parallel analysis for rock and rock-pool habitats. We also performed detrended correspondence analysis on all species to identify patterns in community structure. On rock surfaces, we found similar richness and abundance across structures for algae, higher diversity and abundance for lichen and mobile animals on natural shores, and higher numbers of sessile animals on groynes. Rock-pool habitats were depauperate on groynes for all species groups except for sessile animals, relative to natural shores. Only a slight differentiation between groyne and natural shore communities was observed, while groynes supported higher abundances of some ‘at risk’ species than natural shores. Furthermore, groynes did not differ substantially from natural shores in terms of their presence and abundance of species not native to the area. We conclude that groynes host similar ecological communities to those found on natural shores, but differences do exist, particularly with respect to rock-pool habitats.
Paul Holloway; Richard Field. Can Rock-Rubble Groynes Support Similar Intertidal Ecological Communities to Natural Rocky Shores? Land 2020, 9, 131 .
AMA StylePaul Holloway, Richard Field. Can Rock-Rubble Groynes Support Similar Intertidal Ecological Communities to Natural Rocky Shores? Land. 2020; 9 (5):131.
Chicago/Turabian StylePaul Holloway; Richard Field. 2020. "Can Rock-Rubble Groynes Support Similar Intertidal Ecological Communities to Natural Rocky Shores?" Land 9, no. 5: 131.
Savannas are extremely important socio-economic landscapes, with pastoralist societies relying on these ecosystems to sustain their livelihoods and economy. Globally, there is an increase of woody vegetation in these ecosystems, degrading the potential of these multi-functional landscapes to sustain societies and wildlife. Several mechanisms have been invoked to explain the processes responsible for woody vegetation composition; however, these are often investigated separately at scales not best suited to land-managers, thereby impeding the evaluation of their relative importance. We ran six transects at 15 sites along the Kalahari transect, collecting data on species identity, diversity, and abundance. We used Poisson and Tobit regression models to investigate the relationship among woody vegetation, precipitation, grazing, borehole density, and fire. We identified 44 species across 78 transects, with the highest species richness and abundance occurring at Kuke (middle of the rainfall gradient). Precipitation was the most important environmental variable across all species and various morphological groups, while increased borehole density and livestock resulted in lower bipinnate species abundance, contradicting the consensus that these managed features increase the presence of such species. Rotating cattle between boreholes subsequently reduces the impact of trampling and grazing on the soil and maintains and/or reduces woody vegetation abundance.
Thoralf Meyer; Paul Holloway; Thomas B. Christiansen; Jennifer A. Miller; Paolo D’Odorico; Gregory S. Okin; D’ Odorico; Okin. An Assessment of Multiple Drivers Determining Woody Species Composition and Structure: A Case Study from the Kalahari, Botswana. Land 2019, 8, 122 .
AMA StyleThoralf Meyer, Paul Holloway, Thomas B. Christiansen, Jennifer A. Miller, Paolo D’Odorico, Gregory S. Okin, D’ Odorico, Okin. An Assessment of Multiple Drivers Determining Woody Species Composition and Structure: A Case Study from the Kalahari, Botswana. Land. 2019; 8 (8):122.
Chicago/Turabian StyleThoralf Meyer; Paul Holloway; Thomas B. Christiansen; Jennifer A. Miller; Paolo D’Odorico; Gregory S. Okin; D’ Odorico; Okin. 2019. "An Assessment of Multiple Drivers Determining Woody Species Composition and Structure: A Case Study from the Kalahari, Botswana." Land 8, no. 8: 122.
