Gaps in tropical science from unrepresentative distribution of sampling and citation across natural terrestrial environments

Domain: earth_science
Published: 2025-12-20
Editorial outcome: minor_revision

Abstract
Effective environmental policies for the tropics depend on accurate, representative scientific data. However, there is strong evidence from particular disciplines and regions that existing research is patchily distributed. Here, we show that poor representation of sampling and citation in some biomes and across key environmental gradients from all disciplines for the entire tropics may lead to flawed scientific paradigms and inappropriate policy prescriptions. We map sampling locations and citations from 2738 published studies in natural terrestrial tropical environments across all disciplines to identify gaps in field sampling effort and research attention. Five ecoregions – all in moist broadleaf forests – generate 22% of the total citations but cover only 3% of the tropical land area. By contrast, drier biomes with low tree cover account collectively for 57% of the tropical area but generate only 20% of total citations. Locations that are drier, colder, with greater plant species richness, lower tree cover and facing greater climate change extremes are under-sampled and under-cited. Our results will help to correct these imbalances to improve the scientific basis for environmental policies across the tropics.

Introduction
The terrestrial tropics are highly populated and encompass a wide range of valuable yet threatened ecosystems. Numerous international initiatives have emerged to mitigate these threats. These initiatives are shaped by broad syntheses of regional knowledge across all disciplines, drawing on fieldwork by numerous researchers. However, field research effort across the tropics is uneven, with certain areas disproportionately represented while other regions remain relatively overlooked. Previous studies have tracked research activity within particular disciplines, regions or time frames, consistently revealing strong geographic and thematic biases in research effort, citations, peer review and publication. Moreover, site-specific findings may be extrapolated far beyond their original contexts, exacerbating the risk of inappropriate policy applications. Still missing is a comprehensive, cross-disciplinary overview of the spatial distribution of tropical field research sampling and study citation, and a robust assessment of how well this distribution represents the full spectrum of environmental variation across the tropics.

Here, we identify 4260 articles featuring primary field data within the tropics. Habitat types with a high degree of anthropogenic influence (urban and agricultural) account for 36% and 32% of sampling locations and citations, respectively. The spatial distributions of sampling and citation across the tropics are dominated by this prevalence of research on heavily impacted environments. While heavily human-impacted environments in the tropics are widespread and important for policy, they are subject to distinct drivers than environments where direct human influence is minimal. To focus on the spatial distribution and drivers of sampling and citation across relatively natural environments, we remove studies featuring urban and agricultural habitats for subsequent analyses.

Results
Uneven global representation of field research across tropical biomes

The spatial distribution of sampling locations and citations across the tropics is highly variable. The moist broadleaf forest biome covers around 29% of the tropics but accounts for 68% and 73% of all sampling locations and citations, respectively. The top five most cited ecoregions - all in moist broadleaf forests with major field stations and/or resident population centers - account for 11% and 22% of total locations and citations respectively, but cover only 3% of the tropical biome area. By contrast, drier biomes (dry broadleaf forest, deserts and xeric shrublands, grasslands, savannas and shrublands) account collectively for 57% of the tropical area but feature only 21% and 20% of sampling locations and citations respectively. Deserts and xeric shrublands stand out as poorly sampled and cited both in absolute terms, and after correcting for biome area. Mangroves are generally frequently sampled and cited given their limited area, although the Guinean mangroves in west Africa is one of the least cited tropical ecoregions. Flooded grasslands and savannas, and coniferous forests are cited much less than expected given how often they are sampled.

Regional gaps in tropical research

Current sampling efforts capture some tropical habitats and conditions well, while others remain relatively under-sampled. Specifically, current sampling locations adequately represent environmental conditions from only around 30% of the tropics, corresponding mainly with the moist broadleaf forest biome, particularly in Asia. Areas with environmental conditions that are poorly represented by the present distribution of sampling correspond mainly with biomes in drier regions with low tree cover, particularly in Africa.

Representativeness of sampling and citation across the tropical environmental space

The observed distribution of research sampling locations and citations with varying MAT, MAP, ΔMAT, ΔMAP, LAI, SOC, vascular plant, mammal and bird species richness are different from the expected distribution based upon the tropical land area characterized by these same conditions. Specifically, relatively cold (<20 oC MAT) or dry (<1000mm MAP) locations with low LAI (<3 m2 m-2) and predicted to face more climate extremes (greater future warming, cooling or increased precipitation) are less sampled and cited than expected given their spatial extent, which corresponds to the following tropical biomes: dry broadleaf forest, coniferous forest, grasslands, savannas and shrublands. These areas tend to occur at relatively high and low latitudes, and at higher elevations, within the tropics.

Conversely, areas with high LAI and high diversity of mammals and birds, corresponding roughly with the moist broadleaf forest biome, are sampled and cited more often than expected from the frequency of their occurrence. It is important to note, however, that the datasets used to quantify actual biodiversity distributions are themselves likely spatially biased, for many of the same reasons that drives sampling and citation biases. Therefore, the extent to which actual biodiversity distributions are accurately represented by existing research should be interpreted with caution, though the present analysis likely overestimates representation.

Methods
Inclusion & ethics

The authorship team comprises a diverse range of nationalities and career stages with a reasonably balanced gender composition. There is, however, a distinct overrepresentation of North European and American institutions, although several members of the team are nationals of tropical countries but are now employed outside of the tropics. In large part, this reflects the fact that much of the group was initially established to complete a conceptually similar article focused on Arctic systems. For the present analysis, considerable effort was made to widen the authorship group, enlisting assistance and inputs from researchers working in tropical countries, with limited success. Therefore, in the present article, we have taken particular care to evaluate and thoroughly describe the diverse perspectives about the patterns and drivers of regional and global variation in knowledge production.

