Biology subjects

Bouderbala, I.

Publications and source records attributed to Bouderbala, I..

5 recordsLinked to original sources

bioRxiv2026

Behavioural state inference from movement and environmental data using Markovian step selection functions

O_LIMovement paths reflect temporal shifts in behavioural states, typically driven by internal and external drivers. However, the inherently multiphasic nature of these trajectories is frequently overlooked in empirical studies, an oversight that can hinder progress in our understanding of movement ecology. While Hidden Markov Models (HMMs) can successfully identify latent states--such as foraging or travelling--they face significant challenges, particularly in determining the appropriate number of states and in interpreting their ecological relevance in the context of both movement patterns and environmental covariates. C_LIO_LIWe present a framework based on Hidden Markov Models with Step Selection Functions (HMM-SSFs) that identifies behavioural states, represented by ecologically meaningful labels linked to explicit hypotheses about animal movement, that best explain observed movement patterns. The framework imposes interpretable conditions and diagnostic criteria on the post-identified behavioural states to ensure ecological coherence. It is grounded in the evaluation of biologically motivated scenarios rather than purely data-driven partitioning. The framework proceeds in two main steps: first, movement-based states are identified using movement-derived covariates only; second, these states are refined by incorporating environmental predictors, such as habitat structure or species interactions (e.g., predator-prey dynamics). This sequential integration enables the detection of ecological responses that are conditional on behavioural context. C_LIO_LISimulations show that the framework effectively recovers behavioural states across most conditions. State decoding accuracy was notably higher when control locations were drawn from an exponential-family distribution, compared to a uniform one. The exponential-family approach improved state separation and reduced mislabelling, especially when few control locations are generated. However, low state persistence--particularly in Encamped behaviours--resulted in an overestimation of the number of states. These findings underscore the influence of transition probabilities on behavioural labelling. Finally, we applied our framework to zebra (Equus quagga) movement data by combining movement predictors with changes in direction toward the nearest preferred habitat. This enabled us to distinguish between habitat-dependent and habitat-independent travelling behaviours, as well as to identify spatially finer-scale such as encamped state. C_LIO_LIThe proposed framework balances complexity and biological interpretability by using basic movement metrics to identify the behavioural states and their sequence that best explain multiphasic movement paths, together with environmental factors directing movement in each state. Unlike traditional methods that predefine the number of states, the framework estimates both state number and labels, offering a flexible and ecologically meaningful approach for behavioural inference. C_LI

animal behavior and cognition↗

bioRxiv2026

Inferring biodiversity from indicator species using co-occurrence network structure

Biodiversity loss is accelerating worldwide, yet comprehensive assessment of species assemblages across environmental gradients remains impractical for ecological forecasting and conservation. Indicator species are therefore widely used as proxies for community composition, but existing approaches struggle to reconstruct full assemblages, particularly for rare or poorly sampled species. Here we present an integrated framework combining indicator species analysis, ecological network theory, and machine learning to infer the occurrence of non-indicator species from assemblage data. By leveraging structural properties of species co-occurrence networks, the approach captures latent community structure without explicit environmental modeling, enabling inference under sparse sampling. Across species abundance gradients, we identify three distinct regimes of predictability: high predictability for abundant species, reduced performance for species of intermediate prevalence, and unexpectedly strong predictability for rare species driven by strong co-occurrence structure with indicator species. By exploiting complementary information captured by multiple network metrics, the framework recovers species with diverse connectivity profiles and consistently outperforms richness-based or random indicator selection in both accuracy and coverage. Overall, this data-efficient approach provides a transferable pathway for biodiversity monitoring and forecasting, while offering new insights into the network organization of ecological communities.

ecology↗

bioRxiv2023

A probabilistic methodology to reconstruct biodiversity from implicit interactions with indicator species along latitudinal co-occurrence networks

