Meta-analysis reveals negative but highly variable impacts of invasive alien species across terrestrial insect orders

Domain: biology
Published: 2026-01-15
Editorial outcome: minor_revision

Abstract
Insects are crucial to ecosystem functioning but face numerous threats, with invasive alien species likely among the most severe. As insect declines continue, there is a growing need to synthesise evidence on how invasive alien species affect insects, as research has historically focused more on insects as invader than as victims. Here we conduct a global meta-analysis encompassing 318 effect sizes across 52 studies, assessing invasive alien species impact on terrestrial insect orders (Coleoptera, Hemiptera, Hymenoptera, and Orthoptera), and examining factors influencing these effects. We show that invasive alien species reduce the abundance of insects included in our study by 31%, and species richness by 26%, though these impacts are highly variable across taxa. Stronger negative impacts are found for invasive alien animals compared to invasive alien plants, and for Hemiptera (true bugs) and Hymenoptera (bees, wasps, ants) compared to Coleoptera (beetles). These findings provide quantitative estimates for the relative vulnerability of insects to invasive alien species, which is an important step towards halting declines.

Introduction
Insects are one of the most abundant and species-rich groups on land1, but are undergoing concerning declines across the world2–8. If this trend continues, ecosystem services such as pollination, pest control, decomposition, and food web stability9–11 will be further threatened, leading to adverse impacts on global biodiversity and human well-being12.

Invasive alien species are likely one of the greatest threats to insect biodiversity8,12,13 and are being introduced worldwide in increasing numbers14. Alien species are those introduced outside their natural range, unintentionally or intentionally, to new areas where they would not naturally occur via human activities such as trade and tourism15. Once an alien species establishes and spreads it is termed an invasive alien species14,15. The negative impacts of invasive alien species15–18 occur when invasive alien species predate or parasitise native species, compete for resources, transmit pathogens and diseases, or hybridise with natives19, leading to homogenisation of biota and driving global extinctions14,15,20. Nevertheless, the effects of invasive alien species are not always negative21–24. For example, invasive alien plant can provide pollen and nectar to native pollinators25–27, or invasive alien fish can become a food source for native predatory fish28.

The drivers of differing responses to invasive alien species remain unclear29,30, particularly for insects, despite the vital role insects play in ecosystems. Previous research syntheses have focused on the impact of invasive alien insects on species more widely, rather than the impact of all invasive alien species on insects specifically31–34. Several other meta-analyses have considered the impact of invasive alien species on animals more broadly—including, but not focusing on, insects—often highlighting negative but highly variable effects35–37. Syntheses have also addressed the impact of other threats on insect biodiversity including urbanisation38, plantations39, dams40, and nutrient enrichment41,42. There is a clear need to better understand how insects specifically are affected by invasive alien species to better inform conservation action and to add to a developing evidence base on threats to insects19,40,41,43.

Taxonomy, geography, and traits are all likely predictors of the impact of invasive alien species on insects. For taxonomy, an invasive alien animal may have a more severe and immediate impact than an invasive alien plant due to its increased potential for direct interactions with the native insect via competition and predation, and vice versa44. Geographical factors such as geographical realm are also likely to have a substantial impact. For example, given they are often more specialised and thus more sensitive to change, insects have been found to be more susceptible to invasive alien species inside the tropics than outside39. Alternatively, it may be more challenging for alien species to establish and impact native populations in tropical regions due to high levels of competition45 or lack of disturbance46. Evidence also strongly suggests that insects on islands will be more negatively affected by invasive alien species due to their isolated geographical ranges and the difficulty of recolonising after extinction15,47. For traits, characteristics such as flight capability influence mobility of the native insect, potentially allowing the native to escape areas disturbed by invasive alien species (provided there is suitable habitat available), thereby reducing the impact of an invasion48.

Here we present a meta-analysis of the impact of invasive alien species on a subset of terrestrial insect biodiversity. While previous research has examined the effect of invasive alien species (specifically invasive alien plants) on Lepidoptera49, we focus on insects in the primarily terrestrial orders Coleoptera (beetles), Hemiptera (true bugs), Hymenoptera (ants, bees, sawflies, and wasps), and Orthoptera (grasshoppers, locusts, and crickets). We selected these orders because invasive alien species were identified as a major potential threat in an expert elicitation process43. We address two key research questions: 1. What is the impact of invasive alien species on the abundance, biomass, and species richness of insects in the taxonomic orders Coleoptera, Hemiptera, Hymenoptera, and Orthoptera, relative to areas without invasive alien species present? 2. How do our moderator variables influence the magnitude of this effect? Our expectation was that insect biodiversity will be lower in areas with invasive alien species, but that this effect will be moderated by native insect taxonomy, invasive alien taxonomy (i.e., animal or plant), geographical realm (i.e., tropical or non-tropical), island invasions (i.e., an island or continental invasion), and flight capability (i.e., flying or non-flying in the adult stage). We additionally examine the year of study publication as a potential moderator to investigate the extent to which publication date predicts the reported effect of invasive alien species50.

Results
Data description
We extracted data from the 52 studies that met the predefined inclusion criteria, totalling 318 effect sizes (median effect sizes per study = 4; minimum = 1; maximum = 31) (Supplementary Fig. 1), once the single study analysing biomass was removed. Date of study publication ranged from 1995 to 2022, with more than two-thirds of the studies being published in the latter half of this range (2009 to 2022; Fig. 1a). The distribution of effect sizes shows broad spatial coverage, with data from every continent except Antarctica (Fig. 1b). Many effect sizes originate from North America (n = 81; 25%) and Europe (n = 105; 33%), reflecting wider spatial biases in insect data51,52, while 16% of effect sizes originated from tropical biomes and 7% from islands.

Regarding the invasive alien species investigated, 30 studies assessed an invasive alien animal (including insects), while 22 assessed an invasive alien plant. Of the terrestrial insect orders investigated, most effect sizes describe the abundance or species richness of Hymenoptera (134 effect sizes; 42%) and Coleoptera (133 effect sizes; 42%); followed by Hemiptera (43 effect sizes; 14%), and Orthoptera (8 effect sizes; 3%) (Fig. 1c). Subsequently, ants and dicotyledon plants were the most frequently reported invasive alien species in our dataset, with 39% and 34% of effect sizes, respectively (Fig. 1d). The remaining effect sizes describe the effect of other invasive alien plants and invertebrates, as well as mammals, fish, crustaceans, reptiles, and amphibians. Over 40% of the effect sizes describe how invasive alien species presence affects the focal insect taxon at the species level, and over 75% to at least the family level. The majority of effect sizes (278; 87%) describe changes in abundance, while only 40 (13%) effect sizes report changes in species richness.

