The stakes are far from academic. Around 14,000 plant species have already naturalized worldwide, roughly four percent of all vascular plants, and approximately six percent of those have become invasive. Invasive plants displace native species, contribute to extinctions, disrupt energy and nutrient cycles, and generate billions of dollars in annual costs to agriculture, health, and ecosystem services. Predicting which species will cross the critical threshold from naturalization to invasion has therefore become a central goal of invasion biology, and a prerequisite for prevention rather than expensive after-the-fact control. Yet more than 30 competing hypotheses have been proposed to explain why some introduced species thrive while others fade, ranging from propagule pressure to biotic interactions to climatic filtering, and untangling their relative influence has proven notoriously difficult.

The new study, published in Discover Conservation, approached the problem by treating each plant species as a data point and each invasion hypothesis as a measurable variable. The researchers compiled an extraordinary 49 predictors for every species, spanning functional traits such as leaf area, seed mass, and plant height; phylogenetic distances measuring how closely related each newcomer is to the native flora; metrics of climatic similarity between a species’ native range and the Caatinga; minimum residence time since first introduction as a proxy for propagule pressure; and the output of ecological niche models predicting how much suitable habitat each species could occupy. The species were classified into three stages following established criteria: non-native, meaning introduced taxa without reproductive capacity; naturalized, meaning those with self-sustaining populations; and invasive, meaning those capable of spontaneous spread.

The Caatinga itself provides a demanding test bed. Home to approximately 3,347 plant species, fifteen percent of which are found nowhere else on Earth, the biome is characterized by spiny shrubs, deciduous trees, cacti, and bromeliads under an open canopy, all shaped by water scarcity, high temperatures, and pulsed resource availability. These strong environmental filters make the region an ideal natural laboratory for testing whether invaders succeed by bringing novel strategies or by resembling the hardy natives already adapted to drought. Previous work had suggested the latter, and the new machine-learning results lend that idea substantial quantitative support.

Methodologically, the team built separate binary models for the two key transitions: from non-native to naturalized, and from naturalized to invasive. Because invasive species were underrepresented in the data, they applied the synthetic minority over-sampling technique, known as SMOTE, to generate synthetic observations of the minority class in the multivariate variable space. A preliminary random forest ranked the importance of all 49 predictors, after which the researchers tested subsets of the top-ranked variables, tuned hyperparameters through exhaustive and randomized searches with 200 iterations, and validated everything with five-fold cross-validation. Three tree-based algorithms competed in the final round: random forest, CatBoost, and XGBoost. XGBoost delivered the best performance, though its optimal settings led to overfitting, so the team adopted the next-best configuration to keep the models honest.

The results revealed a striking asymmetry. The invasion models performed well, achieving an area under the ROC curve of 0.811, while the naturalization models struggled, reaching only 0.717 and proving too weak for reliable classification. For invasion, just three predictors carried nearly all the predictive power: the pseudo-F statistic, a multivariate measure of climatic niche dissimilarity between a species’ native range and the Caatinga derived from PERMANOVA; the abundance-weighted mean phylogenetic distance to native species, or MPDw; and the predicted distribution area within the biome. In other words, the species most likely to become invasive are those whose native climates differ markedly from or span broadly beyond the Caatinga’s conditions, those that are closely related to the native flora, and those with vast stretches of potentially suitable habitat.

To peer inside the black box of these predictions, the researchers turned to Shapley additive explanations, or SHAP, a framework borrowed from game theory that quantifies exactly how much each variable pushes an individual prediction up or down. The SHAP analysis confirmed the global rankings and exposed their nonlinear, interacting effects. Low pseudo-F values were generally associated with naturalized species, while low MPDw values combined with very large predicted areas were linked to invasiveness. Species with native environments similar to the Caatinga tended to be classified as naturalized, whereas those both ecologically similar to native species and equipped with large potential ranges were flagged as invasive. Notably, no invasive species displayed an introduced niche broader than its native one, a pattern that argues against rapid niche evolution and instead supports climatic preadaptation combined with functional similarity to natives as the dominant mechanism.

The misclassifications proved almost as informative as the correct predictions. Several notorious invaders, including Leucaena leucocephala and Prosopis pallida, were predicted as merely naturalized, a sobering reminder that even well-performing models can underestimate the threat posed by harmful species, particularly when applied to newly introduced plants. Conversely, species such as Psidium guajava and Amaranthus spinosus were flagged as invasive despite their current naturalized status. Rather than dismissing these errors, the authors suggest some may signal emerging invasions or outdated status designations: guava, for instance, disperses prolifically through human activity and frugivorous animals and is already invasive elsewhere, while Amaranthus spinosus is already spreading unintentionally near the Caatinga’s borders. In this sense, the model functions not just as a classifier but as an early-warning system whose disagreements with the record deserve field verification.