Literature review

On 3 November 2021, we searched the Web of Science database for articles with the term “trop*” in their title. Wider keyword searches of the abstract or main text were not performed since they yielded an intractably large number of articles. The approach was not designed to yield a complete list of tropical research, but to provide as close to an unbiased subset of tropical research as possible. As such, more specific search terms were avoided since they could introduce biases if particular names or terms were more likely to be used in particular locations or by particular disciplines. Non-English language articles were not screened out but represent a minority of the Web of Science database. Uncited papers were not included because it was assumed that they have not yet exerted much influence over scientific paradigms or policy strategy. We include all studies irrespective of discipline and all time periods, including social sciences and laboratory studies, as long as the geographic origin of the samples was reported. The resulting initial list of 11,804 cited papers was then screened to assess their relevance to our objectives. Of these papers, 11,713 (99% of initially screened papers) were successfully accessed via university institutional access to the publisher in question or by writing to the corresponding author for a personal copy. After full review, papers were excluded (6625, 56.0% of initially screened papers) if: (1) they featured only measurements in marine environments; tidal estuaries were counted as terrestrial and labeled as river habitats; (2) they did not include primary field measurements because they were broad reviews, modeling analyses, the data presented had already been published elsewhere, or the measurements were from laboratory measurements using samples without a clear provenance; (3) the primary field measurements featured were located outside of the tropics and buffer regions as defined in our study. Studies that were not field-based (for example, remote sensing, geographical information science, and modeling analyses) were, in some cases, included where they included ‘ground truthing’ field measurements and/or the spatial extent of the study was relatively limited.

After removing papers that did not fulfill these criteria, 5088 (43% of initially screened papers) papers remained, which were subjected to further analysis. Content analysis was used to: (1) extract geographical coordinates of the field measurements. In cases where coordinates were not explicitly provided, we used place or landform names mentioned in the text to determine the approximate coordinates of the field site(s) on Google Maps; (2) classify the habitats sampled within the paper. The habitats featured were forest, grassland, wetland, desert, rocky area, agricultural, urban, lake, and river. Content analysis inevitably included a degree of subjective judgment on the part of the reviewer. All reviewers were trained at least to a university undergraduate level in environmental sciences and received identical review instructions. Individual papers frequently featured multiple habitats and/or single habitats which represented aspects of multiple habitat categories, in which case a maximum of 3 habitats could be assigned to the same sampling location. The information from the content analysis was then paired with basic paper information derived from Web of Science (authors, journal, title, volume and page numbers, science categories and research areas, citations as of 3 November 2021) to form the central dataset for subsequent analyses.

Mapping study sampling locations and citations

To further define our study domain for spatial analysis, we used the biome boundaries that were classified as tropical in the ecoregions database (i.e. BIOME_NAME field included a word “tropical”). To acknowledge that there might be transition zones between the biomes, we added a buffer of 100km around the tropical area. Overall, our domain consisted of 52.9 × 10^6 km^-2 of terrestrial land (ca. 36% of the global land area). After removing articles that were outside this tropical domain, the number of articles, sampling locations, and citations decreased to 4260, 9987, and 131,030, respectively. Finally, to focus on terrestrial environments with lower intensity of direct human influence, we excluded sampling locations in urban and agricultural areas based on the habitat description in the literature database, which resulted in a dataset of 2738 articles, 6370 sampling locations and 89,468 citations for the final analyses.

Extraction of environmental conditions variables from study site locations

All the data processing and analyses were conducted in R program version 4.4.1. We used a range of climatic, vegetation, soil and biodiversity data to characterize the tropical region as a whole and to extract data to study site locations. From the biome dataset, we utilized the variables ecoregion (ECO_NAME) and biome (BIOME_NAME) to broadly classify articles to key ecological domains. We used the 1-km WorldClim v2 mean annual average air temperature (oC) and annual cumulative precipitation data (mm) over 1970-2000 and 2081-2100 based on the SSP2.45 scenario, produced from an ensemble of 12 downscaled CMIP6 layers. Climate anomaly layers were calculated based on the difference between 2081-2100 and 1970-2000 layers. We used the MODIS MOD15A2H dataset, which provides 500-meter resolution data on Leaf Area Index (Lai_500m). We applied quality filtering to exclude poor-quality pixels (included FparLai_QC bit 0 value 0 data, i.e., good quality) and areas affected by clouds (included FparLai_QC bit 3-4 value 0 data, i.e., no clouds). We then calculated annual means over 2002-2023 and filled gaps in the average MODIS LAI map by applying a moving window analysis (window size: 19) with the focal command in the R package terra. Soil organic carbon stock data for the uppermost 2 meters were extracted from the SoilGrids product. We used a dataset of predicted vascular plant species richness (i.e.: alpha diversity) for a plot size of 1000 m^2 including forest and non-forest species (ca. 5 km pixel resolution). This plot size was chosen as it is commonly used when sampling forests. We further extracted predicted bird and mammal species richness datasets and summed them as one animal diversity measure. All the geospatial layers were re-projected to WGS1984 at 1 km resolution and masked by the climate datasets using the R package terra.

Spatial analyses

We calculated the total number of articles, sampling locations, and citations across biomes and ecoregions. Then, we examined the distribution of sampling locations and citations across the full range of tropical conditions, to compare with the actual prevalence of the same conditions in reality. To describe the conditions across the entire tropics, we took a random sample (n = 100,000) of the total pixels within our study domain.

We used statistical multivariate modeling to highlight areas lacking sampling locations when considering overall environmental variability. This approach is conceptually grounded in species distribution models (SDMs). SDMs define a geographic space based on environmental variables and identify areas where environmental conditions are suitable for a given species. We adapted this framework to evaluate representativeness of sampling locations, aiming to delineate the spatial distribution of environmental conditions across a geographic envelope that reflects the range of environments captured by the current sampling locations.

We used a binomial/categorical response variable for the presence-absence data (1 = sampling location exists, 0 = sampling location is missing), and climate (MAT and MAP), soil (SOC), vegetation (LAI), and biodiversity (plant and animal species richness) as explanatory variables. Since our database contains information about sampling locations only, we needed to artificially create locations with the absence of sampling. To do this, we followed an established methodology, creating a random sample of terrestrial absence locations with the same number of observations as our presence locations (n = 6447) with the R package sp. Then, we obtained spatial data in these randomly sampled locations based on coordinate colocation. These were then combined with the literature database, which resulted in a data frame of 12,894 locations. The predictors in the final dataset did not suffer from high multi-collinearities, as the correlations between the predictor variables were < 0.70.

We used common statistical and machine learning models – generalized additive models, random forest models and generalized boosted regression trees – to predict both the presence-absence of sampling locations and the probabilities for the presence. To reduce uncertainties associated with individual models, we calculated the median probability across the three models, which was used to describe the representativeness of sampling locations for each raster pixel across the whole tropics. In the final map, high probabilities indicate a good coverage of current sampling locations in similar conditions (1 = high probability that there is a sampling location in such conditions), and low probabilities suggest lack of locations (0 = no probability for a sampling location). From these probabilities, we also calculated the total area capturing the environmental conditions where sampling sites are covered (>0.5).