Habitat selection analysis is a widely applied statistical framework used in spatial ecology. Many of the methods used to generate movement and couple it with the environment are strongly integrated within GIScience. The choice of movement conceptualisation and environmental space can potentially have long-lasting implications on the spatial statistics used to infer movement–environment relationships. The aim of this study was to explore how systematically altering the conceptualisation of movement, environmental space and temporal resolution affects the results of habitat selection analyses using both real-world case studies and a virtual ecologist approach. Model performance and coefficient estimates did not differ between the finest conceptualisations of movement (e.g. vector and move), while substantial differences were found for the more aggregated representations (e.g. segment and area). Only segments modelled the expected movement–environment relationship with increasing linear feature resistance in the virtual ecologist approach and altering the temporal resolution identified inversions in the movement–environment relationship for vectors and moves. The results suggest that spatial statistics employed to investigate movement–environment relationships should advance beyond conceptualising movement as the (relatively) static conceptualisation of vectors and moves and replace these with (more) dynamic aggregations of longer-lasting movement processes such as segments and areal representations.
Paul Holloway. Aggregating the conceptualization of movement data better captures real world and simulated animal–environment relationships. International Journal of Geographical Information Science 2019, 34, 1585 -1606.
AMA StylePaul Holloway. Aggregating the conceptualization of movement data better captures real world and simulated animal–environment relationships. International Journal of Geographical Information Science. 2019; 34 (8):1585-1606.
Chicago/Turabian StylePaul Holloway. 2019. "Aggregating the conceptualization of movement data better captures real world and simulated animal–environment relationships." International Journal of Geographical Information Science 34, no. 8: 1585-1606.
A better understanding of the current and future distributions of organisms is a critical facet of biodiversity conservation, and species distribution models (SDMs) are an important framework for achieving this. Despite the potential of SDMs to address an array of biogeography questions, they are subject to a number of conceptual and methodological uncertainties, such as the role of animal movement processes in determining geographic ranges. Movement processes have only recently been incorporated in SDMs, predominantly conceptualized as broad-scale movement processes (e.g., dispersal), while finer scale ambulatory movements of mobile animals (e.g., foraging) have been omitted. This research addresses this gap by developing a model that simulates the dynamic relationship between movement and biotic resources (e.g., food sources) for oilbirds (Steatornis caripensis) in Venezuela. This simulation represented the sustainability of an oilbird’s neighborhood, based on the connectivity, accessibility, and viability of its biotic resources. These dynamic variables improved the accuracy and ecological realism of the SDM projection compared to other commonly applied SDM scenarios. Integration of a Lagrangian (individual-level) form of movement in SDM with step-selection functions to parameterize biased-correlated random walks provides a new empirical framework for applying geographic context to simulation.
Paul Holloway. Simulating Movement-Related Resource Dynamics to Improve Species Distribution Models: A Case Study with Oilbirds in Northern South America. The Professional Geographer 2018, 70, 528 -540.
AMA StylePaul Holloway. Simulating Movement-Related Resource Dynamics to Improve Species Distribution Models: A Case Study with Oilbirds in Northern South America. The Professional Geographer. 2018; 70 (4):528-540.
Chicago/Turabian StylePaul Holloway. 2018. "Simulating Movement-Related Resource Dynamics to Improve Species Distribution Models: A Case Study with Oilbirds in Northern South America." The Professional Geographer 70, no. 4: 528-540.
