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Cuba has hosted relatively small numbers of tourist groups given its size, which might have helped to keep invasive plants at bay. Credit: Roberto Machado Noa/LightRocket/Getty

Ecology
18 February 2021

Cuba’s relatively closed economy could explain why it has fewer invasive plant species per unit area than other Caribbean islands.

For more than 60 years, the rocky relationship between the United States and Cuba has helped to steer tourists and businesses away from the Caribbean island. Now, researchers have found that Cuba’s economic and political isolation might also have limited the spread of invasive plants.
Meghan Brown at Hobart and William Smith Colleges in Geneva, New York, and her colleagues estimated the number of invasive plant species on 45 Caribbean islands. The researchers found that larger islands tend to have more exotic plant species than do smaller ones. But Cuba, the biggest island in the Caribbean, is home to hundreds fewer such species than expected for its size.
Mass tourism seems to favour the introduction of invasive plants, the team found, probably because hotels plant exotic ornamental species and tourists carry seeds in their bags or on their shoes. Cuba — which has one of the region’s lowest shares of holidaymakers in comparison to its area — has about the same number of invasive species as Puerto Rico, which is one-tenth the size of Cuba but has many more visitors for its land area. More

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Distribution of tick species in mainland China
We compiled a database comprising 7344 unique records on geographic distributions of 124 known tick species, including 113 hard tick species in seven genera and 11 soft tick species in two genera, together with 103 tick-associated agents detected in either ticks or humans, which were recorded in 1134 counties (39% of all counties in the mainland of China) (Supplementary Fig. 1 and Supplementary Note 1). The most widely distributed tick genus was Dermacentor (in 574 counties), followed by Heamaphysalis (570), Ixodes (432), Rhipicephalus (431), Hyalomma (298), Argas (90), Ornithodoros (38), Amblyomma (37), and Anomalohimalaya (5) (Supplementary Data 1 and Supplementary Figs. 2‒10). At the species level, D. nuttalli, Ha. longicornis, D. silvarum, Hy. scupense, and R. sanguineus were each found in >200 counties, followed by R. microplus, I. persulcatus, I. sinensis, I. granulatus, and Hy. asiaticum that were each detected in 100‒200 counties (Supplementary Data 1). We identified 19 predominant ticks that were detected in more than 40 counties, including five Ixodes species, four Heamaphysalis, four Dermacentor, three Rhipicephalus, two Hyalomma, and one Argas tick species. Forest and meadowlands are the major vegetation types for these 19 tick species, accounting for a median of 46.4% (IQR: 40.0%‒68.9%) of their habitats (Supplementary Data 1).
The abundance of tick species varies substantially across the seven biogeographic zones which are defined by climatic and ecological characteristics (Fig. 1)18,19. Tick species are most abundant in Central China, South China, and Inner Mongolia–Xinjiang districts, hosting 61, 57, and 50 tick species, respectively (Supplementary Data 2). Eight prefectures reported ≥20 tick species, three in Xinjiang Autonomous Region of northwestern China, two in Yunnan Province of southwestern China, and one in each of Gansu, Hubei, and Fujian provinces of northwestern, central, and southeastern China, respectively (Fig. 1). Most genera except for Amblyomma were found in northwestern China, particularly in Xinjiang Autonomous Region. In contrast, less tick diversity was observed in northeastern China, which only harbors Ixodes, Heamaphysalis, and Dermacentor (Supplementary Figs. 2‒10).
Fig. 1: Tick species richness (circles) at the prefecture level in seven biogeographic zones in mainland China from 1950 to 2018.

I = Northeast district (NE), II = North China district (N), III = Inner Mongolia–Xinjiang district (IMX), IV = Qinghai–Tibet district (QT), V = Southwest district (SW), VI = Central China district (C), and VII = South China district (S). Source data are provided as a Source Data file.