The weak performance of the naturalization model carries its own ecological message. Plant height and seed mass, along with rainfall-related bioclimatic variables, emerged as the most influential predictors, but they apparently capture only part of what governs whether an introduced species establishes self-sustaining populations. The authors point to the possibility that key drivers were missing from the dataset altogether, such as the availability of mutualistic partners like pollinators required for successful reproduction, consistent with the so-called missed mutualism hypothesis. Combined with the limited number of introduced species and strong class imbalance, these gaps likely blunted the model’s accuracy. Crucially, however, naturalization is the less dangerous transition; invasion is where the real ecological damage begins, and that is precisely where the framework excelled.

The broader implications reach well beyond one Brazilian biome. Most invasive plants in the Caatinga belong to the Poaceae and Fabaceae, two of the most successful lineages in the ecosystem, and grasses as a family show a strong phylogenetic signal of invasiveness globally. This supports the view that preadaptation, rather than novel traits, drives the transition from naturalization to invasion: species evolutionarily equipped for drought and disturbance simply need an opening. By integrating machine learning with interpretable SHAP values, the study offers a template that conservation managers in drylands worldwide could adapt, screening newly arrived species for climatic tolerance, relatedness to the native flora, and predicted range size before they spread. The researchers have made their raw data and analysis notebooks openly available, inviting replication and refinement. For a biome that remains one of Brazil’s most neglected, the message is clear: the invaders most likely to succeed are the ones that look like they already belong, and now, for the first time, science can spot them before they take over.

Subject of Research: Machine-learning prediction of naturalization and invasion of non-native plants in the Brazilian Caatinga dry forest

Article Title: Machine-learning classification of non-native plant status in the Brazilian dry forest, Caatinga

Article References: Almeida, T. S., de Paiva Silva, D., Martinez, P. A., Cruz, A. B. S., da Paixão Menezes, D., Oliveira, E. V. D. S., & Gouveia, S. F. (2026). Machine-learning classification of non-native plant status in the Brazilian dry forest, Caatinga. Discover Conservation, 3(1), Article 1. https://doi.org/10.1007/s44353-025-00073-9

Image Credits: AI Generated

DOI: 10.1007/s44353-025-00073-9

Keywords: invasive species, Caatinga, machine learning, SHAP values, plant ecology, biological invasions, dryland ecosystems, phylogenetic similarity, climatic niche, ecological niche models, XGBoost, Brazil

The research team, led by scientists at Adigrat University, combined a PRISMA-informed systematic review of 59 peer-reviewed and institutional sources with extensive primary fieldwork. Between 2021 and 2024, the researchers carried out 42 key informant interviews and 12 focus group discussions across six districts and 80 villages in the Eastern Zone of Tigray, supplementing these with participatory rural appraisal exercises, transect walks, community mapping, and soil analyses. This triangulated design, the authors emphasize, was chosen deliberately: because no baseline measurements of cactus biomass or stand structure existed before the invasion, the team prioritized converging lines of evidence over over-confident single estimates, and they flag all numerical results as indicative extrapolations rather than precise measurements.

The historical record uncovered by the study reads as a cautionary tale of institutional failure. Cochineal was intentionally introduced between 2004 and 2007 at three sites near Mekelle, Wajarat, and Mehoni, with the goal of producing carminic acid, a valuable crimson dye. The experimental project was abruptly abandoned, however, without post-release monitoring, containment measures, or technical support to farmers. Infested cladodes were distributed across multiple districts, and the insect, which has no natural predators in the region and a high reproductive rate, spread unchecked. Community members in Raya Azebo could reconstruct the pest’s arrival years before it appeared in official reports, suggesting formal surveillance underestimated the true rate of expansion from the very beginning.

The numbers chart an accelerating catastrophe. By 2015, roughly 16,000 hectares were infested; the figure nearly doubled to 31,184 hectares within a single year, reached 75,570 hectares by 2018, and climbed to about 91,000 hectares by 2020, with cumulative damage now exceeding 96,000 hectares. In the southern and southeastern zones of Tigray, cactus pear production has collapsed entirely. Notably, the eastern zone had managed to contain the pest to just 2,676 hectares, about 5.7 percent of its cactus cover, through coordinated cultural, mechanical, and chemical control. That progress was undone when the Tigray war erupted in late 2020: community mobilization, monitoring, and control programs collapsed amid displacement and institutional destruction, and by May 2024 the infested area in the eastern zone had nearly doubled to 5,239 hectares, surpassing 11 percent of the zone’s cactus coverage.

The ecological stakes are considerable because cactus pear functions as a keystone dryland species. Using Crassulacean Acid Metabolism (CAM) photosynthesis, the plant opens its stomata at night to absorb carbon dioxide, dramatically reducing water loss and allowing it to thrive on degraded, saline, and drought-prone land where conventional crops fail. Under favorable conditions it can sequester up to 30 tons of CO2 per hectare per year, and literature values suggest roughly 12,500 kilograms of carbon stored per hectare. Extrapolating across the destroyed area yields an estimated 1.2 million tons of carbon whose sequestration capacity has been compromised, though the authors caution that, given the absence of baseline biomass data in wild stands, such figures should be read as indicative of foregone capacity rather than verified emissions.