Insect pests now pose a greater threat to crop production given the recent emergence of insecticide resistance, the removal of effective compounds from the market (e.g. neonicotinoids) and the changing climate that promotes successful overwintering and earlier migration of pests. As surveillance tools, predictive models are important to mitigate against pest outbreaks. Currently they provide decision support on species emergence, distribution, and migration patterns and their use effectively gives growers more time to take strategic crop interventions such as delayed sowing or targeted insecticide use. Existing techniques may have met their optimal usefulness, particularly in complex systems and changing climates. Machine learning (ML) arguably is an advance over current capabilities because it has the potential to efficiently identify the most informative time-windows whilst simultaneously improving species predictions. In doing so, ML is likely to advance the length of any integrated pest management opportunity when growers can intervene. As an example, we studied the migration of 51 species of aphids, which include some of the most economically important pests worldwide. We used a combination of entropy and C5.0 boosted decision trees to identify the most informative time windows to link meteorological variables to aphid migration patterns across the UK. Decision trees significantly improved the accuracy of first flight prediction by 20% compared to general additive models; further, meteorological variables that were selected by entropy significantly improved the accuracy by a further 3–5% compared to expert derived variables. Coarser (e.g. monthly) weather variables resulted in similar accuracies to finer (e.g. daily) variables but the most accurate model included multiple temporal resolutions with different period lengths. This combined resolution model alone highlights the ability of machine learning to accurately predict complex relationships between species and their meteorological drivers, largely beyond the experience of experts in the field. Finally, we identified the potential of these models to predict long-term first flight patterns in which machine learning attained equally high predictive ability as shorter-term forecasts. Whilst machine learning is a statistical advance, it is not necessarily a panacea: experts will be needed to underpin results with a mechanistic understanding, thus avoiding spurious relationships. The results of this study should provide researchers with an automated methodology to derive and select the most appropriate environmental variables when predicting ecological phenomena, while simultaneously improving the accuracy of such models.
Paul Holloway; Daniel Kudenko; James R. Bell. Dynamic selection of environmental variables to improve the prediction of aphid phenology: A machine learning approach. Ecological Indicators 2018, 88, 512 -521.
AMA StylePaul Holloway, Daniel Kudenko, James R. Bell. Dynamic selection of environmental variables to improve the prediction of aphid phenology: A machine learning approach. Ecological Indicators. 2018; 88 ():512-521.
Chicago/Turabian StylePaul Holloway; Daniel Kudenko; James R. Bell. 2018. "Dynamic selection of environmental variables to improve the prediction of aphid phenology: A machine learning approach." Ecological Indicators 88, no. : 512-521.
Paul Holloway; Jennifer A. Miller. Analysis and Modeling of Movement. Comprehensive Geographic Information Systems 2018, 162 -180.
AMA StylePaul Holloway, Jennifer A. Miller. Analysis and Modeling of Movement. Comprehensive Geographic Information Systems. 2018; ():162-180.
Chicago/Turabian StylePaul Holloway; Jennifer A. Miller. 2018. "Analysis and Modeling of Movement." Comprehensive Geographic Information Systems , no. : 162-180.
Paul Holloway; Jennifer A. Miller. A quantitative synthesis of the movement concepts used within species distribution modelling. Ecological Modelling 2017, 356, 91 -103.
AMA StylePaul Holloway, Jennifer A. Miller. A quantitative synthesis of the movement concepts used within species distribution modelling. Ecological Modelling. 2017; 356 ():91-103.
Chicago/Turabian StylePaul Holloway; Jennifer A. Miller. 2017. "A quantitative synthesis of the movement concepts used within species distribution modelling." Ecological Modelling 356, no. : 91-103.
Predicting potential and actual distributions of species has become an active research area in biogeography. Recent debate about the appropriate term for these models – “niche models” or “species distribution models” – stems from confusion over the original niche theory on which they are both based. While niche theory itself has evolved in ecology, there are three main niche concepts that have remained the most relevant for niche models. These concepts and how they inform the development and practice of niche modeling are summarized.
Jennifer A. Miller; Paul Holloway. Niche Theory and Models. International Encyclopedia of Geography: People, the Earth, Environment and Technology 2017, 1 -10.
AMA StyleJennifer A. Miller, Paul Holloway. Niche Theory and Models. International Encyclopedia of Geography: People, the Earth, Environment and Technology. 2017; ():1-10.
Chicago/Turabian StyleJennifer A. Miller; Paul Holloway. 2017. "Niche Theory and Models." International Encyclopedia of Geography: People, the Earth, Environment and Technology , no. : 1-10.
Paul Holloway; Jennifer A. Miller; Simon Gillings. Incorporating movement in species distribution models: how do simulations of dispersal affect the accuracy and uncertainty of projections? International Journal of Geographical Information Science 2016, 1 -25.