The performance of the three models and their ensemble was assessed using cross-validation with 99 permutations from which we calculated the area under the curve (AUC) test statistic with the R package ROCR. In the cross-validation procedure, a random sample of 70% of the data was used to test the model fit, and the remaining 30% were used to assess predictive performance. Test statistics were calculated after each permutation to evaluate the ensemble model. AUC scores varied from 0.76 to 0.9, with the mean AUC being 0.83. An AUC value of 1 represents perfect accuracy and 0.5 indicates that the model is no better than random. All the visualizations from the spatial analyses were created with the R package ggplot2 and maps with ESRI ArcGIS Pro version 3.0.3.

Use of AI

Large language models were used to copy-edit existing text, to check for errors in grammar and syntax, and to suggest alternative sentence formulations.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Review of NCOMMS-25-46514-T\nI enjoyed reading this manuscript and think could make a nice contribution to the literature. The analysis is satisfyingly\nsimple and, to me, clearly demonstrates that sampling is not representative of the wider environment in the tropics. As I see\nit, the weakest part of the manuscript is that it is not currently grounded in sampling theory. I have suggested some additional\nliterature and left some technical comments that will hopefully improve the paper in this respect. Perhaps most importantly,\nthe term “sampling bias” is used improperly. Sampling bias (i.e. nonignorable or ‘preferential’ sampling) is defined with\nrespect to a specific study variable (Aubry et al., 2024; Boyd et al., 2025; Schouten et al., 2012). Here there is no one study\nvariable, since many studies are considered, so it’s better to stick to the word “representativeness”. It’s probably also worth\nmentioning the potential biases induced by considering only those studies published in English (Serrano et al., 2025).\nSome specific comments:\n71: what does robust mean in this context?\n72: could potentially remove the sentence starting “However,…”; since the following one highlights the problem anyway.\n77: what is an “unrepresentative scientific paradigm”?\n98: what do you mean by biases in peer review?\n101: so you’re looking at the locations of the fieldwork and also the number of citations given the location of the fieldwork?\n103: sentence reads a bit strangely. Would it be better to say you “map 6370 measurements from 2738 published articles\nthat have been cited 89 468 times…”?\n108: this is confusing since you go on to show that these papers are not an unbiased subset.\n111: the justification for dropping urban studies isn’t very strong imo. But if you will delineate your study in this way, then it\nshould be clear in the title and abstract.\n116: It should be clearer at this point that “patterns of citations” refers to the distribution of citations indexed by original study\nlocation (if that’s what you are doing ofc).\n127: these maps of plant and vertebrate species richness are presumably very geographically biased too. Some\nconsideration of what this means for your results is needed.\n149: it might be nice to plot sample vs. population relative frequencies per environmental category in this section. I.e. relative\nfrequencies of sampled locations per category, possibly of citations per category and the actual frequency distribution of\ncategories across the tropics.\n178: what’s a representativeness analysis? If you mean the SDM-type analysis described in the methods, then this is a map\nof predicted probabilities of sampling rather than representativeness. Some would consider the variability in predicted\nprobabilities across pixels the measure of representativeness (Bethlehem et al., 2008). (The idea here is that under simple\nrandom sampling, the usual benchmark for ‘representative’ sampling, every pixel would have an equal probability of being\nincluded in the sample. A greater departure from equal inclusion probabilities indicates a greater departure from simple\nrandom sampling.)\n196: *“vertebrate diversity”\n215: what do you mean by “translate into valuable ecoregions”?\nSentence starting on line 218 doesn’t make sense grammatically.\n221: how? Stratify on these variables when collecting new data (Pescott et al., 2025)? Also see (Henrys et al., 2024;\nSchouten & Shlomo, 2017).\n226: how? Need some substance here about the challenges of extrapolation under unrepresentative sampling.\n227: this is a really nice example.\n238: this section lacks any reference to statistical corrections, which seem like the obvious approach to mitigating the effects\nof sampling biases. See e.g. (Boyd et al., 2023; Elliott & Valliant, 2017).\n240: geographic biases are driven by spatial biases?\n244: it’s good that you have acknowledged this.\n252: this paragraph is closely related to concepts in meta-analysis. It might be worth referring to internal vs. external validity\nand risk of bias assessments, which are common in evidence synthesis and increasingly in ecology.\n273: again, some discussion of how adaptive sampling might work is needed.\nThanks for the opportunity to review this interesting manuscript,\nRob Boyd\nAubry, P., Francesiaz, C., & Guillemain, M. (2024). On the impact of preferential sampling on ecological status and trend\nassessment. Ecological Modelling, 492, 110707. https://doi.org/10.1016/j.ecolmodel.2024.110707\nBethlehem, J., Cobben, F., & Schouten, B. (2008). Indicators for the Representativeness of Survey Response. Statistics\nCanada’s International Symposium Series: Proceedings, 11.\nBoyd, R. J., Botham, M., Dennis, E., Fox, R., Harrower, C., Middlebrook, I., Roy, D. B., & Pescott, O. L. (2025). Using causal\ndiagrams and superpopulation models to correct geographic biases in biodiversity monitoring data. Methods in Ecology and\nEvolution. https://doi.org/10.1111/2041-210X.14492\nBoyd, R. J., Stewart, G. B., & Pescott, O. L. (2023). Descriptive inference using large, unrepresentative nonprobability\nsamples: An introduction for ecologists. Ecology. https://doi.org/10.1002/ecy.4214\nElliott, M. R., & Valliant, R. (2017). Inference for nonprobability samples. Statistical Science, 32(2), 249–264.\nhttps://doi.org/10.1214/16-STS598\nHenrys, P. A., Mondain-Monval, T. O., & Jarvis, S. G. (2024). Adaptive sampling in ecology: Key challenges and future\nopportunities. In Methods in Ecology and Evolution. British Ecological Society. https://doi.org/10.1111/2041-210X.14393\nPescott, O. L., Powney, G. D., & Boyd, R. J. (2025). Adaptive sampling for ecological monitoring using biased data: a\nstratum-based approach. Oikos. https://doi.org/10.1002/oik.11115\nSchouten, B., Bethlehem, J., Beullens, K., Kleven, Ø., Loosveldt, G., Luiten, A., Rutar, K., Shlomo, N., & Skinner, C. (2012).\nEvaluating, Comparing, Monitoring, and Improving Representativeness of Survey Response Through R-Indicators and\nPartial R-Indicators. International Statistical Review, 80(3), 382–399. https://doi.org/10.1111/j.1751-5823.2012.00189.x\nSchouten, B., & Shlomo, N. (2017). Selecting Adaptive Survey Design Strata with Partial R-indicators. International\nStatistical Review, 85(1), 143–163. https://doi.org/10.1111/insr.12159\nSerrano, F. C., Marconi, V., Deinet, S., Puleston, H., Wiederhecker, H. C., Diaz-Ricaurte, J. C., Farhat, C., Luría-Manzano,\nR., Martins, M., de Souza, E., Marques-Souza, S., dos Santos Vieira-Alencar, J. P., Valdujo, P., Freeman, R., & McRae, L.