This session will highlight recent advances in advancing causal inference in biodiversity science, including theoretical approaches, integrating underused and novel modelling perspectives, and applied uses of biodiversity change detection and attribution frameworks. We will highlight the key challenges and opportunities in applying causal approaches to biodiversity change analyses, offering an accessible overview of current methods and decision points for ecologists and applied practitioners.
Our session is aimed at fostering dialogue across disciplines, highlighting pathways towards integrating theory with data-driven approaches to advance robust causal inference in biodiversity science. We particularly welcome contributions on integrating causal and process-based models, as well as novel applications of detection-attribution frameworks, as well as studies addressing the interface of ecological causal monitoring, policy, and conservation planning.
Disentangling drivers behind biodiversity change implies confidently detecting such shifts and being able to properly isolate and measure the influence of a given pressure, ruling out rival explanations. Although experiments are the gold standard for achieving such attribution, they are not always feasible, depending on the study's scale and the species populations involved. The need to leverage observational data for detection and attribution has led to the uptake of practices, hypotheses and methods inherited from causal inference. However, navigating the many modelling options and identifying suitable approaches for a given project is not straightforward. This is why we have introduced NaviDAM: an online decision-support tool designed to guide method selection by assessing users' case studies against a set of criteria. Depending on the main objective — effect estimation, change detection or scenario projection, for instance — options regarding data structure, affordable assumptions, desired model properties and programming languages must be selected. The tool covers a wide range of approaches, from predictive models to causal estimators. Methods are furthermore briefly described on dedicated pages. Redirection links lead to existing online materials that detail them, as well as to the reference method paper and application articles in ecology (with and without remote sensing data). Finally, their assessment table provides transparency on how the method is filtered. Method descriptions and assessments can be discussed and iteratively refined: NaviDAM is an open-source project that welcomes contributions from the community. The website is completed by good practices and gallery pages that respectfully cover cross-cutting subjects (e.g. review articles, causal graphs and collinear inputs) and provide examples of NaviDAM usage. Through collaborative efforts, the aim is to develop a tool which is: i) user-oriented, with a set of criteria to filter down candidate methods suited to the input case study; ii) method-inclusive, to match the variety of users' needs; and iii) resource-oriented, linking method materials and outstanding applications to help users get started on their own projects.
How to cite: Estopinan, J., Goury, R., Mäkinen, J. A., Santini, L., Beck, M., Zampetti, A., Dee, L., Thuiller, W., Cesab, I. G., and Consortium, O.: NaviDAM: A collaborative decision-support tool for navigating between detection and attribution methods in ecology, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-344, https://doi.org/10.5194/wbf2026-344, 2026.
Understanding causal relationships in ecological systems is essential for predicting how populations and communities respond to environmental change. Over the last decade, interest has grown around causal attribution using long-term ecological time series, with convergent cross mapping emerging as a powerful method for causal discovery. This method is well-suited for nonlinear, coupled ecological systems and tests for causality by detecting whether a driver’s influence leaves a measurable signal within the time series of an affected variable. Given the presence of a causal process, we can predict the time series of the driver (i.e. cause) from the time series of the effect because of this measurable signal. Convergent cross mapping therefore systematically searches for these signals of the driver in the time series of the effect and quantifies whether a causal relationship exists based on how accurately we can predict the time series of the driver. Although convergent cross mapping has been effectively applied to population dynamics of diverse taxa like fish, grasses, and microbes, its application at the community scale remains less explored. We investigated how community-level metrics, including species richness and community abundance, behave under convergent cross mapping compared to individual populations. Using five decades of data from the North American Breeding Bird Survey, which includes annual multi-site bird counts, we examined the causal influence of temperature on both population abundances and community metrics. Our analyses revealed contrasting patterns: at the population level, longer time series increase the likelihood of detecting causal relationships with temperature, consistent with expectations for convergent cross mapping’s performance in recovering driver signals over time. However, at the community level, the probability of detecting temperature-driven causal effects decreases with longer time series. This divergence suggests potential ecological or methodological complexities when scaling from populations to communities and indicates that aggregating species into community metrics may mask species-specific causal signals driven by temperature. These findings highlight challenges in scaling causal inference methods from populations to communities, emphasizing the need for careful interpretation of community-level causal dynamics in the face of global change.
How to cite: Thompson, K. L., Meyer, C., and Chase, J. M.: Causal attribution in ecological systems: scaling from populations to communities, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-391, https://doi.org/10.5194/wbf2026-391, 2026.