How do invasive alien species affect insect abundance and species richness across four orders?
The abundance of Hymenoptera, Coleoptera, Hemiptera, and Orthoptera was 31% lower on average (95% confidence interval: 45% to 14% lower; LRR: −0.37 [−0.60, −0.15]) when invasive alien species were present compared to absent. Moreover, species richness was 26% lower (95% confidence interval: 44% to 1% lower; LRR: −0.30 [−0.59, −0.01]) with invasive alien species (Fig. 2). Heterogeneity for abundance data, assessed with multi-level I2, indicates high variation (91%), with between-study differences accounting for 28% of the variation, and within-study differences accounting for 63%. The variance among true effect sizes for abundance was partitioned into variance between studies (σ² = 0.32) and within studies (σ² = 0.70). For species richness data, heterogeneity was 98% (37% between, 60% within). The variance components for species richness were σ² = 0.23 between studies and σ² = 0.37 within studies.

For the abundance models, the funnel plots were visually symmetrical (Supplementary Fig. 2) around the overall effect size, showing no apparent publication bias. The rank correlation test (non-significant asymmetry; Kendall’s tau = −0.0142, p = 0.7245) and adapted Egger’s regression (no relationship between effect size and its error; estimate = 0.2463, p = 0.6112) formally supported this, indicating no concerns of publication bias. Still, the data points did not form the classic funnel shape, likely due to high heterogeneity across ecological studies, where larger studies do not necessarily show greater precision50,53,54. Our results did not qualitatively change under multiple sensitivity analyses, including when using Hedge’s g as the effect size, when excluding influential effect sizes (Cook’s distance), when excluding data collected with aquatic sampling techniques, or when excluding data where the small sample corrected standardised mean of either the treatment or control did not pass Geary’s test. The AIC value was greater for a model including a phylogenetic correlation matrix as a random effect (Supplementary Table 1).

The rank correlation test for the species richness model indicated potential funnel plot asymmetry (Kendall’s tau = −0.2872, p = 0.0088), although this was not supported by the adapted Egger’s regression (estimate = 0.7596, p = 0.3140), which found no relationship between effect size and its error. For the sensitivity analyses, the results were less consistent: the negative effect of invasive alien species on insect species richness remained significant when excluding data collected with aquatic sampling techniques, but not when using Hedge’s g as the effect size, when excluding particularly influential data points, nor when excluding data that did not pass Geary’s test (Supplementary Table 1). Thus, there is some evidence of publication bias for our species richness models and they are less robust to changes in metric and data inclusion than the abundance models.

How do the moderator variables influence the magnitude of the effect of invasive alien species?
The magnitude of the effect of invasive alien species was affected by the focal insect order (Fig. 3). Hemipteran abundance was 58% lower (72% to 37% lower) in sites where invasive alien species were present, with Hymenopteran abundance also found to significantly decrease (−37% [−54%, −14%]). Contrastingly, the results for Coleoptera (−12% [−34%, 18%]) and Orthoptera (−27% [−68%, 66%]) were not significant (Fig. 3a). Hymenopteran species richness was 46% lower (62% to 21% lower) in the presence of invasive alien species, while no significant change was detected for Hemiptera or Coleoptera (Fig. 3b). No effect sizes were collected for Orthoptera species richness. Including focal insect order as a moderator variable in the model explained a significant proportion of the heterogeneity in both the abundance (QM = 12.7882, p = 0.0051) and species richness models (QM = 6.7964, p = 0.0334), indicating strong differences in response to invasive alien species by different insect orders. The variance among true effect sizes for abundance was partitioned into components between studies (σ² = 0.33) and within studies (σ² = 0.67). For species richness, the corresponding values were σ² = 0.19 and σ² = 0.34. Residual heterogeneity was significant for both abundance (QE = 2008.72, df = 273, p < 0.0001) and species richness (QE = 2368.89, df = 37, p < 0.0001), indicating that unaccounted variation remains in both models.

The type of invasive alien species, whether animal or plant, also moderated the overall effect on abundance (QM = 4.0595, p = 0.0439) (Fig. 4). The abundance of focal insect taxa decreased in the presence of invasive alien animals (−43% [−57%, −24%]), while no significant effect was observed for invasive alien plants (−11% [−36%, 24%]). For species richness, the overall effect size was greater in the presence of invasive animals compared to plants, but the groups did not significantly differ (QM = 0.9556, p = 0.3283). Most of the effect sizes for invasive animals described the effect of invasive alien insects, particularly ants (123 effect sizes, 76%), while the invasive plant group was dominated by dicotyledon plants (108 effect sizes, 69%) (Fig. 1d). For abundance, variance among true effect sizes was partitioned into components between studies (σ² = 0.26) and within studies (σ² = 0.71). The corresponding values for species richness were σ² = 0.22 and σ² = 0.38. Significant residual heterogeneity remained in both models (abundance: QE = 2153.6, df = 275, p < 0.0001; species richness: QE = 3018.45, df = 38, p < 0.0001).

For abundance, single moderator models showed that tropical versus non-tropical areas, islands smaller than 25,000 km2 versus mainlands, focal insect flight ability, and year of study publication did not significantly affect the results (Supplementary Fig. 3). Levels within each moderator did not differ from one another. These moderators were not assessed in relation to species richness due to the limited data available for this metric. While a multi-moderator model produced some differing results, due to reduced sample size and variance inflation factors (VIFs) indicating multicollinearity among some moderators, we had less confidence in those estimates (Supplementary Table 2).

Methods
Literature search
Following PRISMA guidelines87,88, we collated studies assessing the impact of invasive alien species on the abundance, biomass, and species richness of our focal taxa (i.e., insects in the orders Hymenoptera, Coleoptera, Orthoptera, and Hemiptera), relative to areas without invasive alien species present (Supplementary Fig. 4). We focused on primarily terrestrial insect orders for which invasive alien species had previously been identified by experts as a major potential threat43. This assessment evaluated 12 insect orders (Lepidoptera, Hymenoptera, Coleoptera, Diptera, Phasmatodea, Orthoptera, Hemiptera, Dermaptera, Odonata, Ephemoptera, Plecoptera, and Trichoptera), representing 96% of described insect species. Of these, invasive alien species were ranked among the top 10 threats for Hymenoptera, Coleoptera, Orthoptera, Hemiptera, Odonata, Ephemoptera, Plecoptera, and Trichoptera. For this meta-analysis, we focused on the four primarily terrestrial orders from this group: Hymenoptera, Coleoptera, Orthoptera, and Hemiptera.

