Niamh M. Robmann’s research while affiliated with Swiss Federal Institute of Aquatic Science and Technology and other places

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Publications (4)


Author Correction: Native diversity buffers against severity of non-native tree invasions
  • Article
  • Full-text available

September 2023

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881 Reads

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2 Citations

Nature

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Daniel S. Maynard
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Distribution of the study data
Distribution of the full study dataset, coded for non-native severity (n = 471,888 plots). The map shows average per cent invasion across a 1-degree hexagonal grid, from non-invaded (0%) pixels in green to completely invaded (100%) pixels in purple. Plots are considered invaded if there is any non-native tree present.
Anthropogenic drivers are more important than native diversity in determining invasion occurrence
a,b, Importance (Shapley additive explanations (SHAP) values) of all variables included in random forest models ordered from greatest to least important, alongside influence of distance to ports, native richness and native redundancy on non-native presence (whether a plot is invaded or not) for global models of phylogenetic (a) and functional (b) diversity (phylogenetic diversity, n = 17,640 plots; functional diversity, n = 17,271 plots). All results shown are from random forest models. Note that y-axis ranges differ among panels, with the variable importance plots representing the corresponding magnitude. Error bands represent 95% confidence intervals.
Native diversity is the most important driver of invasion severity
a,b, Importance (Shapley additive explanations (SHAP) values) of all variables included in random forest models ordered from greatest to least important, alongside influence of distance to ports, native richness and native redundancy on invasion severity for global models of phylogenetic (a) and functional (b) diversity (phylogenetic diversity, n = 3,498 plots; functional diversity, n = 3,368 plots). Plots are shown for the severity of invasion measured as non-native species abundance (proportion of basal area with non-native plant species); plots for non-native species richness (proportion of non-native plant species) are shown in Extended Data Fig. 4. All results shown are from random forest models. Note that the y-axis ranges differ among panels, with the variable importance plots representing the corresponding magnitude. Error bands represent 95% confidence intervals.
Environmental filtering at temperature extremes
a,c, Estimates of overlapping variables included in temperate and tropical GLM models (forest plot) for phylogenetic (a) and functional (c) diversity models (phylogenetic diversity, n = 3,498; functional diversity, n = 3,368). Values to the left of the zero line indicate negative model estimates, and those to the right indicate positive estimates. b,d, Relationship between mean annual temperature and invasion strategy for phylogenetic (b) and functional (d) diversity models, showing that at extreme temperatures invasion occurs through similarity (Supplementary Table 7; phylogenetic diversity: P(1) = 9.69 × 10⁻¹⁴, P(2) = 2.13 × 10⁻¹¹; functional diversity: P(1) < 2 × 10⁻¹⁶, P(2) = 1.07 × 10⁻⁴, where P(1) and P(2) represent each temperature and temperature squared P values, respectively). Note for functional diversity, this pattern only holds at low temperatures. Error bars and bands represent standard error.
Proximity to ports weakens environmental filtering in the temperate bioclimate zone
a,b, In temperate plots far from ports, temperature is positively correlated with an invasion strategy of increasing dissimilarity for phylogenetic (a) and functional (b) diversity (phylogenetic diversity: n = 2,710 plots, P = 6.37 × 10⁻⁶; functional diversity: n = 2,603, P < 2 × 10⁻¹⁶). c,d, This relationship between temperature and invasion strategy weakens for phylogenetic (c) and functional (d) diversity with proximity to ports (Supplementary Table 7; phylogenetic diversity: P = 0.0001; functional diversity: P = 2.71 × 10⁻¹³). Lines and points represent the lowest (c,d) and highest (a,b) 10% of data. Error bands represent standard error.
Native diversity buffers against severity of non-native tree invasions

August 2023

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2,637 Reads

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41 Citations

Nature

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species1,2. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies3,4. Here, leveraging global tree databases5-7, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.