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Risk mapping and risk factors for 19 predominant tick species
The ecological modeling results for the 19 predominant tick species showed highly accurate predictions, with the average testing area-under-curve (AUC) ranging from 0.83 to 0.97 (Table 1) and the testing partial AUC ratio ranging from 1.30 to 1.78 (Supplementary Tables 1‒5), indicating decent predictive power. The ecoclimatic and environmental variables that were predictive of the geographic distribution of the ticks differed among the species, even for those in the same genus (Fig. 2f, Supplementary Tables 1‒5). Temperature seasonality and mean temperature in the driest quarter were the two most important drivers, contributing ≥5% to the ensemble of models for 14- and 12- tick species, respectively, followed by elevation contributing ≥5% to the models for seven tick species (Fig. 2f, Supplementary Tables 1‒5). The same predictor, however, may drive the risk in different directions for different tick species (Supplementary Figs. 11‒29). For example, a high temperature in the driest quarter was associated with a high probability of presence for I. granulatus and R. haemaphysaloide but with a low probability for I. persulcatus and Ha. longicornis (Supplementary Figs. 11, 13, 16, 22).
Table 1 The average testing areas under the curve (AUC) of the BRT models at the county level and model-predicted numbers, areas and population sizes of affected counties for the 19 most prevalent tick species in China.
Full size table

Fig. 2: Clustering of tick species based on their ecological features and spatial distributions at the county level.

Panels a‒e indicate the spatial distribution of the five clusters (clusters I‒V). The boundaries of the seven biogeographic zones are shown as black solid lines. The dendrogram in panel f displays the clusters I‒V of tick species. The features used for clustering are three quantities associated with each predictor in the BRT models. Two of the three quantities were displayed in panel f to indicate the possible level of ecological suitability: relative contributions (colors in ascending order from yellow to red) and the standardized median value of the predictor (numbers in the heatmap) among counties with tick occurrence (numbers 1‒4 indicate the position of this median in reference to the quartiles of this predictor among all counties). Source data are provided as a Source Data file.

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The model-predicted high-risk areas of the 19 tick species were much more extensive than have been observed, 31‒520% greater in the number of affected counties, 14‒476% larger in the size of affected geographic area, and 25‒556% larger in the affected population size (Table 1, Supplementary Figs. 30‒34). Ha. longicornis was predicted to have the widest distribution that potentially affected 588 million people in 1140 counties, followed by I. sinensis and R. microplus that affected 363 and 350 million people in 630 and 678 counties, respectively (Table 1). High-risk areas of these three tick species collectively covered nearly all densely populated areas in China, mainly provinces in the central, eastern, southern, and southwestern China (Supplementary Figs. 30(b), 31(a), and 32(b)). R. sanguineus, and R. haemaphysaloides each affected more than 200 million people. D. nuttalli, I. crenulatus, Hy. asiaticum, Ar. persicus, and D. daghestanicus ticks were the top five tick species affecting the largest areas at the scale of 2.0‒3.8 million km2 (Table 1).
Ecological clustering of tick species
Based on the ecological similarity represented by the environmental and ecoclimatic predictors, the 19 tick species were grouped into five clusters with clear patterns of spatial aggregation (Fig. 2). D. nuttalli and D. silvarum constituted cluster I that covered the vast region in northern (including northeastern and northwestern) China. This cluster stretches over biogeographic zones I‒IV characterized by middle to high elevations, shrub grassland, strong seasonality in temperature, relatively low temperature in the wettest quarter (often also the warmest quarter), and low precipitation in the driest month (Fig. 2 and Supplementary Figs. 23, 24). Ha. longicornis, Hy. scupense, and R. sanguineus were grouped into Cluster II which was mainly found in biogeographic zones II, III, and VI, featuring the landscape of shrub grassland and irrigated or rainfed croplands at low-middle elevations (7% (Table 2). SFTSV ecologically prefers regions at low to moderate elevations ( 7%). High risks of TBEV were flagged by low to medium elevations ( More