Attributing drivers to ecological change and quantifying their effects is a central goal in ecology, yet remains challenging when relying on observational data. Temperature, for example, has been identified as a major driver of species’ range shifts, behaviour changes and population declines in many bird species, with consequences for community structure and diversity. However, temperature rarely varies in isolation and co-occurring factors - such vegetation changes, landuse shifts or habitat degradation - confound effect estimation if not correctly addressed. Accurately identifying the isolated contribution of temperature is therefore essential for conservation planning, forecasting models, and understanding ecological responses to global change. Causal inference is increasingly recognised as a valuable framework for addressing these challenges, combining promising statistical methods with causal reasoning. Moving beyond a focus on model choice, best practices emphasize explicit causal thinking, transparent assumptions, robustness checks, and the triangulation of evidence across complementary methods.
In this study, we use a Pan-European dataset of structured Breeding Bird Surveys to estimate the effect of temperature on bird community diversity over the past 15 years. We apply variants of two-way fixed-effects models to control for unobserved spatial and temporal confounding and quantify the responses of complementary diversity metrics towards both temperature mean conditions and measures of extremes.
Preliminary results suggest that bird diversity responds most strongly - and generally positively - to changes in average breeding-season temperature. This effect was stronger concerning site-level anomalies rather than general temperature variation. Metrics of extreme temperatures show more heterogeneous effects and in most cases were linked to Bioregion-wide trends: higher maximum temperatures and greater interannual variability tend to reduce diversity, whereas increases in minimum temperatures seem to be associated with positive responses. By explicitly disentangling temperature effects from co-occurring environmental changes, our work demonstrates how causal inference can enhance the reliability in assessments of biodiversity change under a warming climate.
How to cite: Beck, M., Schrodt, F., Thuiller, W., and consortium, I.: Causal attribution of temperature impacts on European breeding bird communities, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-474, https://doi.org/10.5194/wbf2026-474, 2026.
Evidence is accumulating that the ranges of many species change in response to environmental change. How different global change drivers contribute to colonisation and local extinction across species is, however, poorly understood.
Here, we leveraged dynamic occupancy models to simulate metapopulation dynamics and attribute observed changes in occupancy to climate and land use drivers. We fitted the models to 25 years (1995-2019) of occurrence data for 159 bird species from the North American Breeding Bird Survey using a Bayesian approach. In the dynamic occupancy models, we related initial occupancy, site-level colonisation and extinction to annually resolved climate and land use variables. Simulated occupancy dynamics were evaluated using spatial and temporal block cross-validation. For 80 species, the models showed fair predictive performance, and we subsequently only considered these species for impact attribution. We assessed the relative importance of climate and land use change for the simulated occupancy dynamics based on counterfactual scenarios with detrended climate and constant land use with the base year 1995, the first year of our observation time series, and controlled for differences in prediction error between models.
Our results suggest that climate change has affected occupancy dynamics of North American birds more strongly over the last three decades than land use change. For 75 % of the assessed species, climate change has had a negative impact on their overall occupancy trend, according to our simulations. For many of these species, we found that climate change is more important to reproduce the observed occupancy dynamics than for species for which we found a positive impact of climate change on the overall trend. While land use change turned out to be less important than climate change for the observed occupancy dynamics, its effect on the overall trend is negative for almost all of the assessed species.
Our data-driven statistical framework allows robust attribution of multiple global change drivers on transient bird occupancy dynamics. This can provide valuable information for species management under environmental change and represents an important step towards operationalising detection and attribution in biodiversity science.
How to cite: Schifferle, K., Briscoe, N. J., Urban, M. C., and Zurell, D.: Assessing the impact of past climate and land use change on bird occupancy dynamics in North America, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-240, https://doi.org/10.5194/wbf2026-240, 2026.