AMA StylePaul Holloway, Jennifer A. Miller, Simon Gillings. Incorporating movement in species distribution models: how do simulations of dispersal affect the accuracy and uncertainty of projections? International Journal of Geographical Information Science. 2016; ():1-25.
Chicago/Turabian StylePaul Holloway; Jennifer A. Miller; Simon Gillings. 2016. "Incorporating movement in species distribution models: how do simulations of dispersal affect the accuracy and uncertainty of projections?" International Journal of Geographical Information Science , no. : 1-25.
In this paper we explore relationships between bird species richness and environmental factors in New York State, focusing particularly on how spatial scale, autocorrelation and nonstationarity affect these relationships. We used spatial statistics, Getis-Ord Gi*(d), to investigate how spatial scale affects the measurement of richness “hot-spots” and “cold-spots” (clusters of high and low species richness, respectively) and geographically weighted regression (GWR) to explore scale dependencies and nonstationarity in the relationships between richness and environmental variables such as climate and plant productivity. Finally, we introduce a geovisualization approach to show how these relationships are affected by spatial scale in order to understand the complex spatial patterns of species richness.
Paul Holloway; Jennifer A. Miller. Exploring Spatial Scale, Autocorrelation and Nonstationarity of Bird Species Richness Patterns. ISPRS International Journal of Geo-Information 2015, 4, 783 -798.
AMA StylePaul Holloway, Jennifer A. Miller. Exploring Spatial Scale, Autocorrelation and Nonstationarity of Bird Species Richness Patterns. ISPRS International Journal of Geo-Information. 2015; 4 (2):783-798.
Chicago/Turabian StylePaul Holloway; Jennifer A. Miller. 2015. "Exploring Spatial Scale, Autocorrelation and Nonstationarity of Bird Species Richness Patterns." ISPRS International Journal of Geo-Information 4, no. 2: 783-798.
Movement in the context of species distribution models (SDMs) generally refers to a species’ ability to access suitable habitat. Movement ability can be determined by some combination of dispersal constraints or migration rates, landscape factors such as patch configuration, disturbance, and barriers, and demographic factors related to age at maturity, mortality, and fecundity. Including movement ability can result in more precise projections that help to distinguish suitable habitat that is or can be potentially occupied, from suitable habitat that is inaccessible. While most SDM studies have ignored movement or conceptualized it in overly simplistic ways (e.g. no dispersal versus unlimited dispersal), it is increasingly important to incorporate realistic information on movement ability, particularly for studies that aim to project future distributions such as climate change forecasting and invasive species applications. This progress report addresses the increasingly complex ways in which movement has been incorporated in SDM and outlines directions for further study.
Jennifer A Miller; Paul Holloway. Incorporating movement in species distribution models. Progress in Physical Geography: Earth and Environment 2015, 39, 837 -849.
AMA StyleJennifer A Miller, Paul Holloway. Incorporating movement in species distribution models. Progress in Physical Geography: Earth and Environment. 2015; 39 (6):837-849.
Chicago/Turabian StyleJennifer A Miller; Paul Holloway. 2015. "Incorporating movement in species distribution models." Progress in Physical Geography: Earth and Environment 39, no. 6: 837-849.
Paul Holloway; Jennifer A. Miller. Uncertainty Analysis of Step-Selection Functions: The Effect of Model Parameters on Inferences about the Relationship between Animal Movement and the Environment. Transactions on Petri Nets and Other Models of Concurrency XV 2014, 48 -63.
AMA StylePaul Holloway, Jennifer A. Miller. Uncertainty Analysis of Step-Selection Functions: The Effect of Model Parameters on Inferences about the Relationship between Animal Movement and the Environment. Transactions on Petri Nets and Other Models of Concurrency XV. 2014; ():48-63.
Chicago/Turabian StylePaul Holloway; Jennifer A. Miller. 2014. "Uncertainty Analysis of Step-Selection Functions: The Effect of Model Parameters on Inferences about the Relationship between Animal Movement and the Environment." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 48-63.