Our final search was conducted on 3rd March 2023, using both Scopus and Web of Science databases to return peer-reviewed, primary research studies. We used the following search terms: (hymenoptera OR coleoptera OR orthoptera OR grasshopper OR hemiptera) AND (invasi* OR alien OR “non native” OR introduced OR exotic OR novel) AND (abundance OR biomass OR “species richness” OR biodiversity) AND (impact OR effect OR compet*) AND NOT (distribution OR monitor* OR detect* OR spread OR control). We did not impose a publication date cutoff. See our protocol (Supplementary Note 1) and the guidance document for the production and collation of meta-analyses for the GLiTRS (GLobal Insect Threat-Response Synthesis) project89 for further details on the search process, including how the search string was refined.

Screening
Of the studies identified from our final search, those found in both databases were de-duplicated using remove_duplicates() in the litsearchr R package90. We then performed two rounds of screening on the resulting list of studies (Supplementary Data 1 provides full screening and exclusion details). In the first round, we screened the titles and abstracts only using the metagear R package91 and discarded all studies that were irrelevant to our research question. For example, we discarded studies that discussed invasive alien species only as secondary factors and primarily focused on other anthropogenic threats such as urbanisation or land-use change (e.g., conversion to plantation), as attributing observed change to the presence of an invasive alien species is more complicated. We also excluded studies in which insects were considered only as the invasive alien species, rather than as taxa responding to the presence of invasive alien species. For the second screening round, we downloaded the full text of the remaining studies and conducted a full-text screen based on our inclusion criteria (Supplementary Table 3). Briefly, for a study to be included, it needed to report the abundance, species richness, or biomass of native Hymenoptera, Coleoptera, Orthoptera, and/or Hemiptera in treatment (invasive alien species present) and control (invasive alien species absent) field sites. The data also needed to be reported to at least taxonomic order level and include summary statistics such as the mean, sample size, and a measure of variation, or provide sufficient primary data to calculate these values.

Data extraction
The following data extraction processes were attempted in sequence; where one failed, we applied the next. First, wherever possible, the mean, sample size, and measure of variance for the treatment and control sites were extracted from tables in the main text or supplementary materials. Second, we used the shinyDigitise R package92 to digitise data provided in graphical forms, such as a bar graph or scatter plot. Third, we used the raw data (if provided) to calculate the mean, sample size, and measure of variance. Lastly, we emailed the authors requesting access to their data.

During data extraction, we came across several scenarios where additional manipulation was required. First, where the authors reported a biodiversity measurement at the plot level (calculated by averaging multiple samples within each plot), we calculated a single biodiversity value for the invaded treatment and non-invaded control sites by calculating a weighted average of the plot-level means and the corresponding standard error, following the method described by Tatebe93. The weighted average of plot-level means S is calculated as

S = (n_a * a + n_b * b) / (n_a + n_b) (1)

where n_a and n_b are the sample sizes for plot a and plot b, respectively. The corresponding standard error ε is calculated as

ε = sqrt((n_a * ε_a^2 + n_b * ε_b^2 + (n_a * n_b * (a - b)^2) / (n_a + n_b)) / (n_a + n_b)) (2)

where ε_a and ε_b are the standard errors associated with plots a and b, respectively; n_a and n_b are the sample sizes, and a and b are the means, always defined as those where invasive aliens were present and absent, respectively, regardless of the description of the authors (e.g., a treatment where invasive alien species were removed).

To avoid duplication and pseudo-replication, we applied the following rules. First, we extracted data to the most refined taxonomic level available. For example, if a study reported results on Hymenoptera overall, and individual species such as Bombus lapidaries and Andrena minutula, we would extract the data for the individual species, and not include an additional data point for the order overall. Second, if a study reports results for multiple years, we only took the most recent data. Third, if a study reports results for multiple levels of invasion e.g., marginally invaded, moderately invaded, and extremely invaded, we only extracted the most extreme comparison (invasive alien species absent versus extremely invaded) to best reflect the definition of invasive alien species absent versus invasive alien species present.

Along with the mean, sample size, and variance measures, we extracted additional variables to serve as moderator variables for our second research question. To this end, we extracted year of publication, taxonomic description of the focal insect taxa, invasive alien species name, geographical realm (tropical if between 23 degrees north and 23 degrees south, otherwise non-tropical), whether the sites were on an island smaller than 25,000 km2, and whether the focal taxa could fly or not (ants were defined as non-flying). We were unable to extract data describing the intensity of the invasion since this information is typically not reported in a comparable or standardised way.

Spot checks were conducted at the study screening and data extraction stages by a second author. The second author screened 50 studies according to the same inclusion criteria and extracted data from five studies using the same data extraction spreadsheet. For the screening spot check, the calculated kappa statistic of 0.85 suggests very good agreement between the two authors. No concerning differences (e.g., strongly different values or different groups of values) were identified between the authors’ sets of extracted data.

Effect size calculation
Our dataset contains pairwise comparisons of the abundance, species richness, and biomass of Hymenoptera, Coleoptera, Hemiptera, and Orthoptera in sites with and without invasive alien species present. Any variance measures reported as standard error were converted to standard deviation before calculating the effect size for each pairwise comparison using the escalc() function from the metafor R package94. We chose the log response ratio (LRR) as our effect size due to its popularity in ecological meta-analyses for quantifying proportionate change and its robustness to non-independence95,96. A negative LRR indicates lower abundance, species richness, or biomass of the focal insect taxa when the invasive alien is present relative to a matching site in which the invasive alien is absent. An LRR close to zero indicates little effect relative to the control. As a high proportion of our extracted mean biodiversity measures were close to zero (Supplementary Fig. 5), we applied a bias correction to our effect sizes and associated variances using the delta method97. Accordingly, the adjusted effect sizes were calculated as

Adjusted LRR = LRR + (1/2 * (N_T * SD_T^2) / X_T^2) - (1/2 * (N_C * SD_C^2) / X_C^2) (3)

where SD_T and SD_C are the standard deviations, N_T and N_C are the sample sizes, and X_T and X_C are the means, respectively. The adjusted variance was calculated as

Adjusted var = var + (1/2 * (N_T * SD_T^2) / X_T^2)^2 + (1/2 * (N_C * SD_C^2) / X_C^2)^2 (4)

To make the effect sizes more interpretable, we converted the adjusted LRRs to percentage changes98

Percentage change = 100 * (e^LRR - 1) (5)

where e^LRR is the exponent of the log response ratio.

One effect size had substantially greater variance than all others (more than 25 times greater adjusted variance than the effect size with the second greatest adjusted variance), due to a relatively large standard deviation on a mean that was less than 0.1 (i.e., a poorly sampled insect species). This high variance effect size was removed before running any meta-analytic models.

