Unprecedented quantities of the brown floating macroalgae Sargassum have been recorded in the tropical North Atlantic Ocean. However, the environmental factors affecting holopelagic Sargassum spp. growth and survival remain poorly understood. Here, we developed a multi-reserve DEB model for Sargassum spp. to link environmental conditions (temperature, nutrients, and light) to the organism physiology. In the context of DEB theory, estimating a multi-reserve model parameters can be particularly challenging and strongly relies on the availability of the datasets with information about physiological processes. We conducted a literature review of Sargassum spp. physiological responses, and although some data have been reported, experimental datasets on some key physiological processes are still relatively scarce. From the available data we performed an estimation of the DEB model parameters. We then followed a “virtual ecologist” approach to create a framework linking modelers and empiricists, to analyze which experiments that are most needed to reduce parameter uncertainty. We tested parameter identifiability by creating “virtual” observed data and we analyzed the type of datasets that reduce the uncertainty on parameter estimates, thus increasing their identifiability and the prediction power of the algae model. Our analysis showed to which extent new experiments on nutrient uptake and nutrient limitation would reduce the uncertainty of key model parameters and improve our understanding of holopelagic Sargassum spp. physiology. More generally, our study demonstrates how the use of DEB models, coupled with parameter identifiability analyses, can help design most needed experiments thereby contributing to an integrated approach between experiments and modeling. • Multi-reserve DEB model captured how environment affects holopelagic Sargassum growth. • Review showed diversity of physiological responses studied of holopelagic Sargassum. • We identified data that improves DEB parameters estimation and predictability. • Creation of approach to help design targeted experiments to estimate model. parameters.

• We develop a trait- and niche-based theoretical model to analyse invasion dynamics. • Generalist invaders profit from their strong competitiveness, while specialists profit from empty niche spaces. • Ecological specialisation and niche position strongly mediate invasion impact. • Optimal level of removal effort differs among diversity indices, emphasising that control programs should align their effort levels with clearly defined biodiversity objectives. • Results provide mechanistic insight to guide more effective management of biological invasions. Biological invasions drive species extinctions and population declines, prompting costly mitigation efforts. To evaluate and thereby improve the mitigation efforts, it is essential to understand the invasion process and the effects of the removal effort on the invading species. In this study, we use a trait- and niche-based theoretical approach to study such processes and mechanisms. We show that the degree of ecological specialisation of both the resident community and invading species, as well as the niche position of the invader, are key mediators of the negative impact of invasions. More specifically, generalist invaders tend to be particularly negative, especially when invading central niche positions of communities that are mainly composed of specialist species. Interestingly, our analyses also show that the optimal level of removal effort differs among diversity indices, emphasising that control programs should align their effort levels with clearly defined biodiversity objectives. Moreover, timing emerges as an important variable in mitigating potential negative effects. These results thus provide a fundamental understanding of the invasion process itself, the effect that ecologically different invaders may have when invading different types of communities, and how different degrees of removal effort can mitigate negative effects on the invaded community. Such an understanding can act as a stepping-stone for future efforts that aim to increase the effectiveness of human measures against biological invasion.

• High-resolution monitoring reveals species responses to pulse disturbances. • Gaussian HMM framework for detecting species vocalisation dynamics. • Using changes in log vocalisation counts and constraints on the mean to focus on temporal variability. • Three-state mean-constrained HMM detects a warning state preceding disturbances. • Identified short-term behavioural dynamics in response to pulse disturbance. Continuous biodiversity monitoring is crucial for understanding ecosystem dynamics in an era of global environmental change. Advances in bioacoustic hardware facilitate autonomous monitoring of vocalising animals in terrestrial and aquatic ecosystems. Time series of processed audio data can provide insights into multi-species behavioural responses to imminent disturbances. Here, we present a general Gaussian hidden Markov model (HMM) framework for analysing processed species detection data from audio recordings to identify changes in species behavioural dynamics under sudden and short-term (pulse) disturbances. Our framework transforms species detection data by calculating the logarithmic change in species detection counts between consecutive time points, focusing on shifts in temporal variability rather than counts per se . The framework includes a suite of HMMs with varying complexities in their number of states, constraints on the mean, and inclusion of covariates. We recommend an ensemble of in-sample and out-of-sample model selection methods that balance complexity, interpretability, and forecasting ability. We illustrate the framework using processed bird species detection data from an acoustic sensor array in Okinawa, Japan. To demonstrate the ability of our framework to detect changes in species vocalisation behaviour, we analysed 66 days of bird vocalisation data from before, during, and after two large typhoons struck Okinawa in 2018. A parsimonious three-state mean-constrained model and its non-homogeneous variant with precipitation were selected. The estimated HMM states represent ‘ambient’, ‘warning’ and ‘disturbed’ periods, respectively capturing low, medium, and high variability in vocal activity. A warning state consistently preceded a disturbed state, suggesting that our framework could help detect early behavioural responses to impending pulse disturbances. These findings demonstrate how species behavioural dynamics inferred from high-resolution monitoring can provide early warning signals of emerging ecological disturbances.