\n(2025). Knowledge from non-English-language studies broadens contributions to conservation policy and helps to tackle\nbias in biodiversity data. Journal of Applied Ecology. https://doi.org/10.1111/1365-2664.70092", "reviewer_2": "This is an interesting and timely contribution that provides valuable perspectives on the biases of sampling and biodiversity\nresearch across the globe, particularly the undersampling of the tropics where biodiversity is highest and threats are most\nsevere. The paper applies robust methods and approaches to examine these biases. However, there are several key areas\nthat I think require greater emphasis and refinement.\nFirst, the reliance on published literature as the basis for “observations” raises questions. Did the authors cross-reference\ntheir findings with GBIF occurrence records or other biodiversity databases that could serve as useful proxies for sampling\neffort? Such a comparison might strengthen the analysis. This is also an important area that needs some coverage, and how\ntropical research contributes to this (see suggested reference for tropical Southeast Asia below).\nSecond, in the section ‘Towards broader and more inclusive tropical sciences’, while the recommendations are useful, they\ndo not fully reflect the realities and challenges faced by tropical biologists. The point about poor citation practices limiting the\nvisibility and influence of tropical scholarship is well taken, but the discussion that follows is underdeveloped and supported\nby overly generic recommendations. This is a crucial aspect of the study that deserves more detailed analysis, ideally\nsupported by the data presented. I suggest incorporating socioecological and economic factors that shape these patterns\nand providing concrete suggestions for how such barriers might be addressed in future research.\nAdditionally, the issue of “helicopter science” is only briefly mentioned but warrants deeper treatment given its centrality to\ninequities in tropical research. Relatedly, the institutional representation in the study itself appears skewed; only 5 out of 34\ninstitutions involved are based in the tropics or subtropics. This raises concerns about whether the perspectives of tropical\nscientists are adequately incorporated into both the analysis and interpretation.\nOverall, while there are already several works on the inequalities of sampling and effort in the tropics (which were poorly\ncited here), the manuscript presents a valuable and much-needed global analysis. However, its contribution would be\nsignificantly strengthened by a more nuanced and critical discussion of the structural and systemic challenges facing tropical\nscience, backed by both the authors’ analysis and relevant literature. Please also consider the following references, which\nmay help situate the discussion more effectively:\nHughes, A. C., Than, K. Z., Tanalgo, K. C., Agung, A. P., Alexander, T., Kane, Y., Bhadra, S., Chornelia, A., Sritongchuay, T.,\nSimla, P., Chen, Y., Chen, X., Uddin, N., Khatri, P., & Karlsson, C. (2023). Who is publishing in ecology and evolution? The\nunderrepresentation of women and the Global South. Frontiers in Environmental Science, 11, 1211211.\nhttps://doi.org/10.3389/fenvs.2023.1211211\nAmano T, Ramírez-Castañeda V, Berdejo-Espinola V, Borokini I, Chowdhury S, Golivets M, et al. (2023) The manifold costs\nof being a non-native English speaker in science. PLoS Biol 21(7): e3002184. https://doi.org/10.1371/journal.pbio.3002184\nJarić, I., Diagne, C. and Chowdhury, S. (2025), Moving beyond continents for global and inclusive science. Front Ecol\nEnviron, 23: e2851. https://doi.org/10.1002/fee.2851\nTanalgo, K. C. (2025). Open and FAIR data sharing are building blocks to bolster biodiversity conservation in Southeast\nAsia. Biological Conservation, 307, 111192. https://doi.org/10.1016/j.biocon.2025.111192"}, "author_response": "Responses to comments\nWe thank the two reviewers for their thorough and thoughtful comments. We have addressed\nthese comments (see detailed comments below) and extensively revised the manuscript\naccordingly. We believe that these changes have resulted in a much stronger and more detailed\nexposition of the study data. We attach both a final revised version of the manuscript (with\nmajor additions highlighted in red), and a track-changed version so that the full detailed changes\nfrom the original submission are visible. In the text below, responses are in normal text, and\ntext cited from the revised manuscript are presented within quote marks in italics.\nReviewer 1\nComment) I enjoyed reading this manuscript and think could make a nice contribution to the\nliterature. The analysis is satisfyingly simple and, to me, clearly demonstrates that sampling is\nnot representative of the wider environment in the tropics. As I see it, the weakest part of the\nmanuscript is that it is not currently grounded in sampling theory. I have suggested some\nadditional literature and left some technical comments that will hopefully improve the paper in\nthis respect.\nResponse) Thank you for your positive evaluation and constructive inputs. We have addressed\neach of your points in detail, see responses below and line numbers denoting changes in the\nrevised manuscript. We believe that these changes have greatly strengthened the manuscript,\nadding important detail and context.\nComment) Perhaps most importantly, the term “sampling bias” is used improperly. Sampling\nbias (i.e. nonignorable or ‘preferential’ sampling) is defined with respect to a specific study\nvariable (Aubry et al., 2024; Boyd et al., 2025; Schouten et al., 2012). Here there is no one\nstudy variable, since many studies are considered, so it’s better to stick to the word\n“representativeness”.\nResponse) We agree that our use of the term sampling bias risks confusing readers, so we have\nfollowed your advice and substituted this term with variants of “representativeness”. Depending\non the context we have in certain cases chosen “unrepresentative” or “poorly represented”.\nComment) It’s probably also worth mentioning the potential biases induced by considering\nonly those studies published in English (Serrano et al., 2025).\nResponse) Some non-english studies were included but they were a small minority of the\narticles retrieved from the bibliographic search. We already briefly mentioned the likely biases\nintroduced by only including English studies (Lines 277-279 in original submission). However,\nwe have now incorporated your suggested reference to add further detail and context (Line 304-\n307).\n“This issue may be exacerbated by the under-representation of non-English language\nliterature79,80, which we recommend integrating more fully into future reviews and syntheses.\nFast evolving translation tools make this a realistic vision81. “\nIn addition, we have now added the following relevant information when describing the\nliterature search (Lines 341-342):\n“Non-english language articles were not screened out but represent a minority of the Web of\nScience database84”\nComment) 71: what does robust mean in this context?\nResponse) We agree that the use of “robust” in this context was vague and open to\ninterpretation. We have now replaced this word with “accurate” (Line 71) since the term\n“accuracy” has a more specific and clear meaning in the context of scientific data:\nhttps://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Accuracy\nComment) 72: could potentially remove the sentence starting “However,…”; since the\nfollowing one highlights the problem anyway.