Global Invasion Decoded: Using Meta-Analysis to Reveal the Hidden Rules of Biological Invasion
Technologies: Meta-Analysis, GLMs statistical Modeling, R Programming, Systematic Literature Review, Spatial & Temporal Analysis
This is part of my PhD study. The full thesis is available in the repository
Can we predict the impact of an invasive species before it causes a crisis?
By synthesizing hundreds of scientific studies, I built a global framework that reveals the hidden patterns of aquatic invasions.This project quantified the ecological impacts of 141 non-native species (NNS) by analyzing 362 separate publications. The result is the first direct comparison of invasion impacts across freshwater, brackish, and marine ecosystems, culminating in a predictive guide for conservation managers.
The Problem That Hooked Me
Take a look at the following image, which depicts a typical marine environment. How can we identify the presence of invasive species underwater, and what kinds of impacts might they be having?
As an ecologist, I was frustrated by the chaos of information. One study would report an invasive clam devastating a river, while another found the same species had little effect elsewhere. For conservation agencies with limited budgets, which threats do you tackle? It felt like they were making high-stakes decisions based on fragmented, often contradictory, evidence.
I realized the problem wasn't a lack of data, but a lack of synthesis. The answer had to be hidden within the thousands of published studies.
Could I sift through decades of global research to find a universal "rulebook" for biological invasions? Could we teach ourselves to see the global pattern by looking at all the local snapshots at once?
Technical Architecture: From Scientific Papers to a Global Impact Map
Phase 1: The Global Evidence Hunt I began by systematically scouring scientific databases—specifically Web of Science and Scopus—for every field study that quantified the impact of a non-native aquatic species. This wasn’t just a simple keyword search; it was a deep dive into the scientific literature to build the most comprehensive dataset possible on this topic. In total, I screened over 4,800 papers!
Phase 2: Building the Analytical Framework The core of the project was creating a rigorous, standardized structure to compare "apples to oranges"—that is, different species in different habitats studied by different scientists. This involved statistical modeling using Hedges' g to create a standardized "impact score" for every single case.
Phase 3: Visualizing a Global Web of Interactions The final step was to translate the complex statistical outputs into a clear, intuitive "impact map" that managers could use to see which types of invaders pose the biggest threat to which parts of the aquatic world.
Data Challenges: Taming the Scientific Literature
Dataset: 362 publications, 141 NNS, and 2,737 individual ecological interactions. See the map below for the study locations
The real-world data reality check:
Methodology Soup: Studies used different sampling techniques, were conducted in different seasons, and lasted for different durations.
Apples vs. Oranges: How do you compare the impact of an alga on nutrient levels to the impact of a predatory fish on native abundance?
The Brackish Blind Spot: Transitional habitats like estuaries were often miscategorized as either "freshwater" or "marine," hiding their unique role as invasion gateways.
Statistical Synthesis & Core Architecture
Core Model: Mixed-Effects Meta-Analysis
This model allowed us to calculate an overall effect size while accounting for the fact that multiple data points often came from the same study or focused on the same invasive species.
Key Analytical Insight: The real breakthrough came from separating biotic impacts (effects on living things like abundance and diversity) from abiotic impacts (effects on the physical environment like nutrient levels). Previous analyses mixed these, muddying the results. By separating them, I uncovered a much clearer signal.
Final Results: The "Rules" of Invasion Emerge
This impact web for freshwater ecosystems shows that non-native predators, primary producers, and omnivores predominantly cause a decrease (blue bands) in the abundance of native fish, benthic invertebrates, and macroflora , while having no net impact on microbes and phytoplankton.
Freshwater & Marine systems are hit hard and equally. Both ecosystems see a significant drop in the abundance and diversity of native species following an invasion. The idea that marine systems are more resistant is a myth.
Brackish water is different. In these estuaries, I found no net decrease in the diversity of local life. Instead, many invaders act as "ecosystem engineers," creating new physical habitats (like reefs of shells) that other species can colonize. This can lead to an "invasional meltdown," where one invader helps others get established.
The "Most Wanted" List is Clear: Invasive predators and primary producers (like algae and plants) consistently cause the largest decreases in native communities across all systems.
The Underdogs: Invasive herbivores had a more complex effect, sometimes even increasing the diversity of other groups by creating habitat.
The Global Impact Map: Understanding the "Why"
I translated these findings into a network diagram that visualizes the entire web of interactions. This goes beyond a simple number and shows who impacts whom.
Ecological Validation: The patterns revealed by the model align perfectly with ecological theory.
Predictable Negative Impacts: The diagram clearly shows predators reducing the abundance of fish and invertebrates, and primary producers outcompeting native macroflora.
Surprising Positive Interactions: It also visualizes the unexpected positive effects, like non-native herbivores and detritivores increasing the diversity of benthic invertebrates in brackish water, likely by providing habitat.
Why this matters for conservation
This isn't just an academic exercise. A manager can use this diagram to see that a new predatory NNS discovered in a freshwater system is a major red flag for native fish, justifying a rapid and decisive response.
Future Vision: Proactive vs. Reactive Conservation
The findings from this project help shift invasion management from a reactive to a proactive stance.
Next Steps
Integrate with Horizon Scanning: Use this framework to prioritize surveillance efforts on the highest-risk invader types in the most vulnerable habitats.
Develop a Management Decision Tool: Create an interactive dashboard where managers can input a new invader's characteristics and receive a probabilistic impact assessment.
Incorporate Economic Data: Layer economic impact data on top of the ecological framework to calculate the "full cost of invasion" and better justify management spending.
Potential Impact: To transform conservation, saving time, money, and biodiversity by tackling the most significant threats before they become unstoppable.