The soil evidence is more concrete. Laboratory analyses showed that soils beneath cactus canopies hold substantially more organic carbon (2.48 percent versus 1.82 percent), organic matter (5.45 percent versus 3.23 percent), and total nitrogen (0.25 percent versus 0.19 percent) than adjacent open ground, with available phosphorus reaching 161 ppm compared with just 22 ppm. Canopy soils were also moister (9.49 percent versus 6.74 percent) and less compacted (bulk density of 1.28 versus 1.46 g/cm3). In effect, each cactus stand acted as a nutrient island, stabilizing slopes, dampening runoff, and sheltering understory vegetation. Farmers interviewed for the study described the aftermath bluntly: after the cactus died, the land began to erode away even with small rains. Field observations confirm increased gully formation, topsoil loss, reduced infiltration, and expanding barren patches across affected hillsides.

The loss has rippled through biodiversity as well. Residents reported declines in bird species that once nested on cactus branches and in small mammals that depended on cactus fruits, alongside the disappearance of the protective microenvironments that had allowed native shrubs and grasses to persist. Yet the study also documents unexpected glimmers of recovery: in places such as Shilen in the Maichew-Mekhoni corridor, previously suppressed indigenous woody and herbaceous species have begun to regenerate where cactus died back, suggesting that with active restoration, cochineal-driven mortality could open ecological niches for native vegetation to reclaim degraded land.

The socio-economic toll has been severe. Before the outbreak, cactus pear provided three to five months of household food security annually, along with drought fodder worth 20 to 30 tons of fresh cladodes per hectare, seasonal cash income averaging roughly USD 11,272 per hectare under good management, and informal employment in harvesting and marketing for landless youth, many of them women. The study estimates annual fruit production losses of about 84,985 tons across the region, with cladode losses of roughly 1.9 to 2.9 million tons per year regionally. Combining production losses with degraded ecosystem services, the researchers calculate total economic damages of approximately USD 1.082 billion annually, a figure informants considered conservative. Focus groups described longer hunger seasons, rising livestock mortality, increased labor migration from districts such as Atsbi, Hawzien, and Irob, and the breakdown of traditional coping mechanisms that had relied on cactus reserves as drought insurance.

Current control efforts remain fragmented and largely inadequate. Communities have resorted to labor-intensive measures such as manually removing and burning infested cladodes, selective pruning, and coordinated clean-ups, but these approaches falter once infestations become severe. Chemical control has been tried only sporadically and suffers from high costs and environmental concerns, while biological control agents that have proven effective elsewhere remain untested under Tigray’s arid conditions. The authors identify weak quarantine enforcement, absent post-release monitoring, poor inter-institutional coordination, and limited funding as the systemic gaps that allowed the invasion to flourish and that now hamper its containment.

The study’s recommendations are direct: implement integrated pest management with rigorously evaluated biological control options, restore cactus pear using resistant varieties, strengthen enforceable quarantine and surveillance systems, embed community-based monitoring in extension services, and fold restoration into broader climate adaptation strategies. The researchers also call for long-term monitoring, remote sensing, and field-based biomass assessments to replace today’s uncertain extrapolations with defensible carbon and impact estimates. As the authors conclude, the Tigray cochineal crisis is not merely a pest problem but a demonstration of how ecological, climatic, and institutional fragilities interact, and how the collapse of coordinated governance, whether through abandoned projects or armed conflict, can convert a well-intentioned introduction into a regional environmental and humanitarian emergency.

Subject of Research: Ecological and socio-economic impacts of cochineal insect infestation on cactus pear ecosystems in Tigray, Northern Ethiopia

Article Title: Environmental degradation and climate implications of cochineal (dactylopius coccus Costa) infestation on cactus pear: a case study from Tigray, Northern Ethiopia

Article References: Gebrekidan, T. K., Gebreziher, H. G., Kahsay, H. T., Weldemariam, N. G., Berhane, E., & Gebrekidan, E. W. (2026). Environmental degradation and climate implications of cochineal (dactylopius coccus Costa) infestation on cactus pear: a case study from Tigray, Northern Ethiopia. BMC Environmental Science, 3(1), Article 16. https://doi.org/10.1186/s44329-026-00054-w

Image Credits: AI Generated

DOI: 10.1186/s44329-026-00054-w

Keywords: Dactylopius coccus, cochineal, cactus pear, Opuntia ficus-indica, Tigray, Ethiopia, invasive species, carbon sequestration, soil degradation, food security, integrated pest management, dryland ecosystems