\nResponse) We agree that this sentence was redundant, so we have removed it.\nComment) 77: what is an “unrepresentative scientific paradigm”?\nResponse) We agree that this phrasing is not clear. We have now replaced it with the following\ntext (Line 75):\n“flawed scientific paradigms”\nComment) 98: what do you mean by biases in peer review?\nResponse) We were referring here to evidence that the peer review process in academic journals\nmay be biased against certain groups. We now include additional descriptions of this\nphenomenon with references in the following text (Lines 224-227, 286-295):\n“The spatial biases we reveal likely emerge from a complex mix of factors; locations of research\nstations46, article peer review outcomes and citation rates2, evolution of population centers and\ntransport infrastructure17, as well as imbalances among regions in resources available for\nresearch47.”\n“The underlying causes of these disparities likely overlap substantially with those driving\nglobal patterns: including unequal access to research resources and infrastructure among\ntropical countries and regions47,73, variation in social and political stability74, administrative\nbarriers to knowledge transfer across regions and countries75, the preferential channeling of\ninternational funding and collaborations through a small subset of tropical institutions and\ncountries46, a bias in research toward forested landscapes relative to other tropical habitat\ntypes70, unethical collaborative practices which disproportionately benefit partners from\nwealthier countries often outside the tropics76 and systemic biases in the recognition of\nscientific knowledge production77,78”\nComment) 101: so you’re looking at the locations of the fieldwork and also the number of\ncitations given the location of the fieldwork?\nResponse) Yes, that is correct. We believe that citations are useful to include, in addition to\nsampling locations for the following reasons: (1) because citations are a useful proxy for the\nscientific influence exerted by specific studies, and the geographic locations at which studies\ntook place, and (2) because patterns of citations may be subject to a range of different biases\nfrom those that affect sampling locations. We had described both the rationale for including\ncitation data and how they were calculated for individual locations in the methods section, but\nwe have now moved this text to the introduction of the study to help with understanding the\ncitation data (Lines 127-131):\n“Citation data were included as a flawed but valuable proxy for the scientific influence exerted\nby specific studies27. In cases where multiple coordinates for sampling locations were identified\nin a paper, citations per sampling location were calculated as total paper citations divided by\nthe number of locations identified per paper.”\nComment) 103: sentence reads a bit strangely. Would it be better to say you “map 6370 measurements from 2738 published articles\nthat have been cited 89 468 times…”?\nResponse) We agree that your formulation is easier to read and understand. The sentence has\nnow been modified to the following (Lines 118-119):\n“Here, we map 6 370 field measurements from 2 738 published articles representing 89 468\ncitations”\nComment) 108: this is confusing since you go on to show that these papers are not an unbiased\nsubset.\nResponse) We agree that this sounds confusing, so have modified this text to provide more\ndetail and context (Lines 121-125), the references provide descriptions of similar applications\nof this search methodology:\n“We compiled an initial list of studies with a minimum of 1 citation from a keyword searches for\n“trop*” in the article title on the Web of Science database26. The search was designed to\nminimize introduction of spatial biases arising from the search process itself, such that any\nbiases in the identified body of literature likely reflected genuine trends in research effort and\nattention15,16”\nComment) 111: the justification for dropping urban studies isn’t very strong imo. But if you\nwill delineate your study in this way, then it should be clear in the title and abstract.\nResponse) We already specified in the abstract that the study was focused on natural terrestrial\nenvironments (Lines 78-79 in original submission), but we have now clarified this also in the\ntitle (Lines 1-2), and in all figure/table legends.\nOur dataset initially included all environments, including those heavily influenced by\nanthropogenic activity (classified as either “urban” or “agricultural” habitats). We found that\nthese anthropogenic habitats constituted a large portion of all samples/citations. In one sense,\nthis is an interesting result but it also means that all patterns in Figures 1-3 are dominated by\nthe trends of sampling/citation in anthropogenic habitats, obscuring other important and\ninteresting trends in more natural habitats. We believe that it would be very difficult to dedicate\nthe necessary attention to the separate drivers and trends presented by the different portions of\nthe entire dataset in the context of a single highly space-limited paper. The approach we have\ntaken is to (1) reserve the data from anthropogenic habitats for the full detailed analysis in a\nseparate article which we believe it merits, and (2) present only the data from habitats with\nminimal anthropogenic influence in the present analysis.\nIn the revised manuscript, we now (1) present a new figure in the supplementary material,\noutlining the key patterns shown by the full dataset (including urban and agricultural studies)\n(Extended Data Fig. S1), (2) add text in the main manuscript (Lines 109-113) presenting an\nbrief overview of the basic patterns shown by the anthropogenically influenced habitats, which\nwe believe helps to explain our decision to exclude these data from the main analyses presented\nin the manuscript:\n“We identified 4 260 cited articles featuring primary field data within the tropics. Habitat types\ncharacterized by a high degree of anthropogenic influence (urban and agricultural) accounted\nfor 36% and 32% of sampling locations and citations respectively. Spatial patterns of sampling\nand citation across the tropics are dominated by this prevalence of research on heavily\nimpacted environments (Extended Data Fig. S1)”\nComment) 116: It should be clearer at this point that “patterns of citations” refers to the\ndistribution of citations indexed by original study location (if that’s what you are doing ofc).\nResponse) We agree the term 'pattern' was vague. In our revisions, we now use the more\nspecific terms 'distribution' or 'spatial distribution' when referring to our data (e.g., sampling\nand citation) and retain 'pattern' only for more general discussions in the literature.\nComment) 127: these maps of plant and vertebrate species richness are presumably very\ngeographically biased too. Some consideration of what this means for your results is needed.