Meta-analytic models
We used the rma.mv() function from metafor94 to run multi-level mixed-effects meta-analytic models for the estimation of a pooled effect size and 95% confidence intervals. The model specification was as follows

metafor::rma.mv(yi, vi, random = ~ 1 | PaperID / Observation ID) (6)

where yi represents the effect size (LRR) for each individual observation and vi is the corresponding variance for each effect size. As effect sizes within a paper have a unique methodological context to which they relate, nested paper-level and observation-level random effects were used to account for non-independence within papers. Models were run separately for abundance and species richness, while biomass was not analysed due to too few effect sizes (1 study, 3 effect sizes). We considered invasive alien species to have a significant effect on insect biodiversity if the 95% confidence intervals of the overall model-derived effect did not overlap zero.

As is typical in an ecological meta-analysis, we expected high heterogeneity due to the differing contexts each effect size was collected under54,99. We quantified what proportion of this heterogeneity was due to within- and between-study differences using the var.comp() function from the dmetar R package100, which provides multi-level I2 (heterogeneity) estimations.

We ran further meta-analytic models to investigate variables likely to influence the direction or magnitude of the overall effect, using a series of meta-regression models with factors included as categorical predictors. These moderators included the year the study was published, the insect order of the focal taxon, whether the invasive alien species was a plant or animal, whether the data were collected in a tropical or non-tropical location, whether the data were collected from an island smaller than 25,000 km2, and whether the focal taxon was known to fly. The metafor R package94 provides the output of the QM test of moderators (an omnibus test) to indicate whether the included moderator explains a significant proportion of the heterogeneity, thus indicating there are differences between the groups. For the abundance data, we additionally tested a multi-moderator meta-regression including all moderators simultaneously. However, this approach reduced the sample size by 30%, and multicollinearity among moderators led to imprecise estimates. We therefore chose to model moderators separately—an approach commonly used in ecological meta-analyses34,40, and one that reflects our aim to test distinct hypotheses for each moderator and assess whether they influence the direction or magnitude of the effect.

Models sensitivity and publication bias checks
We took several steps to ensure confidence that our conclusions are supported by the evidence we present. Specifically, we followed the Koricheva and Gurevitch checklist101 (Supplementary Note 2) for meta-analyses, meaning we used formal meta-analysis methodologies, clear documentation of the bibliographic search process, explicit inclusion and exclusion criteria, and thorough assessment of heterogeneity and potential bias.

To assess publication bias (i.e., whether studies with a particular effect have been selectively over- or under-published), we generated funnel plots to check for asymmetry. Additionally, we ran the rank correlation test with the ranktest() function from metafor94 to formally assess funnel plot asymmetry. As an additional publication bias check, we implemented an adapted version of Egger’s regression102, which quantifies the relationship between effect sizes and their uncertainty, and is better suited than traditional Egger’s regression and fail-safe numbers for datasets with non-independent effect sizes.

Finally, we conducted a series of sensitivity tests to assess whether using a different effect size metric or a certain subset of data changed the results. First, we re-ran the models with Hedge’s g as the effect size instead of the LRR. Second, we ran our original models with only data points that passed Geary’s test, defined as97

(X^4 * N^3) / (SD^4 * (1 + 4N)) >= 3 (7)

where X is the mean, SD is the standard deviation, and N is the sample size of the biodiversity measure. For inclusion, both the treatment and control group must meet this rule based on their respective means, standard deviations, and sample sizes. Third, we ran the models on data points that are not disproportionately influential (as assessed by Cook’s distance: data points were excluded if their Cook’s distance exceeded 4/N). Fourth, although our focal taxa are typically terrestrial, some species within the focal orders are aquatic (such as Gerridae in Hemiptera). As our research question is primarily focused on the effect of invasive alien species on terrestrial insects, we also re-ran the models excluding any data that were collected via aquatic sampling methods (e.g., kick sampling). Lastly, we re-ran the abundance model incorporating a phylogenetic correlation matrix as a random effect to account for shared evolutionary history among taxa. After restricting the data to include only species-level data, we used the rotl R package103 to import the phylogenetic data from the Open Tree of Life. We then used the ape R package104 to apply Grafen’s method (with the height argument set to its default value of 1) to estimate branch lengths, and to convert the tree to a correlation matrix for inclusion in the model.

