Search bioRxiv⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.05.06.592700

Productivity of the eelgrass biotope and the resilience of fisher-hunter-gatherers in prehistoric Denmark

Meadows, J.·Fischer, A.
View original source ↗Export metadata ↓

Abstract

In the shallow coastal waters of the Danish archipelago, eelgrass (Zostera marina) forms extensive and extremely productive stands, which support food webs that are isotopically distinct from those based on the primary productivity of marine phytoplankton. The isotopic signatures of eelgrass and phytoplankton food webs explain much of the variation in {delta}13C, {delta}15N and {delta}34S values in marine vertebrate remains recovered from prehistoric coastal sites in Denmark. Isotope data from human and dog remains reflect the overwhelming importance of the eelgrass biotope to the fisher-hunter-gatherer (FHG) subsistence during the Late Mesolithic Ertebolle epoch ([~]7400-5800 cal BP). By recognising eelgrass and pelagic resources as isotopically distinct, we obtain more accurate diet reconstructions and dietary 14C reservoir effect corrections for Ertebolle individuals. Longstanding research issues, including technology, palaeodemography, and the resilience and sustainability of the FHG economy need to be reassessed in view of the isotopic signature and productivity of the eelgrass biotope in Danish waters. Eelgrass productivity is also pertinent to comparisons between Ertebolle and Neolithic economies in Denmark, and between FHG adaptations in Denmark and neighbouring regions, and may be key to understanding the much-discussed delay in the transition to farming.

Source connections

Explore related subjects

ecologyPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Meadows, J., Fischer, A.. 2024-05-08. Productivity of the eelgrass biotope and the resilience of fisher-hunter-gatherers in prehistoric Denmark. https://doi.org/10.1101/2024.05.06.592700

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Operationalising the context in regenerative agriculture: decision-making and farm variability

Soil degradation is a widespread challenge that requires a broad response at the individual farm level. To ensure effectivity, the practices should be tailored to the farm context: land manager objectives and farm specific challenges. These have however been difficult to quantify. Here we demonstrate that a workable farm context can be created based on a value survey, open satellite and soil data, and published models for vegetation gross primary productivity and soil erosion. Based on the findings, despite individual differences, farmers value profitability and operational efficiency, but also biodiversity and soil health. At least the regenerative farmers surveyed also value working for the greater good more than maintaining tradition or power. In spite of wide differences in farm production orientation, we also found that each farm also had a broad variation in individual fields GPP. Most fields have a stable GPP level, which is either high or low, and that there is a 2-3-fold difference between the weakest and best producing fields indicating the potential for improving GPP by improving the growing conditions on currently weak fields. In addition, soil loss was found to be highly concentrated in critical source areas, where 10% of the field area contributed to 50% of the soil loss. Overall, open data can be linked to modelling workflows to rapidly produce a decision-making context for farmers. This facilitates benchmarking and co-learning as well as enables land managers and advisors to identify the farm context for planning effective responses to soil degradation.

ecology↗
bioRxiv2026

Fly, land, listen: Autonomous intermittent locomotion enables scalable low-noise drone ecoacoustic surveys

Ecoacoustic monitoring is enabling scientists and land managers to monitor and manage biodiversity more effectively and cost-efficiently in the face of human pressures and rapidly changing climates. Currently, most ecoacoustic surveys use manually deployed static sensors to record data, limiting the scale and reach of surveying efforts. Here we present a proof-of-concept autonomous drone platform that can use intermittent locomotion to conduct ecoacoustic surveys using an onboard sensor. Our custom prototype is able to fly, navigate, and avoid obstacles autonomously, land at a pre-determined location, record audio from an onboard microphone whilst static, before taking off and moving to the next sampling site. Autonomous navigation and operation enable greater sampling flexibility, reach, and scalability. Furthermore, by recording audio only whilst landed, noise from the drone's rotors does not mask signals or disturb animals, simplifying signal processing and downstream ecological analyses. We conducted trials in a scrubland habitat at the Knepp Estate in West Sussex, where our prototype demonstrated successful autonomous navigation and obstacle avoidance. Furthermore, we found that avian biodiversity data collected from the drone platform was comparable to that from traditional static acoustic sensor deployments, and that vocalisation patterns were not significantly impacted by the noise of the drone arriving or leaving a site. While scaled deployments of our technology would require further technical and regulatory challenges to be solved, our first demonstration of autonomous intermittent robotics-assisted ecoacoustic surveys lays the foundations for more cost-effective and far-reaching biodiversity surveys, with transformative potential for conservation, agricultural management, biosecurity, and more.

ecology↗
bioRxiv2026

Do higher-order moments improve inference of population dynamics?

Fitting mathematical models of population dynamics to microbial time-series data allows us to estimate the ecological processes and interactions taking place in the microbiome. Repeated experiments of microbial systems yield replicates which slightly differ from each other. Some of this variability arises due to the fact that births and deaths occur at random. Most prior work focuses on fitting a deterministic mathematical model to the average across replicates. We use a stochastic model to fit the variability to the observed variability across replicates. Using a simulation-driven approach, we study the conditions under which our approach allows us to infer a larger fraction of ecological parameters correctly. We observe a substantial improvement in parameter inference. Lastly, our Bayesian approach not only allows us to incorporate prior information about the system, but also provides a distribution of parameters which conveys some idea of the uncertainty of the estimates.

ecology↗