Restor: Transparency and connectivity for the global environmental movement

May 2022

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198 Reads

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16 Citations

One Earth

Protecting and restoring biodiversity requires that nature becomes the economically sustainable option for local communities across the globe. Here, we present Restor, a data sharing platform developed to facilitate this process by providing transparency and connectivity to nature-based solutions. In the process, Restor provides a unique database to study the global environmental movement.


Figure 1. Overview of methodology. With a georeferenced observational dataset as input, the GMP uses a composite image to extract environmental information to train a RF model to create a spatial prediction.
A geospatial mapping pipeline for ecologists

July 2021

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921 Reads

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15 Citations

Geospatial modelling can give fundamental insights in the biogeography of life, providing key information about the living world in current and future climate scenarios. Emerging statistical and machine learning approaches can help us to generate new levels of predictive accuracy in exploring the spatial patterns in ecological and biophysical processes. Although these statistical models cannot necessarily represent the essential mechanistic insights that are needed to understand global biogeochemical processes under ever-changing environmental conditions, they can provide unparalleled predictive insights that can be useful for exploring the variation in biophysical processes across space. As such, these emerging tools can be a valuable approach to complement existing mechanistic approaches as we aim to understand the biogeography of Earth's ecosystems. Here, we present a comprehensive methodology that efficiently handles large datasets to produce global predictions. This mapping pipeline can be used to generate quantitative, spatially explicit predictions, with a particular emphasis on spatially-explicit insights into the evaluation of model uncertainties and inaccuracies.

Citations (4)


... Consequently, random forest algorithms have been widely used in regression prediction problems [66] and feature classification [67] in the ecological field. In addition, the random forest model can effectively assess and rank the importance of each variable [68]. Therefore, it is also possible to further determine the degree of importance of each factor to the RSEI of mining cities, and this method has been applied in the study of ecological quality changes in mainland China [69]. ...

Reference:

Quantifying the Impact and Importance of Natural, Economic, and Mining Activities on Environmental Quality Using the PIE-Engine Cloud Platform: A Case Study of Seven Typical Mining Cities in China
Author Correction: Native diversity buffers against severity of non-native tree invasions

Nature

... Additionally, canopy height plays a pivotal role in characterizing habitat structural heterogeneity as an important factor in explaining biodiversity spatial patterns Marselis et al., 2022;Torresani et al., 2023). Endemic forests represent one of the global biodiversity hotspots and must-preserved ecosystems (Delavaux et al., 2023), but climate change and human pressure are jeopardizing the capability of species to adapt fast enough to resist disturbances due to stand replacement or prolonged heat waves (Anderegg et al., 2015;Hartmann et al., 2018). In the Mediterranean basin, the landscape is undergoing transformations driven by droughts, extreme heat episodes and increasingly recurrent wildfires, impacting carbon fluxes and threatening the habitats of endemic species (Grünig et al., 2023;Moreira et al., 2011;Ruffault et al., 2020). ...

Native diversity buffers against severity of non-native tree invasions

Nature

... Nonetheless, several restoration monitoring initiatives have recently started, such as the IUCN Restoration Barometer, which tracks restoration and works with governments to use the data it gathers, the World Resources Institute Global Restoration Initiative that monitors restoration globally and at multiple scales (from www.kva.se/en governmental jurisdictions to individual projects), and Restor, a data sharing platform that tracks restoration and conservation interventions (Crowther et al. 2022). ...

Restor: Transparency and connectivity for the global environmental movement
  • Citing Article
  • May 2022

One Earth

... The binary AOA is derived by applying a threshold to the DI. Other 40 approaches to limit predictions to the area where the model has been enabled to learn about relationships are the extrapolation index (Jung et al., 2020), convex hulls in the feature space (van den Hoogen et al., 2021) or the use of geographic distances from training data locations (Sabatini et al., 2022). All these approaches share the limitation that they do not discriminate between areas with few or even just solitary training data points, and areas that are densely covered by training data. ...

A geospatial mapping pipeline for ecologists