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Study area
We established field survey sites in four different habitat types (BW: breakwater wall; EB: eelgrass bed; IF: intertidal flat; SB: subtidal bottom) within Matsunaga Bay, located in the eastern Seto Inland Sea, Japan (Fig. 1). Matsunaga Bay is a small semi-closed bay with an area of approximately 12 km2. It is connected with other water bodies through the Tozaki-Seto Strait (width: approx. 400 m) and the Onomichi Strait (width: approx. 200 m)29,30. Water depths are mostly less than 20 m throughout the bay. The water depths at our four survey sites were approximately 4.5 m, but part of site SB located near a shipping channel reached depths of 10–13 m. Intertidal flats cover 35% (4.3 km2) of the bay area, whereas eelgrass beds cover 1.7% (0.2 km2)31. The bottom sediment type is mainly muddy throughout the bay, although some parts of EB and SB have sandy and muddy bottoms (see Supplementary Table S1)30.
Figure 1

Locations of sampling sites in Matsunaga Bay, Hiroshima, Japan. This map was created based on coordinate data from Google (http://www.gis-tool.com/mapview/maptocoordinates.html). The four habitats examined in this study are indicated by BW (breakwater wall; grey circle), EB (eelgrass bed; grey circle), IF (intertidal flat; grey rectangle), and SB (subtidal bottom; grey polygon).

Full size image

Although human activities along the coast of Matsunaga Bay appear to be associated with artificial structures (e.g., industrial plants, marinas, and lumber yards), natural habitats are still relatively well-preserved compared to areas in the eastern Seto Inland Sea29. The total population of towns within 5 km of the coastline of the bay can be expected to exceed 100,000 people, which ranks within the top 20% of administrative districts in Japan, including prefectures, towns, villages, and the 23 wards of Tokyo32.
Data collection
We conducted one field survey in September 2016 (summer) and another in January 2017 (winter) to collect data on benthic community structures and environmental conditions in each habitat. We established five sampling sites within each habitat to obtain replicated samples. To reduce biases due to tidal cycle, we performed all field sampling and measurements at high tide, when all habitats were underwater.
We used a standard sample area (approximately 0.15 m2 per sample) at each sampling site irrespective of sampling method to obtain comparable data on benthic communities. Some of our sampling methods involved Smith–McIntyre grab samplers and quadrats that could not cover the standard sample area in a single sample; for these methods, we combined the data from three samples to make up a single sampling site. At BW, benthic macrofauna (hereafter referred to as “benthos”) samples were collected by SCUBA divers. We established 15 sampling positions in a 5 × 3 grid (i.e., five depths [sampling sites] and three replicates) based on distance from the seafloor at the breakwater wall (Supplementary Fig. S1). The SCUBA divers used scraper blades, 0.1-mm mesh bags, and 22.5 cm × 22.5 cm quadrats because the benthic communities were mainly composed of sessile organisms. At EB, we employed different sampling methods for the above- and belowground components. SCUBA divers collected aboveground samples of eelgrass-associated benthos and eelgrass shoots using a mesh bag (mesh size: 0.1 mm; bag diameter: 45 cm). They then cut away the eelgrass shoots near each aboveground site and collected belowground samples of the benthos on top of and within the sediment by using the bucket part of a Smith–McIntyre grab sampler (sampling area: 22.5 cm × 22.5 cm). At IF and SB, benthos and bottom sediment samples were collected from a ship using a Smith–McIntyre grab sampler (sampling area: 22.5 cm × 22.5 cm).
All benthos was extracted using a 1-mm sieve and preserved in buffered 10% formalin in the field as soon as possible after sampling. The samples were identified to the lowest possible taxonomic unit and counted in the laboratory. After identification, we organized the dominant benthic species (or taxa) according to their primary feeding types and common life forms with reference to the World Register of Marine Species (http://www.marinespecies.org/) and the literature. No vertebrate species were targeted in this study. We defined the primary life forms of adult benthic species on/in their substrates as “common life forms.”
Although differences in environmental conditions were not the focus of our study, we did assess whether there were considerable water quality differences among sites. The purpose of this assessment was to try to identify sites with similar conditions so that exogenous impacts on biological communities could be discounted as much as possible in the analysis. We measured water and sediment conditions at each sampling site (except at BW, where sediment conditions were not measured due to the absence of sediment). Prior to benthos and sediment sampling, we measured water temperature, salinity, pH, and dissolved oxygen concentration at each site at a depth directly above the seafloor by using a multi-parameter water quality meter (AAQ‐RINKO, JFE Advantech Co. Ltd., Japan). At BW, where the substrate (i.e., the breakwater) is oriented vertically (see Supplementary Fig. S1), we measured water conditions in the middle of the water column. We also measured temperature, pH, oxidation–reduction potential (ORP), water content, and median particle size (D50) in the sediment. Sediment temperature, pH, and ORP were measured by using a portable ion meter (IM-32P, DKK-TOA Co., Japan) immediately after each sample was collected. Sediment water content and D50 were measured in the laboratory once benthic species had been removed from the sample.
Data analysis
First, we identified how many species were shared between all habitat pairs to understand inter-habitat species-sharing relationships. Second, we compared species compositions and abundances among habitats using similarity indices and multivariate analysis (described below).
To detect species sharing in terms of species commonality and endemism among the four habitats, we classified benthic species into the following three categories: common, endemic, and shifting. Common species were defined as species that occurred across all habitats. Endemic species are those that were found in only one habitat. Shifting species were defined as those that occurred in multiple habitats (but not across all habitats) and therefore showed a broad allowable range of habitat types or conditions. To analyse the importance of habitat sets in maintaining local species diversity, we further categorized the shifting species into two- or three-habitat users (i.e., those that occurred in two or three different habitats). Moreover, we calculated the numbers and proportions (i.e., using the Jaccard similarity index) of shared species in each habitat pair to evaluate the potential strength of any inter-habitat relationships. The Jaccard similarity index ((J)) is calculated as follows:

$$ J = frac{{S_{alpha beta } }}{{S_{alpha } + S_{beta } – S_{alpha beta } }}, $$

where (S_{alpha }) is the number of species in habitat (alpha), (S_{beta }) is the number of species in habitat (beta), and (S_{alpha beta }) is the number of species that are shared among habitats (alpha) and (beta).
In terms of the functional groups, we analysed abundance matrices of abundant species grouped by primary feeding types and common life forms. Focusing on abundant species is a useful way to reflect the functional characteristics of biological communities14. Therefore, we identified the most abundant species from each sample before constructing the abundance data matrices. To determine how many species to select for analysis, we calculated the number e of equally-abundant species that would be required to obtain the Simpson diversity index of each community (i.e., the effective number of species33). We then selected e abundant species from each sample in rank order from most to least abundant. If multiple species of the same rank occupied this cut-off threshold, we selected all of them. This selection method, which is unique to our study, successfully identified dominant species that accounted for over 70% of the total abundance in each community in all habitats.
To compare benthic macrofaunal communities among habitats, we performed non‐metric multidimensional scaling (NMDS)34 based on dissimilarity matrices obtained by using the metaMDS function in the vegan package35. To compare species compositions, we constructed Jaccard dissimilarity matrices based on presence/absence data, and to compare species abundance and functional compositions, we constructed Bray–Curtis dissimilarity matrices based on abundance datasets. All abundance datasets were square-root transformed before calculating the Bray–Curtis dissimilarity matrices to reduce the influence of abundance bias. We accepted the NMDS ordinations if stress values were less than 0.2 to maintain the accuracy of the two‐dimensional ordinations34. Then, we tested the effects of habitat type and sampling time by conducting a two-way permutational multivariate analysis of variance (two-way PERMANOVA)36 using the adonis function in the vegan package. Here, we considered four habitats (Habitat), two sampling times (Time), and their interaction term as explanatory factors. Although our main focus was differences in community compositions among habitats at each sampling time, we also examined the magnitude of variation in each habitat by comparing two stable seasonal extremes (i.e., summer and winter). If the results of the PERMANOVA were significant, we performed post-hoc tests (pairwise PERMANOVA) to identify which pairs of community structures were significantly different by using the pairwise.perm.manova function in the RVAideMemoire package37. We used 9999 permutations for the NMDS ordination, PERMANOVA, and pairwise PERMANOVA. P-values calculated during the pairwise PERMANOVA were corrected using the false discovery rate method38. For the benthic community data collected at each sampling point at EB, above- and belowground datasets were integrated to reflect spatial representativeness (see Figs. S3 and S4). All analyses were performed using R version 3.5.139. More