The relationship between species presence, biodiversity reconstruction, and latitudinal gradients is a complex and multifaceted topic that has been the subject of extensive research in ecology. Recent studies have provided valuable insights into the patterns and drivers of these phenomena. Also, with the ongoing decline in biodiversity, there is a need for efficient field monitoring techniques. Indicator species (IS) emerged as a promising tool to monitor diversity because their presence indicates a maximum number of conditionally co-occurring species. We aim to assess the effectiveness of IS for biodiversity reconstruction implicitly based on their co-occurrence with other species through a network-based methodology. The IS are identified based on various network metrics and the likelihood of species occurrences is computed based on (1) their conditional occurrence probability with IS and (2) the occurrence probability of IS. We test the approach with field observations of birds in the Cote-Nord region of Quebec. From our methodology, the climate latitudinal gradient plays a significant role on the alternation in composition of IS with an almost complete turnover between northern and southern networks. The latitudinal gradient impacts also the nature of the inter-specific interactions with more avoidance relationship toward the Tropics and more cooperation liaisons toward the north. Regarding the effectiveness in the reconstruction of assemblages occurrence, we observe a strong negative correlation (r [≤] -0.95) between the percentage of sites occupied and the dissimilarity between the original and the estimated occurrences. More precisely, species must be present in more than 29% and 33% of northern and southern sites to recover well from its co-occurrence with IS. Therefore, it is more challenging to reconstruct biodiversity in communities closet to Tropics due to higher complex interactions and interspecific competition in these areas, which make it more difficult to infer community composition. In conclusion, our method demonstrates that it is possible to predict local species assemblages based on their implicit interactions with local IS. Nevertheless, the relatively low success of less present species illustrates the need for further theoretical development to reconstruct biodiversity, mainly to recover the occurrence of rare species.

ecology↗

bioRxiv2022

Long-term effect of forest harvesting on boreal species assemblages under climate change

Logging is the main human disturbance impacting biodiversity in forest ecosystems. However, the impact of forest harvesting on biodiversity is modulated by abiotic conditions through complex relationships that remain poorly documented. Therefore, the interplay between forest management and climate change can no longer be ignored. Our aim was to study the expected long-term variations in the assemblage of bird and beetle communities following modifications in forest management under different climate change scenarios. We developed species distribution models to predict the occurrence of 87 species of birds and beetles in eastern Canadian boreal forests over the next century. We simulated three climate scenarios (baseline, RCP4.5 and RCP8.5) under which we varied the level of harvesting. We also analyzed the regional assemblage dissimilarity by decomposing it into balanced variations in species occupancy and occupancy gradient. We predict that forest harvesting will alter the diversity by increasing assemblage dissimilarity under all the studied climate scenarios, mainly due to species turnover. Species turnover intensity was greater for ground-dwelling beetles, probably because they have lower dispersal capacity than flying beetles or birds. A good dispersal capacity allows species to travel more easily between ecosystems across the landscape when they search for suitable habitats after a disturbance. Regionally, an overall increase in the probability of occupancy is projected for bird species, whereas a decrease is predicted for beetles, a variation that could reflect differences in ecological traits between taxa. Our results further predict a decrease in the number of species that increase their occupancy after harvest under the most severe climatic scenario for both taxa. We anticipate that under severe climate change, increasing forest disturbance will be detrimental to beetles associated with old forests but also with young forests after disturbances.

ecology↗

bioRxiv2022

Effects of global change on animal biodiversity in boreal forest landscape: an assemblage dissimilarity analysis

Despite an increasing number of studies highlighting the impacts of climate change on boreal species, the main factors that will drive changes in species assemblages remain ambiguous. We quantify two climate-induced pathways based on direct and indirect effects on species occupancy and assemblage dis-similarity under different harvest management scenarios. The direct climate effects illustrate the impact of climate variables while the indirect effects are reflected through the changes in land cover composition. To understand the main causes in assemblage dissimilarity, we analyze the regional and the latitudinal species assemblage dissimilarity by decomposing it into balanced variation in species occupancy and occurrence and occupancy and occurrence gradient. We develop empirical models to predict the distribution of more than 100 bird and beetle species in the Cote-Nord region of Quebec over the next century. Our results show that the two pathways are complementary and alter biodiversity, mainly caused by balanced variation in species occupancy and occurrence. At the regional scale, both effects have an impact on decreasing the number of winning species. Yet, responses are much larger in magnitude under mixed climate effects (a mixture of direct and indirect effects). Regional assemblage dissimilarity reached 0.77 and 0.69 under mixed effects versus 0.09 and 0.10 under indirect effects for beetles and birds, respectively, between RCP8.5 and baseline climate scenarios when considering harvesting. Therefore, inclusion of climatic variables considers aspects other than just those related to forest landscapes, such as life cycles of animal species. Latitudinally, assemblage dissimilarity increased following the climate conditions pattern. Our analysis contributes to the understanding of how climate change alters biodiversity by reshaping community composition and highlights the importance of climate variables in biodiversity prediction.

ecology↗