Analyses were completed in R statistical software version 4.4.1105. We used multiple R packages for data preparation, analysis, and visualisation, including litsearchr 1.0.090, metagear 0.791, writexl 1.5.1106, shinyDigitise 0.1.092, metafor 4.6-094, dmetar 0.1.0100, tidyverse 2.0.0107, rnaturalearth 1.0.1108, waffle 1.0.2109, ggimage 0.3.3110, orchaRd 2.0111, rotl 3.1.0103, ape 5.8-1104, stringr 1.5.1112, Polychrome 1.5.4113, and cowplot 1.1.3114.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study is a meta-analysis focusing on the effects of invasive alien species on insect abundance and species richness. The authors found that both insect abundance and richness are lower in sites with alien species. The strongest effects are observed on the abundance of Hemiptera and the richness of Hymenoptera and alien animals had a stronger effect than alien plants. This is a very important study that enriches our understanding the impact of invasive alien species on insect communities. As the authors point out, insects are seldom considered the victims of invasions. I would also like to congratulate the authors for using a pre-registration for their study.\nGeneral comments\n1) I would strongly recommend the authors to incorporate a phylogeny matrix in the meta-analytic models (this matrix can be constructed using the Open Tree of Life and the respective R packages ape and rotl and the matrix can be fitted in the model with the use of the argument “R” of the function rma.mv). More information about phylogenetic meta-analyses can be found here: https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13760.\n2) Somehow it is not clear throughout the manuscript that the category ‘Animals’ includes insects. Could you please clarify this in the Results (in the section ‘Data description’)?\n3) Some important metrics are missing from the Results section, i.e. tau-squared (to quantify the variance among true effect sizes) and the Q test for heterogeneity (for moderators). Could you please add them?\n4) It might be interesting to examine the effect of ants and dicotyledon plants with a subgroup analysis.\nSpecific comments\n1) L83: Please replace the full stop with a (semi)colon after the phrase “research questions”.\n2) L91-93: Here there is a mix-up between two types of biases; publication bias and time-lag bias. The authors have examined both, but the phrasing here is not optimal.\n3) L103: Please replace the full stop with “, while”.\n4) Figure 1C: What is shown in the axes? If the aim is to show just the relative frequencies of the different taxa, I would recommend a more complex figure that shows the distribution of invasive taxa per continent/country or per publication year.\n5) L120: Rephrase to “The abundance of Hymenoptera, Coleoptera, Hemiptera, and Orthoptera was…”\n6) L135, L146: Hedge’s g is written with a capital H.\n7) L187: Very nice idea to link the results to other threats.\n8) L206: Could you please elaborate on the statement about competition?\n9) L240-241: How do we know that the West Indian marsh created a favourable habitat for Coleoptera?\n10) L228: context-dependent\n11) L296: I think the lack of studies that report presence/absence is expected, because the search query excluded the keyword distribution. The reason for this is because presences and absences are the most commonly used data in species distribution models.\n12) L324: Please rephrase to “PRISMA guidelines”.\n13) L357-358: I would move this Table to the Supplementary Material and just briefly mention the most important criteria in the text.\n14) L406-436: The structure of this section is a bit confusing. Please re-structure so that the text presents the calculations that were done in a chronological order (e.g. the conversion to Percentage change probably took place after the calculation of the Adjusted LRR and its variance).\n15) L456-459: This sentence is too long.\n(Remarks on code availability)\nThe code is well structured. I was able to reproduce most of it.", "reviewer_2": "This meta-analysis is the first to quantitatively synthesize impacts of all types of invasive species on terrestrial insects. While previous studies have examined impacts of specific groups of invaders such as other terrestrial invertebrates, this more comprehensive assessment allows a better understanding of the vulnerability of insects to invasive alien species. The methods used in this manuscript are very clearly written and seem appropriate, and the results are also clearly described. I have fairly minor comments:\nIn the methods section, fail-safe numbers are used to assess publication bias, but I believe these are unable to handle non-independence among effect sizes and also don’t control for heterogeneity (see https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13724). The funnel plots that are also used are more appropriate, but it would be best to at least colour the points in the funnel plot so that effect sizes coming from the same study are the same colour (as suggested in the above paper). Also see https://environmentalevidencejournal.biomedcentral.com/articles/10.1186/s13750-023-00301-6#Sec12 for another possible method (an extension of Egger’s regression). Also in the methods, please add the citation for the R program itself.\nWhy were separate sub-group analyses chosen to look at moderators individually, versus a meta-regression approach, which apparently can be more powerful statistically (https://environmentalevidencejournal.biomedcentral.com/articles/10.1186/s13750-023-00301-6#Sec8; https://link.springer.com/article/10.1007/s10682-012-9555-5)? I know that sub-group analyses have been more common in the literature, but it seems like this study is well-suited for meta-regression.\nIt is not entirely clear to me if the 12 insect orders evaluated by Bladon et al. were other aquatic orders, or terrestrial orders that were not identified as being significantly threatened by invasive species. This should be clarified and potentially those orders should be listed there, since it isn’t possible for readers to refer to the manuscript in preparation.\nReference 91 (Tatebe) seems incomplete, and it would be better to give more explanation on this method in the text as well.\nIn Figure 1b, there should be a scale indicating what the circle size means (presumably it is proportional to the number of studies, but what size equates to what number of studies?)\n(Remarks on code availability)\nI was able to install and run the code, and reproduce the results for the models. The dmetar package did not seem to be available for the latest version of R, and therefore I did not manage to run the var.comp function to calculate the I2 but presumably it would work with an earlier version."}, "author_response": "Response to reviewers\nDear Reviewer #1, and Reviewer #2,\nThank you very much for your feedback and comments on our manuscript, ‘Meta-analysis reveals negative but highly variable impacts of invasive alien species on terrestrial insects’. We agree with the revisions recommended and have made the corresponding changes, which we believe have strengthened our meta-analysis.\nOverall, we have:\n● Conducted additional models and tests to improve the robustness of our results by accounting for non-independence in the data. For example, we explored whether incorporating a phylogeny matrix as a random effect improved model fit and replaced the fail-safe number test with an adapted version of Egger’s regression to assess publication bias, due to its superior ability to handle non-independent effect sizes.\n● Provided further clarification on key concepts to improve readers’ understanding and interpretation of our analyses. Specifically, we clarified that the ‘Animals’ invasive category includes insects, more clearly described the types of bias investigated and the models employed, explained our rationale for modeling moderators separately, and explicitly reported additional metrics for variance and heterogeneity.\n● Improved the manuscript’s clarity and readability by moving some methodological details to the supplementary materials, and updating figures to highlight key messages more effectively.\nSpecific changes are detailed in blue text below each corresponding point of feedback. We include line numbers corresponding to the revised manuscript with tracked changes on, along with screenshots to locate the relevant changes.\nThank you for these valuable recommendations which have helped to improve our manuscript.\nOn behalf of all authors,\nGrace Skinner\nReviewer #1\nRemarks to the Author\nThis study is a meta-analysis focusing on the effects of invasive alien species on insect abundance and species richness. The authors found that both insect abundance and richness are lower in sites with alien species. The strongest effects are observed on the abundance of Hemiptera and the richness of Hymenoptera and alien animals had a stronger effect than alien plants. This is a very important study that enriches our understanding the impact of invasive alien species on insect communities. As the authors point out, insects are seldom considered the victims of invasions. I would also like to congratulate the authors for using a pre-registration for their study.