Warming as the dominant driver of floristic change in the French Alps
Over the past half-century, climate change has triggered profound ecological shifts, with the redistribution of species being one of the most consistent responses. In the European Alps, for example, temperatures have risen at roughly twice the hemispheric average since the late 19th century. This has led to earlier snowmelt, leaving plants vulnerable to frost. These combined environmental changes are having a significant impact on mountain ecosystems, resulting in consistent increases in thermophilous species at high elevations. However, the relative importance of climatic drivers remains unclear. Using a 30-year dataset documenting the spatial and temporal shifts of 4,250 plant species across the French Alps, we employed a causal inference framework to quantify the dominant factors shaping floristic change. Mean annual temperature, growing-degree days and water deficit were identified as the three most influential factors, each having an overall positive effect on the occurrence probabilities of species, whereas nitrogen deposition, precipitation and drought had weaker and slightly negative effects. A 1 °C rise in temperature increased species occurrence probabilities by 0.9%, yet responses differed sharply among biogeographic groups. Mediterranean generalists and alpine specialists showed the strongest gains, whereas broadly distributed generalists displayed no significant trends. Functional traits shaped these outcomes, with graminoids benefiting from warming while woody species showed weaker responses. The combined influence of warming and rising water deficits revealed additional trade-offs, as drought-tolerant groups such as Mediterranean generalists and alpine specialists responded positively to both drivers, in contrast with the weak or neutral responses of generalist taxa. By applying a causal inference framework, we identified the relative importance of multiple drivers and showed that temperature is the dominant force shaping shifts in the French Alpine flora, while interactions among climatic drivers are equally crucial for understanding how species respond.
How to cite: Goury, R., Estopinan, J., Thuiller, W., and Münkemüller, T.: Warming as the dominant driver of floristic change in the French Alps, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-485, https://doi.org/10.5194/wbf2026-485, 2026.
There is cumulative evidence that human impact and land-modifications negatively affect global biodiversity. However, understanding in more detail to what extent specific human interventions—particularly forestry practices—affect biodiversity on a local basis is a complex and currently under-explored challenge. Investigating the causal links between these practices and biodiversity changes is essential for evidence-based conservation and restoration strategies.
This study applies causal inference methods to quantify the impact of different forestry management strategies on biodiversity across thousands of observational plots sampled over several decades across the entirety of Sweden which is part of the Swedish National Forest Inventory (Riksskogstaxeringen) (One of the world's oldest continuous forest inventory which started from 1923). These long-term and large-scale observational studies provide a rich dataset that contains information on several types of human impacts in the forest, lists of detected species, as well as other biotic and abiotic features.
Our modeling approach explicitly distinguishes association from causation, thereby providing robust insights into how forestry interventions affect biodiversity. We estimate conditional causal effects, allowing us to examine how the impact of forestry practices varies across ecological contexts such as forest age, biogeographic region. This conditional perspective is critical for identifying heterogeneous responses and tailoring management strategies to local conditions.
To capture nonlinear dynamics in how biodiversity responds after a forestry management strategy, we employ piecewise linear spline regression within the causal framework. This enables us to model how biodiversity indicators changes through time. We also conditioned the analysis with ecological context similar to before. By integrating spline-based regression with causal inference, we achieve a flexible yet interpretable framework that can capture more ecological complexity while maintaining statistical rigor.
The findings address critical gaps in forest ecology by moving beyond descriptive assessments toward causal understanding. Ultimately, this work can provide policymakers and forest managers with actionable evidence to design sustainable forestry practices that balance economic demands with biodiversity conservation goals.
How to cite: Wen Jie, V., Nyström, J., Wigren, T., Schön, T., Andermann, T., and Zachariah, D.: Quantifying the Impact of Forestry Practices on Biodiversity Using Causal Inference Methods, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-659, https://doi.org/10.5194/wbf2026-659, 2026.
Theory predicts that species’ upper range limits are constrained by cool temperatures, and should expand upwards under climate warming. However, recent evidence reveals that many plant species are not moving fast enough to track changing climate. Alternative potential limiting factors are dispersal limitation or competition with resident high-elevation species. To understand the mechanisms explaining range shift lags, we transplanted ten lowland plants to a site above their current elevation limit in the eastern Swiss Alps, with or without the existing vegetation. We tracked individual level vital rates of these species across three growing seasons to construct integral projection models (IPMs) to predict population growth rates in the different experimental treatments.
All species were predicted to successfully establish above their current elevation limit when the existing vegetation was removed, indicating that the high elevation site is within the climate niche of the species. Population growth rates of four species were below one when growing with competitors, suggesting that their absense from the high elevation sites can be explained by competitive exclusion by the resident community. While dispersal limitation can not be ruled out for these species, this result highlights the potential for species interactions to constrain range expansion in response to climate change. The remaining six species were predicted to successfully establish above their current range edge even in the face of interactions with the existing vegetation. This indicates that their absence from the high elevation site today can be explained by dispersal limitation. While this is surprising given the very short dispersal distances involved, simulations of the IPMs revealed that in some cases, hundreds of thousands of seeds would be needed to support rapid population establishment.