\nResponse) We agree. The source for vascular plant species richness (Sabatini et al. 2002)\ndescribed some attempt at bias correction in the methods section, but we doubt that will have\ncompletely removed the likely very strong biases in the underlying biodiversity data. The source\nfor vertebrate species richness (Jenkins et al. 2013) gives no clear description of any correction\nfor unrepresentative sampling. We speculate that the distribution of species data and our\nmeasurements of sampling and citation should be highly correlated since they will both likely\nbe confounded by very similar factors affecting research effort. This means that (1) we should\nbe generally cautious about the species maps and our observed relationships between\nsampling/citation and diversity, (2) we may overestimate the extent to which current sampling\nand citation “match” or represent current spatial patterns of diversity, so our current assessment\nof representativeness of sampling for biodiversity will, if anything, tend to be optimistic. We\nnow include new text outlining these key points in the manuscript, with appropriate references\n(Lines 188-192):\n“It is important to note, however, that the datasets used to quantify actual biodiversity\ndistributions13,14 are themselves likely spatially biased32,33, for many of the same reasons that\ndrive sampling and citation biases1-9,23. Therefore, the extent to which actual biodiversity\ndistributions are accurately represented by existing research should be interpreted with\ncaution, though the present analysis likely overestimates representation (Fig. 3).”\nComment) 149: it might be nice to plot sample vs. population relative frequencies per\nenvironmental category in this section. I.e. relative frequencies of sampled locations per\ncategory, possibly of citations per category and the actual frequency distribution of\ncategories across the tropics.\nResponse) We believe that the relative frequency per biome of all of these variables – the total\npopulation as well as sampled locations and citations – are presented as percent values in Table\n1 (and for individual ecoregions in the Supplementary information). However, we may be\nmisunderstanding your comment. We are happy to make additional changes but may require\nadditional guidance on this point.\nComment) 178: what’s a representativeness analysis? If you mean the SDM-type analysis\ndescribed in the methods, then this is a map of predicted probabilities of sampling rather than\nrepresentativeness. Some would consider the variability in predicted probabilities across pixels\nthe measure of representativeness (Bethlehem et al., 2008). (The idea here is that under simple\nrandom sampling, the usual benchmark for ‘representative’ sampling, every pixel would have\nan equal probability of being included in the sample. A greater departure from equal inclusion\nprobabilities indicates a greater departure from simple random sampling.)\nResponse) We agree that this term was vague and liable to create confusion. The approach we\nhave chosen, which we believe is more in line with the rest of the results text, is to remove this\ninitial part of the sentence and only focus on the results. The analyses underlying these results\nare instead mentioned in the Figure 2 caption (Lines 739-742), and are presented in detail in the\nMethods section (Lines 419-456).\nComment) 196: *“vertebrate diversity”\nResponse) We agree that “animal” is overly broad. However, this refers to data which covers\nmammals and birds, not all vertebrates. So we have revised the text to the following (Lines 185-\n188):\n“Conversely, areas with high LAI and high diversity of mammals and birds, corresponding\nroughly with the moist broadleaf forest biome (Extended Data Fig. S3), were sampled and cited\nmore often than expected from the frequency of their occurrence (Fig. 3)”\nComment) 215: what do you mean by “translate into valuable ecoregions”?\nResponse) We have modified this sentence to hopefully make it simpler and clearer (Lines 196-\n198):\n“We document major spatial biases in research foci across the terrestrial tropics, which means\nthat valuable ecoregions and widespread environmental conditions remain largely overlooked”\nComment) Sentence starting on line 218 doesn’t make sense grammatically.\nResponse) We have modified this sentence to make it gramatically correct, and clearer that we\nwere referring to the drier biomes mentioned in the preceding sentence (Lines 200-202):\n“Yet, these drier biomes are home to around one-third of the global human population36, their\nhabitats are generally more threatened37 and they receive less formal protection than other\nbiomes38”\nComment) 221: how? Stratify on these variables when collecting new data (Pescott et al.,\n2025)? Also see (Henrys et al., 2024; Schouten & Shlomo, 2017).\n226: how? Need some substance here about the challenges of extrapolation under\nunrepresentative sampling.\nResponse) We now address the issues raised by these two comments with substantial new text\nand references in the next section (Lines 220-224, 256-278) which is more explicitly targeted\nat practical strategies to provide more representative insights. So we have chosen to remove the\nclaim that our study will help to focus resources in the section “Policy risks from\nunrepresentative sampling and citation in tropical research” (Lines 194-217). The content of\nthis section is now better aligned with the described section subheading, focusing only on the\nobserved patterns of sampling and citation and their likely policy consequences.\nComment) 238: this section lacks any reference to statistical corrections, which seem like the\nobvious approach to mitigating the effects of sampling biases. See e.g. (Boyd et al., 2023; Elliott\n& Valliant, 2017).\nResponse) Thank you for pointing us towards this literature in this and the two previous\ncomments, they are indeed highly relevant to the issues we discuss. We now include a large\namount of new text describing various practical approaches, with relevant references, to\nmitigating the effects of unrepresentative sampling (Lines 220-224, 256-278). Broadly, these\ninclude (1) greater use of risk-of-bias assessments to acknowledge and counteract biases where\npossible (Lines 259-263), (2) greater use of statistical sample adjustments and adaptive\nsampling strategies (Lines 263-268), (3) more explicit description of biases and limitations in\nthe data collected in scientific studies and what it means for scientific inferences to wider\nregions/populations/time periods (Lines 272-276).\nComment) 227: this is a really nice example.\nResponse) Thank you.\nComment) 240: geographic biases are driven by spatial biases?\nResponse) We agree that this is an obvious connection, we have revised the text to the following\n(Lines 224-227):\n“The spatial biases we reveal likely emerge from a complex mix of factors; locations of\nresearch stations46, article peer review outcomes and citation rates2, evolution of population\ncenters and transport infrastructure17, as well as imbalances among regions in resources\navailable for research47.”\nComment) 244: it’s good that you have acknowledged this.\nResponse) Thanks, yes we think this is an important possibility to acknowledge, although we\nbelieve for the reasons outlined in the text it is unlikely to affect our key results (Lines 228-\n236).\nComment) 252: this paragraph is closely related to concepts in meta-analysis. It might be worth\nreferring to internal vs. external validity and risk of bias assessments, which are common in\nevidence synthesis and increasingly in ecology.\nResponse) We now explicitly include the terms internal and external validity, with relevant\nreferences, where this topic is introduced (Lines 237-241).