\nThank you for taking the time to comment on our manuscript and for your positive feedback. We address each of your comments below in turn.\nGeneral comments\n1) I would strongly recommend the authors to incorporate a phylogeny matrix in the meta-analytic models (this matrix can be constructed using the Open Tree of Life and the respective R packages ape and rotl and the matrix can be fitted in the model with the use of the argument “R” of the function rma.mv). More information about phylogenetic meta-analyses can be found here: https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.13760\nWe now also test rma.mv model accounting for phylogeny, using the rotl and ape packages to construct a tree. Given we were only able to construct a tree for a reduced set of effect sizes (only 40% of the data points are at species-level) this model was performed on a filtered subset. We show that for this reduced set of effect sizes, a model without the phylogenetic structure had the lower AIC value (AIC = 385.8 with phylogeny matrix vs AIC = 384.7 without). To clarify for the reader, we now introduce this sensitivity test in the methods (L577) and refer to it in the results (L149-150).\n2) Somehow it is not clear throughout the manuscript that the category ‘Animals’ includes insects. Could you please clarify this in the Results (in the section ‘Data description’)?\nWe have added a clarification in the ‘Data description’ section stating that invasive alien animals include insects (L111).\n3) Some important metrics are missing from the Results section, i.e. tau-squared (to quantify the variance among true effect sizes) and the Q test for heterogeneity (for moderators). Could you please add them?\nThank you for raising this, we agree that these statistics are important to include. For our multilevel models, heterogeneity was partitioned into variance in effect sizes both between studies and within studies. We now report this variance between and within studies, along with the results of the QE tests for residual heterogeneity for the models with moderators (L129-136, L179-183, L191-195).\n4) It might be interesting to examine the effect of ants and dicotyledon plants with a subgroup analysis.\nThank you for the suggestion to examine the effects of invasive ants and dicotyledon plants separately. Given that ants and dicots comprised the majority of the data in the broader categories of invasive animals and plants, respectively, we found that additional subgroup analyses yielded very similar results to our original analysis using the broader animal vs. plant categorisation: ants significantly reduced native insect abundance, while dicots did not (see figure below). As such, we have decided not to include this additional result to avoid redundancy. However, we have updated the main text to note that the majority of effect sizes for invasive plants were dicots, building on the original statement that most invasive animals were ants (L190-191).\nSpecific comments\n1) L83: Please replace the full stop with a (semi)colon after the phrase “research questions”.\nWe have replaced the full stop with a colon (L85).\n2) L91-93: Here there is a mix-up between two types of biases; publication bias and time-lag bias. The authors have examined both, but the phrasing here is not optimal.\nReviewer 1 is correct that we tested for two forms of bias here. We include a moderator for year of publication, grouped into four time periods, to test the extent to which publication date predicts the reported effect of invasive alien species. Second, we visually inspected funnel plots, performed a rank correlation test, and implemented an adapted version of Egger’s regression. We clarify for the reader our description of the year of publication moderator at the end of the introduction (L93-94), and then refer the reader to our tests for publication bias in the methods (L549-555).\n3) L103: Please replace the full stop with “, while”.\nWe have replaced this full stop with “, while” (L107).\n4) Figure 1C: What is shown in the axes? If the aim is to show just the relative frequencies of the different taxa, I would recommend a more complex figure that shows the distribution of invasive taxa per continent/country or per publication year.\nFigure 1C is a waffle plot representing our 318 effect sizes, with each rectangle corresponding to one effect size. We now clarify this for the reader both on the figure and in the legend. For years of publication and the geographic distribution of effect sizes, we refer the reader to panels 'a' and 'b'.\n5) L120: Rephrase to “The abundance of Hymenoptera, Coleoptera, Hemiptera, and Orthoptera was…”\nWe have rephrased this sentence according to your suggestion (L125).\n6) L135, L146: Hedge’s g is written with a capital H.\nWe have capitalised Hedge’s g (L146, L160).\n7) L187: Very nice idea to link the results to other threats.\nThank you for the positive feedback!\n8) L206: Could you please elaborate on the statement about competition?\nWe have revised the sentence to clarify that the greater impact of invasive alien animals on native insects may be due to more direct competition for similar resources, compared to the more indirect effects of invasive alien plants, which may take longer to become apparent (L234-237).\n9) L240-241: How do we know that the West Indian marsh created a favourable habitat for Coleoptera?\nThe statement about West Indian marsh grass creating a favorable habitat for Coleoptera is based on the findings of Houston and Duivenvoorden (2002), who observed higher numbers of Coleoptera in invaded beds compared to native plant beds. While this suggests that the habitat may be more favorable for Coleoptera, we acknowledge that this is a correlation, and we have adjusted the manuscript accordingly to reflect this. We have added an additional citation for the Houston and Duivenvoorden (2002) study to provide further clarity. (L272-273).\n10) L228: context-dependent\nWe have hyphenated ‘context-dependent’ (L259).\n11) L296: I think the lack of studies that report presence/absence is expected, because the search query excluded the keyword distribution. The reason for this is because presences and absences are the most commonly used data in species distribution models.\nWe have included a statement to explain that the lack of studies reporting incidence is likely due to the exclusion of the keywords ‘incidence’ and ‘distribution’ in our search (L331-333).\n12) L324: Please rephrase to “PRISMA guidelines”.\nWe have rephrased to “PRISMA guidelines” (L360).\n13) L357-358: I would move this Table to the Supplementary Material and just briefly mention the most important criteria in the text.\nWe agree that this level of detail is not essential to the main text, so we have moved the table with the full inclusion criteria to the Supplementary Material (Table S2) and provided a concise summary of the key criteria in the main text (L397-402).\n14) L406-436: The structure of this section is a bit confusing. Please re-structure so that the text presents the calculations that were done in a chronological order (e.g. the conversion to Percentage change probably took place after the calculation of the Adjusted LRR and its variance).\nWe have rearranged this section such that we now first introduce the equation and text for the calculation of the adjusted log response ratio, and then the conversion of that adjusted log response ratio to a percentage change (L495-499).\n15) L456-459: This sentence is too long.\nWe have revised the sentence by breaking it into two sentences (L525-532).\nRemarks on code availability\nThe code is well structured. I was able to reproduce most of it.\nThis is good to hear!\nReviewer #2\nRemarks to the Author\nThis meta-analysis is the first to quantitatively synthesize impacts of all types of invasive species on terrestrial insects. While previous studies have examined impacts of specific groups of invaders such as other terrestrial invertebrates, this more comprehensive assessment allows a better understanding of the vulnerability of insects to invasive alien species. The methods used in this manuscript are very clearly written and seem appropriate, and the results are also clearly described. I have fairly minor comments:\nThank you for taking the time to comment on our manuscript and for your positive feedback. We address each of your comments below in turn.\nIn the methods section, fail-safe numbers are used to assess publication bias, but I believe these are unable to handle non-independence among effect sizes and also don’t control for heterogeneity (see https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.13724). The funnel plots that are also used are more appropriate, but it would be best to at least colour the points in the funnel plot so that effect sizes coming from the same study are the same colour (as suggested in the above paper). Also see https://environmentalevidencejournal.biomedcentral.com/articles/10.1186/s13750-023-00301-6#Sec12 for another possible method (an extension of Egger’s regression). Also in the methods, please add the citation for the R program itself.