Biology subjects

Connolly, R.

Publications and source records attributed to Connolly, R..

5 recordsLinked to original sources

bioRxiv2025

2000-year fish bone record reveals transition to commercial fisheries during climatic change

Animal bones from archaeological contexts can reveal the interplay between past environments and human societies. Resource acquisition shaped many aspects of past societies and influenced the development of trade networks and migration. Fish have been a cornerstone of human subsistence for millennia, yet the rise of commercial fishing and trade was complex. Here, we synthesised a database of [~]1.9 million zooarchaeological fish records spanning 2000 years across Europe. Using machine-learning of catch compositions alongside fish thermal tolerances, we show that fisheries became less local over time, with homogenisation coinciding with Little Ice Age-associated cooling, a period of documented resource scarcity, concurring with growing trade. Moreover, increased proportions of marine taxa and more specialist marine fisheries were observed in the preceding Medieval Climate Anomaly, to sustain concurrent urban and population growth. Enhanced use of marine protein buffered food insecurity, whilst signalling the transition from localised to trans-regional trade networks.

ecology↗

bioRxiv2021

Detecting change in interconnected ecosystem assets to inform indicators of condition in environmental economic accounts

There is an increasing need for long-term monitoring of ecosystems and their services to inform on-ground management and policy direction. The supply of many ecosystem services relies on connections that span multiple ecosystems. Monitoring the underlying condition of interconnected ecosystems, using established indicators, is therefore required to track effectiveness of past interventions and, ideally, identify impending change. Here we conduct performance testing of ecological indicators for a catchment-to-coast system with the aim of identifying the time-scales over which they respond to change. We chose a case-study of a coastal fishery in Northern Australia that exhibits strong catchment-to-coast connectivity, has long-term available data and is under threat from water resource development. We developed a novel approach to performance testing. Our model drew on state-space modelling to capture ecological dynamics, and structural equation modelling to capture covariation in indictors timeseries. We first quantified covariation among three established ecological indicators: pasture biomass, vegetation greenness and barramundi catch per unit effort. Covariation in the indicators was driven by river flow, with higher values of all indicators occurring in years with greater river flow. We then defined reference bounds for each indicator that accounted for natural variation in river flow. We predicted the emergence times for each indicator, as the time taken for each indicator to emerge from the background of natural variation. Emergence times quantified at 80% and higher confidence levels were >10 years in all cases. Past trends and current status of ecosystem service flows are often used by decision makers to directly inform near-term actions, particularly provisioning services (such as barramundi catch) due to their important contribution to regional economies. We found that the ecological indicators should be used to assess historical performance over decadal timespans, but not as short-term indicators of recent change. More generally, we offer an approach to performance testing of indicators. This approach could be useful for quantifying time-scales of ecosystem response in other systems where cross-ecosystem connections are important.

ecology↗

bioRxiv2021

Improved accuracy for automated counting of a fish in baited underwater videos for stock assessment

The ongoing need to sustainably manage fishery resources necessitates fishery-independent monitoring of the status of fish stocks. Camera systems, particularly baited remote underwater video stations (BRUVS), are a widely-used and repeatable method for monitoring relative abundance, required for building stock assessment models. The potential for BRUVS-based monitoring is restricted, however, by the substantial costs of manual data extraction from videos. Computer vision, in particular deep learning models, are increasingly being used to automatically detect and count fish at low abundances in videos. One of the advantages of BRUVS is that bait attractants help to reliably detect species in relatively short deployments (e.g. 1 hr). The high abundances of fish attracted to BRUVS, however, make computer vision more difficult, because fish often occlude other fish. We build upon existing deep learning methods for identifying and counting a target fisheries species across a wide range of fish abundances. Using BRUVS imagery targeting a recovering fishery species, Australian snapper (Chrysophrys auratus), we tested combinations of three further mathematical steps likely to generate accurate, efficient automation: 1) varying confidence thresholds (CTs), 2) on/off use of sequential non-maximum suppression (Seq-NMS), and 3) statistical correction equations. Output from the deep learning model was accurate at very low abundances of snapper; at higher abundances, however, the model over-predicted counts by as much as 50%. The procedure providing the most accurate counts across all fish abundances, with counts either correct or within 1 to 2 of manual counts (R2 = 93.4%), used Seq-NMS, a 55% confidence threshold, and a cubic polynomial corrective equation. The optimised modelling provides an automated procedure offering an effective and efficient method for accurately identifying and counting snapper in BRUV footage. Further testing is required to ensure that automated counts of snapper remain accurate in the survey region over time, and to determine the applicability to other regions within the distributional range of this species. For monitoring stocks of fishery species more generally, the specific equations will differ but the procedure demonstrated here would help to increase the usefulness of BRUVS, while decreasing costs.