\n“We emphasize that the spatial biases identified are an emergent property resulting from\nsynthesizing many individual research studies then drawing broad conclusions from them\n(“external validity” in reviews and meta-analyses50), even though individual studies may not\nmake inferences beyond their immediate study site. As such, our results make no claim about\nthe accuracy and quality of individual articles (“internal validity”50).”\nIn the new text describing strategies to account for unrepresentative sampling in scientific\nstudies, we now describe, with references, the basic principle and purpose of risk-of-bias\nassessments (Lines 259-263).\n“Rigorous assessments of the contributions of bias on descriptive inference - so called “risk-\nof-bias” assessments – are standard in medical research proposals and papers58. Expanding\nthe use of risk-of-bias assessments into other fields could improve scientific transparency and\nrigor, helping both authors and readers better understand the limitations and generalizability\nof research findings.”\nComment) 273: again, some discussion of how adaptive sampling might work is needed.\nResponse) We agree. In the new text describing strategies to account for unrepresentative\nsampling in scientific studies, we now describe, with references, the basic principle and purpose\nof adaptive sampling, although we have chosen to not explicitly use the term “adaptive\nsampling” since it is a rather specialist term which may create confusion and distract from the\ncore point (Lines 263-268).\n“Risk of bias can be reduced with auxiliary variables which are associated with both the\nlikelihood of a unit being sampled and with the underlying values of the variable of interest, to\nadjust the overall population-level estimate so that it lies closer to the true value44,59,60.\nMoreover, such variables can also be used to guide future sampling efforts – to target locations\nwhich have been sampled less frequently than would be expected by chance61-63. The spatial\ndistribution of sampling documented in this study could serve as an auxiliary variable to correct\ncurrent estimates and guide future, more balanced sampling, improving biome or pan-tropical\nestimates of environmental variables.”\nReviewer 2\nComment) This is an interesting and timely contribution that provides valuable perspectives\non the biases of sampling and biodiversity research across the globe, particularly the\nundersampling of the tropics where biodiversity is highest and threats are most severe. The\npaper applies robust methods and approaches to examine these biases. However, there are\nseveral key areas that I think require greater emphasis and refinement.\nResponse) Thank you for your positive evaluation and constructive inputs. We have addressed\neach of your points in detail, see responses below, resulting in extensive changes in the revised\nmanuscript. We believe that these changes have greatly strengthened the manuscript, adding\nimportant detail and context.\nComment) First, the reliance on published literature as the basis for “observations” raises\nquestions. Did the authors cross-reference their findings with GBIF occurrence records or other\nbiodiversity databases that could serve as useful proxies for sampling effort? Such a comparison\nmight strengthen the analysis. This is also an important area that needs some coverage, and how\ntropical research contributes to this (see suggested reference for tropical Southeast Asia below).\nResponse) After some careful reflection we have decided not include any formal comparison\nor analysis using GBIF data in the revised manuscript, for the following reason. There is\nextensive evidence that GBIF data is spatially biased for many of the same reasons described\nin our paper (Hughes et al. 2021, Beck et al. 2014, Garcia-Rosello et al. 2023, Bowler et al.\n2025). Therefore, we believe that the spatial distribution of sampling and citation would be\nhighly correlated with the GBIF distribution, mainly because they are driven by the same\nunderlying factors. We are not convinced that highlighting this correlation would contribute\nmuch useful insight to the analysis. However, we do now explicitly acknowledge the possibility\nthat the biodiversity datasets used in the article may themselves be spatially biased, and include\nsome discussion of how this may affect our interpretations (Lines 188-192):\n“It is important to note, however, that the datasets used to quantify actual biodiversity\ndistributions13,14 are themselves likely spatially biased32,33, for many of the same reasons that\ndrive sampling and citation biases1-9,23. Therefore, the extent to which actual biodiversity\ndistributions are accurately represented by existing research should be interpreted with\ncaution, though the present analysis likely overestimates representation (Fig. 3).”\nThank you for the reference from tropical Southeast Asia, we have now included it within the\ntext as an excellent example of the strong spatial variation in research effort within different\ntropical countries and regions (Lines 284-286).\n“our study highlights the extent and importance of regional disparities within the tropics71,72 –\nparticularly between South America and Asia versus Africa, and tropical lowland forests versus\nother habitats”\n Hughes, A.C., Orr, M.C., Ma, K., Costello, M.J., Waller, J., Provoost, P., Yang, Q., Zhu, C. and\nQiao, H. Sampling biases shape our view of the natural world. Ecography 44, 1259-1269 (2021)\n J. Beck, M. Böller, A. Erhardt, W. Schwanghart. Spatial bias in the GBIF database and its effect\non modeling species' geographic distributions. Ecological Informatics 19, 10-15 (2014).\n E. Garcia-Rosello, J. Gonzalez-Dacosta, C Guisande, and J. M. Lobo. GBIF falls short of\nproviding a representative picture of the global distribution of insects. Systematic Entomology 48, 489–\n497 (2023).\n D. E Bowler, R. J Boyd, C. T Callaghan, R. A Robinson, N. J. B. Isaac and M. J. O. Pocock.\nTreating gaps and biases in biodiversity data as a missing data problem. Biological Reviews 100, 50-67\n(2025).\nComment) Second, in the section ‘Towards broader and more inclusive tropical sciences’,\nwhile the recommendations are useful, they do not fully reflect the realities and challenges faced\nby tropical biologists. The point about poor citation practices limiting the visibility and\ninfluence of tropical scholarship is well taken, but the discussion that follows is underdeveloped\nand supported by overly generic recommendations. This is a crucial aspect of the study that\ndeserves more detailed analysis, ideally supported by the data presented. I suggest incorporating\nsocioecological and economic factors that shape these patterns and providing concrete\nsuggestions for how such barriers might be addressed in future research.\nResponse) We thank the reviewer for pushing us to confront these issues more directly, we\nbelieve that the manuscript is greatly improved as a result. We agree that the underlying social\nand economic drivers of the observed patterns are critical to study. However, we believe that\nwe cannot do justice to the complexity and importance of this topic with a formal quantitative\nanalysis in the present manuscript, where it would be presented as supplementary to the main\ntopic, and distract from the core focus on environmental properties surveyed in tropical science.\nInstead, we plan to address these drivers in a separate, dedicated analysis and manuscript.