\nThank you for this helpful suggestion. We have now removed the fail-safe number analysis, and instead implemented an adapted Egger’s regression to handle the non-independent effect sizes. This change is now reflected in both the Methods (L552-555) and Results (L137-144, L153-156) sections of the manuscript.\nWe have also updated the funnel plots in the Supplementary Materials so that points are coloured by study, and included the number of studies in the figure headings.\nWe also apologise for the oversight in not citing the R program itself. We have added this now (L584).\nWhy were separate sub-group analyses chosen to look at moderators individually, versus a meta-regression approach, which apparently can be more powerful statistically (https://environmentalevidencejournal.biomedcentral.com/articles/10.1186/s13750-023-00301-6#Sec8; https://link.springer.com/article/10.1007/s10682-012-9555-5)? I know that sub-group analyses have been more common in the literature, but it seems like this study is well-suited for meta-regression.\nThank you for this constructive comment. To clarify, our analyses were conducted using meta-regression models. Moderators (e.g., invasive alien type, insect taxonomic order) were included as categorical predictors in meta-regression models, rather than running independent models on subgroups of data and comparing the results of the subgroups using statistical tests (as in traditional subgroup analysis). This follows the approach described in the paper the reviewer cited, where the authors note that:\n“Traditionally, researchers conduct separate meta-analyses per different groups (known as ‘sub-group analysis’), but we prefer a meta-regression approach with a categorical variable, which is statistically more powerful [40].” We have clarified the description of our approach in the manuscript by explicitly stating that meta-regression models were used, and that we ran a series of meta-regression models with our moderator variables (L525-528).\nWe recognise the reviewer may be suggesting a multi-moderator meta-regression, where all moderators are included simultaneously. We explored this for our dataset.\nFor the abundance data, including all moderators in one model reduced the sample size by 30% because flight capability data was not available for 84 of the 277 effect sizes. Additionally, there were issues with non-independent moderators and unbalanced moderator levels. For example, only 20 data points came from islands compared to 257 from mainlands, and 18 of the 20 data points originating from islands were also from tropical biomes. We found substantial multicollinearity, with variance inflation factors (VIFs) exceeding 3 for half of the parameters and reaching up to 8. This likely contributed to the imprecise estimates, where standard errors were often larger than the estimates themselves.\nFor species richness, while the multi-moderator model converged, the sample size (n = 40) was insufficient to robustly estimate the large number of parameters, falling well below recommended thresholds of including at least 10 studies per moderator modelled (Cochrane handbook section 10.11.5.1 https://www.cochrane.org/authors/handbooks-and-manuals/handbook/current/chapter-10#section-10-11-5). Even in the single moderator models, the flight capability, island/mainland, tropical/non-tropical, and year moderators were not analysed due to very limited data for at least one level of each moderator.\nGiven these issues, we chose to present the single meta-regression models for both abundance and species richness in the main text, an approach widely used in ecological meta-analyses (Tercel et al. (2023); Wang et al. (2020)), especially when datasets are limited or contain missing moderator data. For transparency, we include the results of the multi-moderator abundance model in the Supplementary Material (Supplementary Table 3). We also introduce the approach in the methods (L535-540) and comment on the findings in the results (L200-203).\nIt is not entirely clear to me if the 12 insect orders evaluated by Bladon et al. were other aquatic orders, or terrestrial orders that were not identified as being significantly threatened by invasive species. This should be clarified and potentially those orders should be listed there, since it isn’t possible for readers to refer to the manuscript in preparation.\nThank you for highlighting that this was not clear. We have now listed all 12 insect orders evaluated in Bladon et al., clarified which were identified as being significantly threatened by invasive alien species, and specified the four primarily terrestrial orders from this group that formed the focus of our meta-analysis (L365-372).\nReference 91 (Tatebe) seems incomplete, and it would be better to give more explanation on this method in the text as well.\nThank you for pointing this out. Reference 91 is not a standard journal article, and the formatting was incorrect as a result. We have now updated both the in-text citation and the reference in the bibliography. We have expanded the explanation in the main text by explicitly including the relevant equations from the source (L421-436).\nIn Figure 1b, there should be a scale indicating what the circle size means (presumably it is proportional to the number of studies, but what size equates to what number of studies?)\nWe have added an additional legend to Figure 1b to indicate how many studies each circle represents.\nRemarks on code availability\nI was able to install and run the code, and reproduce the results for the models. The dmetar package did not seem to be available for the latest version of R, and therefore I did not manage to run the var.comp function to calculate the I2 but presumably it would work with an earlier version.\nThank you for testing the code and confirming the results. Apologies that you were unable to run the var.comp function from the dmetar package. We were able to install dmetar using the instructions at https://dmetar.protectlab.org/ on R version 4.4.2. However, we’ve noted that R version 4.5.0 is currently being rolled out, which may not yet support the package."}, {"round": 2, "reviewer_comments": {"reviewer_2": "The responses to both of the reviewers are thorough and I do not see any further issues in the revised manuscript. I also ran some of the new code and did not encounter any problems.\n(Remarks on code availability)", "reviewer_3": "This meta-analysis is a great contribution to our understanding of insect declines! It is well performed and documented, and the authors seem to have properly addressed most previous referees concerns. I believe the statistical analysis is sound, and the authors have properly considered potential bias and non-independence. I have just a couple of comments.\nFirst, I am concerned about their restriction of the taxa included. I understand that they were focusing on taxa more likely to be affected by invasive species based on an unpublished manuscript. But I cannot access the manuscript and I cannot know how these taxa were assessed. Not being able to evaluate the manuscript used to exclude other taxa is relatively minor. A main concern for me is that they are excluding two very abundant and rich orders: Lepidoptera and Diptera. These two orders together can account for about 300,000 species, whereas Orthoptera and Hemiptera together account for about 100,000 species. Thus, because they are not including almost a third of the insects, I believe the authors cannot say this is a test of the effect of invasive species on terrestrial insects. And they also did not find a negative effect on Coleoptera, the most diverse insect order. Lastly, by focusing on orders more likely to be affected by invasive species, they might be more prone to finding negative effects. This restriction could be by itself a bias in their study. I suggest toning down some of the main conclusions (e.g., line 332 and title).\nSecond, I believe this meta-analysis is novel in focusing solely on insects and including all types of invasive species, but there are other meta-analyses that have evaluated the effect of invasive species on insects. I found at least four meta-analyses that consider the impact of invasives on animals (not just insects, but at least a good portion of the taxa evaluated are insects or they have subdivisions by taxon), and one specific about Lepidoptera. These meta-analyses should be considered to be included in the paragraph starting in line 56 (and maybe might be useful for the rest of the manuscript as well):\nCharlebois, J. A., & Sargent, R. D. (2017). No consistent pollinator‐mediated impacts of alien plants on natives. Ecology Letters, 20(11), 1479-1490.