Decoding Ten Years of Little Penguin Foraging: Bio-Logging Reveals Foraging Patterns with Implications for Climate Change Mitigation and Marine Spatial Planning
Abstract <jats:list list-type="order">Protected areas are a widely adopted resource management strategy for mitigating the consequences of global change and preserve functioning ecosystems. Long-term species monitoring programmes, aided by bio-logging technology, provide insights into the extent and spatial variation of areas occupied by wild animals and inform conservation and management. High-resolution GPS-acceleration data offer a more accurate understanding of animal behavior and area use, compared to location-based inference, emphasizing the significance of specific sites amid long-term climate change.We based our case-study on the largest colony of little penguins (Eudyptula minor) located at Phillip Island. Based on a ten-year bio-logging dataset (247 individual tracks), we combine high-resolution bio-logging data from GPS-accelerometer loggers with proxies for resource availability (e.g. Sea Surface Tenperature, thermocline, water turbidity). Using machine learning techniques and Generalized Additive Mixed Models, we quantify the environmental factors determining spatio-temporal variability in foraging effort (defined as hunting time) across different breeding seasons and stages.Little penguins increased their hunting time by reducing spatial displacement (shorter step length) and diving deeper, with a slower increase in hunting effort below 10 m depth. In relation to environmental conditions, penguins increased hunting effort in coastal areas with high turbid and productive waters and decreased effort with increasing Sea Surface Temperature. This gives insights into how these animals allocate effort differently according to shifting environmental conditions.Our analysis offers crucial long-term insights into little penguin area usage in the Bass Strait at sufficient spatial and temporal resolution for management and conservation planning. The Bass Strait is facing intense climatic and anthripogenic pressures, and the findings here on intensity of area usage and strategy shifting according to environmental conditions, are of great relevance for the marine spatial planning currently under development along the coast.Policy implications:High-resolution behavioral information obtained from bio-logging data using GPS-accelerometer tags provides understanding of how species shift strategies in response to environmental variability. This is vital to implement climate-adaptive conservation and management strategies. Given the growing availability of long-term accelerometer datasets within the ecological community, we recommend integrating such high-resolution information into conservation programs..
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Preprint| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
bioRxiv.org - (2023) vom: 20. Okt. Zur Gesamtaufnahme - year:2023 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Chimienti, Marianna [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.10.14.562344 |
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| PPN (Katalog-ID): |
XBI041225317 |
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| 520 | |a Abstract <jats:list list-type="order">Protected areas are a widely adopted resource management strategy for mitigating the consequences of global change and preserve functioning ecosystems. Long-term species monitoring programmes, aided by bio-logging technology, provide insights into the extent and spatial variation of areas occupied by wild animals and inform conservation and management. High-resolution GPS-acceleration data offer a more accurate understanding of animal behavior and area use, compared to location-based inference, emphasizing the significance of specific sites amid long-term climate change.We based our case-study on the largest colony of little penguins (Eudyptula minor) located at Phillip Island. Based on a ten-year bio-logging dataset (247 individual tracks), we combine high-resolution bio-logging data from GPS-accelerometer loggers with proxies for resource availability (e.g. Sea Surface Tenperature, thermocline, water turbidity). Using machine learning techniques and Generalized Additive Mixed Models, we quantify the environmental factors determining spatio-temporal variability in foraging effort (defined as hunting time) across different breeding seasons and stages.Little penguins increased their hunting time by reducing spatial displacement (shorter step length) and diving deeper, with a slower increase in hunting effort below 10 m depth. In relation to environmental conditions, penguins increased hunting effort in coastal areas with high turbid and productive waters and decreased effort with increasing Sea Surface Temperature. This gives insights into how these animals allocate effort differently according to shifting environmental conditions.Our analysis offers crucial long-term insights into little penguin area usage in the Bass Strait at sufficient spatial and temporal resolution for management and conservation planning. The Bass Strait is facing intense climatic and anthripogenic pressures, and the findings here on intensity of area usage and strategy shifting according to environmental conditions, are of great relevance for the marine spatial planning currently under development along the coast.Policy implications:High-resolution behavioral information obtained from bio-logging data using GPS-accelerometer tags provides understanding of how species shift strategies in response to environmental variability. This is vital to implement climate-adaptive conservation and management strategies. Given the growing availability of long-term accelerometer datasets within the ecological community, we recommend integrating such high-resolution information into conservation programs. | ||
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| 700 | 1 | |a Saraux, Claire |4 aut | |
| 700 | 1 | |a Ropert-Coudert, Yan |4 aut | |
| 700 | 1 | |a Kato, Akiko |4 aut | |
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