ecology↗

bioRxiv2020

Deep learning for automated analysis of fish abundance: the benefits of training across multiple habitats

Environmental monitoring guides conservation, and is thus particularly important for coastal aquatic habitats, which are heavily impacted by human activities. Underwater cameras and unmanned devices monitor aquatic wildlife, but manual processing of footage is a significant bottleneck to rapid data processing and dissemination of results. Deep learning has emerged as a solution, but its ability to accurately detect animals across habitat types and locations is largely untested for coastal environments. Here, we produce three deep learning models using an object detection framework to detect an ecologically important fish, luderick (Girella tricuspidata). Two were trained on footage from single habitats (seagrass or reef), and one on footage from both habitats. All models were subjected to tests from both habitat types. Models performed well on test data from the same habitat type (object detection measure: mAP50: 91.7 and 86.9% performance for seagrass and reef, respectively), but poorly on test sets from a different habitat type (73.3 and 58.4%, respectively). The model trained on a combination of both habitats produced the highest object detection results for both tests (92.4 and 87.8%, respectively). Performance in terms of the ability for models to correctly estimate the ecological metric, MaxN, showed similar patterns. The findings demonstrate that deep learning models extract ecologically useful information from video footage accurately and consistently, and can perform across habitat types when trained on footage from the variety of habitat types.

ecology↗

bioRxiv2019

Automating the analysis of fish abundance using object detection: optimising animal ecology with deep learning

Aquatic ecologists routinely count animals to provide critical information for conservation and management. Increased accessibility to underwater recording equipment such as cameras and unmanned underwater devices have allowed footage to be captured efficiently and safely. It has, however, led to immense volumes of data being collected that require manual processing, and thus significant time, labour and money. The use of deep learning to automate image processing has substantial benefits, but has rarely been adopted within the field of aquatic ecology. To test its efficacy and utility, we compared the accuracy and speed of deep learning techniques against human counterparts for quantifying fish abundance in underwater images and video footage. We collected footage of fish assemblages in seagrass meadows in Queensland, Australia. We produced three models using a MaskR-CNN object detection framework to detect the target species, an ecologically important fish, luderick (Girella tricuspidata). Our models were trained on three randomised 80:20 ratios of training:validation data-sets from a total of 6,080 annotations. The computer accurately determined abundance from videos with high performance using unseen footage from the same estuary as the training data (F1 = 92.4%, mAP50 = 92.5%), and from novel footage collected from a different estuary (F1 = 92.3%, mAP50 = 93.4%). The computers performance in determining MaxN was 7.1% better than human marine experts, and 13.4% better than citizen scientists in single image test data-sets, and 1.5% and 7.8% higher in video data-sets, respectively. We show that deep learning is a more accurate tool than humans at determining abundance, and that results are consistent and transferable across survey locations. Deep learning methods provide a faster, cheaper and more accurate alternative to manual data analysis methods currently used to monitor and assess animal abundance. Deep learning techniques have much to offer the field of aquatic ecology.

ecology↗