These findings challenge assumptions that high-elevation communities contain weak competitors, and demonstrate that competition and dispersal limitation may often impede expansion into newly climatically suitable habitat. This suggests that even widespread species might require active management to support climate tracking as climates continue to warm rapidly.
How to cite: Iseli, E., Bjorkman, A., Clark, R., Hargreaves, A., Kardol, P., Vandvik, V., Zong, S., and Alexander, J.: Experimental tests of the mechanisms explaining lags in plant elevational range shifts, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-328, https://doi.org/10.5194/wbf2026-328, 2026.
The populations of many cold adapted bird species have been declining in the last centuries because of global warming. Many high arctic or mountain species declined or even retreated from southern or low-elevation areas. Knowing the mechanisms that lead to the observed declines helps developing measure to support the populations. We studied how changes in the environment affect the reproduction and survival of a high-elevation bird species, the white-winged snowfinch Montifringilla nivalis.
In central and eastern Alps, we monitored breeding success by measuring nestling growth rate and fledgling weight. We further estimated annual apparent survival for first year and adult birds based on long-term mark-recapture data.
We showed that nestling growth, fledgling weight and first year survival is decreasing with climate warming induced advances of snowmelt. We further found a strong negative correlation between summer temperature and annual apparent survival in adult birds, particularly in females. Summer temperature negatively affected apparent survival during the subsequent winter.
The population dynamics of the white-winged snowfinch is affected by global warming through a phenological mismatch. Breeding phenology of snowfinches does not advance in parallel to the advancing snowmelt. Consequently, the time when the brood needs most energy, the nestling period, does no longer match the time period of highest food availability, i.e. the peak abundance of insect larvae, which develop in the meltwater of the snow. This mismatch leads to lowered first year survival. In addition, warm summer temperatures exhibit a negative carry-over effect on winter apparent survival of adults, particularly in the females. Some alpine plants produce less seeds in warm and dry summers, which may lead to food shortage in the subsequent winter. Food shortage may affect to a higher degree the smaller, less competitive individuals, i.e. the females.
An adaptation of the species to a warmer world may only be possible if habitats providing food such as flower-rich alpine meadows can be preserved. Specifically, alpine meadows at cooler and humid locations such as northern slopes, where the snow melts late, may become particularly important.
How to cite: Korner-Nievergelt, F., Dirren, S., Hille, S., and Niffenegger, C.: Population declines of cold adapted bird species are caused by mechanisms involving reproduction and survival, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-530, https://doi.org/10.5194/wbf2026-530, 2026.
Climate-driven extreme events are fundamentally reshaping riverine ecosystems, with floods, droughts, and heatwaves increasing in severity and frequency. River ecosystems, structured by their connected dendritic networks, are highly vulnerable to the propagation of localised disturbances. Both isolated and compounding extreme climatic events events affect biodiversity across scales, from the erosion of genetic diversity to the loss of ecosystem functioning, and these impacts are often amplified by other underlying stressors. However, extreme events are rare by their very nature, making them challenging to understand due to limited opportunities to learn. This presents a conundrum for predictive modelling of their impacts. Faced with insufficient information about how ecosystems might respond to such events over both short and long timescales, managers may be forced to make decisions that lack scientific credibility. Traditional ecohydrological models, often based on empirical correlations within historical ranges of variability and assumptions of hydroclimatic stationarity, may be limited when anticipating the impacts of ECEs. First, extremes are rare events located in the tails of statistical distributions, thereby challenging conventional methods that focus on central tendencies, and providing questionable insight into, and opportunities to learn from, the dynamics of extreme values. Second, the effects of extremes can propagate across levels of ecological organisation and among components of a species’ life history. Third, anomalous events are often treated as statistical anomalies that are scrubbed from the data prior to analyses. In light of these and other challenges, I discuss promising avenues for modelling frameworks that can be employed to predict the impacts of ECEs ranging from near-term forecasts to mechanistic approaches particularly suited to long-term scenario-based projections such as community-wide matrix population models. Distributional regression approaches, such as Generalised Additive Models for Location, Scale and Shape offer a particularly useful set of tools to understand the full range of ecological responses to extremes. Increasing risks of extreme events in freshwater ecosystems means new approaches are needed to transform the field from restoration thinking to resilience thinking.
How to cite: Tonkin, J.: Anticipating ecological responses to extreme climatic events in rivers, World Biodiversity Forum 2026, Davos, Switzerland, 14–19 Jun 2026, WBF2026-897, https://doi.org/10.5194/wbf2026-897, 2026.