\nFletcher, R. A., Brooks, R. K., Lakoba, V. T., Sharma, G., Heminger, A. R., Dickinson, C. C., & Barney, J. N. (2019). Invasive plants negatively impact native, but not exotic, animals. Global Change Biology, 25(11), 3694-3705.\nYoon, S. A., & Read, Q. (2016). Consequences of exotic host use: impacts on Lepidoptera and a test of the ecological trap hypothesis. Oecologia, 181(4), 985-996.\nMontero‐Castaño, A., & Vilà, M. (2012). Impact of landscape alteration and invasions on pollinators: a meta‐analysis. Journal of Ecology, 100(4), 884-893.\nSchirmel, J., Bundschuh, M., Entling, M. H., Kowarik, I., & Buchholz, S. (2016). Impacts of invasive plants on resident animals across ecosystems, taxa, and feeding types: a global assessment. Global change biology, 22(2), 594-603.\n(Remarks on code availability)\nCode seems okay."}, "author_response": "Response to reviewers (second revision)\nDear Reviewers,\nWe thank the reviewers for their time and helpful feedback on our manuscript, ‘Meta-analysis reveals negative but highly variable impacts of invasive alien species on terrestrial insects’. We understand that Reviewer #1 was not available to provide further comments in this round. We appreciate the positive feedback from Reviewer #2 and the constructive comments from Reviewer #3, and we have addressed all their comments in detail below.\nOverall, we have:\n● Refined our conclusions to better reflect the study’s taxonomic scope. We have more explicitly stated throughout the manuscript – including in the abstract and first sentence of the Discussion – that our findings apply to the subset of terrestrial insect orders included in the study.\n● Expanded the Discussion to note that broader inclusion of insect taxa (beyond those orders identified by experts as having invasive alien species among their top threats) might reveal more variable and less negative responses.\n● Replaced reference to an unpublished manuscript (Bladon et al.) with citation of a now-published paper (Millard et al., 2025; https://doi.org/10.1111/ddi.70025) that presents the referenced material, explaining some of our rationale and approach for the expert ranking of threats.\n● Placed greater emphasis on the conclusion of ‘highly variable impacts’ in key summary paragraphs; and cited existing literature where broader animal-focused meta-analyses (which included, but did not focus on, insects) reported similarly negative but variable effects.\n● Added additional citations to relevant meta-analyses to provide further context when introducing our study.\nSpecific changes made in response to the current round of feedback are detailed in blue text below each corresponding point. We include line numbers corresponding to the second-round revised manuscript, which shows only the new tracked changes made since the previous submission. Screenshots are included to locate the relevant changes.\nThank you for these additional recommendations.\nOn behalf of all authors,\nGrace Skinner\nReviewer #2\nRemarks to the Author\nThe responses to both of the reviewers are thorough and I do not see any further issues in the revised manuscript. I also ran some of the new code and did not encounter any problems.\nWe thank Reviewer #2 for their positive feedback and for confirming they have no further concerns. We also appreciate the valuable feedback they provided in the previous round.\nReviewer #3\nRemarks to the Author\nThis meta-analysis is a great contribution to our understanding of insect declines! It is well performed and documented, and the authors seem to have properly addressed most previous referees concerns. I believe the statistical analysis is sound, and the authors have properly considered potential bias and non-independence. I have just a couple of comments.\nThank you for taking the time to review our manuscript. We appreciate the encouraging and positive comments, and we have addressed the comments you have raised below.\nFirst, I am concerned about their restriction of the taxa included. I understand that they were focusing on taxa more likely to be affected by invasive species based on an unpublished manuscript. But I cannot access the manuscript and I cannot know how these taxa were assessed. Not being able to evaluate the manuscript used to exclude other taxa is relatively minor. A main concern for me is that they are excluding two very abundant and rich orders: Lepidoptera and Diptera. These two orders together can account for about 300,000 species, whereas Orthoptera and Hemiptera together account for about 100,000 species. Thus, because they are not including almost a third of the insects, I believe the authors cannot say this is a test of the effect of invasive species on terrestrial insects. And they also did not find a negative effect on Coleoptera, the most diverse insect order. Lastly, by focusing on orders more likely to be affected by invasive species, they might be more prone to finding negative effects. This restriction could be by itself a bias in their study. I suggest toning down some of the main conclusions (e.g., line 332 and title).\nThank you for raising this point regarding the taxonomic scope of the meta-analysis. We clarify that our results are not necessarily representative of all terrestrial insects, but rather the subset included in our study. We now explicitly state this in the abstract (L32), at the start of the last paragraph of the Introduction (L83), and in both the first sentence (L200-203) and the concluding paragraph of the Discussion (L344).\nRegarding the absence of a negative effect for Coleoptera, this is reflected in the ‘highly variable impacts’ highlighted in our manuscript’s title. To reinforce this narrative, we now emphasise this variability in the Abstract (L33) and the Discussion (L203 and L343-344).\nWe recognise that focusing on expert-selected groups may bias results toward detecting negative impacts. We expand the Discussion to highlight that broader inclusion of terrestrial insects might reveal a more variable and on average less negative response (L208-210). Given it has now been published, we now also cite and paraphrase our recent paper in Diversity and Distributions (citation 43 in our manuscript), where we explain some of our rationale and approach for the expert ranking of threats (Methods L359, Introduction L89). Wherever we have previously cited the in-preparation expert elicitation paper, we now instead cite this paper in Diversity and Distributions\".\nSecond, I believe this meta-analysis is novel in focusing solely on insects and including all types of invasive species, but there are other meta-analyses that have evaluated the effect of invasive species on insects. I found at least four meta-analyses that consider the impact of invasives on animals (not just insects, but at least a good portion of the taxa evaluated are insects or they have subdivisions by taxon), and one specific about Lepidoptera. These meta-analyses should be considered to be included in the paragraph starting in line 56 (and maybe might be useful for the rest of the manuscript as well):\nCharlebois, J. A., & Sargent, R. D. (2017). No consistent pollinator‐mediated impacts of alien plants on natives. Ecology Letters, 20(11), 1479-1490.\nFletcher, R. A., Brooks, R. K., Lakoba, V. T., Sharma, G., Heminger, A. R., Dickinson, C. C., & Barney, J. N. (2019). Invasive plants negatively impact native, but not exotic, animals. Global Change Biology, 25(11), 3694-3705.\nYoon, S. A., & Read, Q. (2016). Consequences of exotic host use: impacts on Lepidoptera and a test of the ecological trap hypothesis. Oecologia, 181(4), 985-996.\nMontero‐Castaño, A., & Vilà, M. (2012). Impact of landscape alteration and invasions on pollinators: a meta‐analysis. Journal of Ecology, 100(4), 884-893.\nSchirmel, J., Bundschuh, M., Entling, M. H., Kowarik, I., & Buchholz, S. (2016). Impacts of invasive plants on resident animals across ecosystems, taxa, and feeding types: a global assessment. Global change biology, 22(2), 594-603.\nThank you for highlighting these relevant meta-analyses. We agree that these studies provide valuable context and further support for our findings of negative but highly variable impacts of invasive species on insects. We now cite three of these studies (Fletcher et al., 2019; Montero‐Castaño and Vilà, 2012; Schirmel et al., 2026) where you suggest in the Introduction (L60-62), and again in the Discussion (L203-204) to support our conclusion of ‘negative but highly variable’ effects. We also cite Yoon and Read (2016) in the final Introduction paragraph (L84-85). We chose not to cite Charlebois and Sargent (2017) as their focus is primarily on how invasive plants affect native plants via pollinator-mediated mechanisms, rather than directly on insect pollinators themselves.\nRemarks on code availability\nCode seems okay.\nWe appreciate the reviewer’s assessment of the code. No changes were made to the code during this revision."}]

Decision letter