\nNevertheless, in the present manuscript we have now added substantial new text to this section\nthat provides a much more detailed description of different key issues, including\nsocioecological and economic factors, driving differences in scientific productivity and\nrecognition among different tropical countries and regions (Lines 283-299, 303-304, 307-312,\n316-321). Together with this text, we have added, where relevant, multiple new references\n(references 67-84) which describe in detail the diverse challenges facing tropical scientists, and\nthe broader dynamics driving global and regional inequity in scientific knowledge production.\nSpecifically, we now provide a much more detailed overview of the drivers of inequities (Lines\n283-295). Further, we now specify some possible reforms including lowering of financial and\nadministrative barriers in academic publishing and research (Lines 296-299), improving\nrepresentation in academia (Lines 307-312), and restructuring global funding and collaborative\nnetworks led by wealthier country partners away from the small number of tropical research\ninstitutions and field stations which currently dominate to a wider range of countries and regions\n(Lines 316-321).\nComment) Additionally, the issue of “helicopter science” is only briefly mentioned but\nwarrants deeper treatment given its centrality to inequities in tropical research. Relatedly, the\ninstitutional representation in the study itself appears skewed; only 5 out of 34\ninstitutions involved are based in the tropics or subtropics. This raises concerns about whether the\nperspectives of tropical scientists are adequately incorporated into both the analysis and\ninterpretation.\nResponse) We agree, we now refer to a wide range of inequities including “unethical\ncollaborative practices which disproportionately benefit partners from wealthier countries often\noutside the tropics”, which includes the phenomenon of helicopter science (Lines 292-294):\n“The underlying causes of these disparities likely overlap substantially with those driving global\npatterns: including unequal access to research resources and infrastructure among tropical\ncountries and regions47,73, variation in social and political stability74, administrative barriers to\nknowledge transfer across regions and countries75, the preferential channeling of international\nfunding and collaborations through a small subset of tropical institutions and countries46, a bias\nin research toward forested landscapes relative to other tropical habitat types70, unethical\ncollaborative practices which disproportionately benefit partners from wealthier countries often\noutside the tropics76 and systemic biases in the recognition of scientific knowledge\nproduction77,78.”\nWe acknowledge that our author composition reflects many of the problems highlighted. These\nproblems are, as we say in the article, relatively easy to identify but in many cases difficult to\nsolve (Lines 295-296). In our case, the northern bias of the author list largely derives from the\nfact that much of the same author team were involved several years ago in a conceptually very\nsimilar paper focused on northern systems (Metcalfe et al. 2018. Nat Ecol Evol 2, 1443–1448).\nWe note also that multiple authors with affiliations at non-tropical institutions originate from\ntropical regions, so have a strong personal connection with tropical science despite their current\naffiliation. Given our author group’s northern bias, we have taken great effort to examine the\nwide range of published perspectives on this issue before adding our own contribution. We\nbelieve that the current more detailed and extensively referenced discussion text is a fair\noverview of the key issues and potential solutions that have already been presented by tropical\nscientists. Furthermore, we now discuss the background of the group and the ethics raised in a\nnew Inclusion & Ethics section (Lines710-721)\n“The authorship team comprises a diverse range of nationalities and career stages with a\nreasonably balanced gender composition. There is, however, a distinct overrepresentation of\nnorth European and American institutions, although, several members of the team are\nnationals of tropical countries but are now employed outside of the tropics. In large part, this\nreflects the fact that much of the group was initially established to complete a conceptually\nsimilar article (Metcalfe et al. 2016. Nature Ecology & Evolution 2: 1443-1448) focused on\nArctic systems. For the present analysis, considerable effort was made to widen the authorship\ngroup enlisting assistance and inputs from researchers working in tropical countries, with\nlimited success. Therefore, in the present article we have taken particular care to evaluate and\nthoroughly describe the diverse perspectives about the patterns and drivers of regional and\nglobal variation in knowledge production.”\nComment) Overall, while there are already several works on the inequalities of sampling and\neffort in the tropics (which were poorly cited here), the manuscript presents a valuable and\nmuch-needed global analysis. However, its contribution would be significantly strengthened by\na more nuanced and critical discussion of the structural and systemic challenges facing tropical\nscience, backed by both the authors’ analysis and relevant literature. Please also consider the\nfollowing references, which may help situate the discussion more effectively:\nResponse) We have now added substantial new text to this section that provides a much more\ndetailed description of different key issues, including socioecological and economic factors,\ndriving differences in scientific productivity and recognition among different tropical countries\nand regions (Lines 283-299, 303-304, 307-312, 316-321). Together with this text, we have\nadded, where relevant, multiple new references including those you provide (references 67-84)\nwhich describe in detail the diverse challenges facing tropical scientists, and the broader\ndynamics driving global and regional inequity in scientific knowledge production.\nSpecifically, we now provide a much more detailed overview of the drivers of inequities (Lines\n283-295). Further, we now specify some possible reforms including lowering of financial and\nadministrative barriers in academic publishing and research (Lines 296-299), improving\nrepresentation in academia (Lines 307-312), and restructuring global funding and collaborative\nnetworks led by wealthier country partners away from the small number of tropical research\ninstitutions and field stations which currently dominate to a wider range of countries and regions\n(Lines 316-321)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "Thank you to the authors for addressing my comments. In my view, the paper is now publishable and will make a nice\ncontribution to the literature.", "reviewer_2": "I thank the authors for their effort in revising the manuscript and addressing the key comments raised in the previous round.\nThe paper has now substantially improved, presenting a more balanced integration of information from various geographical\ncontexts. The limitations of the analysis are also now presented alongside the methods and overall approach, making the\nstudy easier to follow and reproduce.\nHowever, the manuscript would still benefit from further refinement to improve the coherence and flow of the narrative. A\ncareful polishing of the text and smoother transitions between ideas will enhance readability and overall presentation,\nmaking it suitable for publication"}, "author_response": ""}]

Decision letter