Philippa Kaur

Sequencing and assembly of the H. schachtii genomeWe measured (Supplemental Fig. 1), sequenced (BioProject PRJNA722882), and assembled the genome of H. schachtii (population Bonn) using a combination of flow cytometry, Pacific Biosciences sequencing, and Illumina sequencing. H. schachtii has the largest genome (160–170 Mb) of any cyst nematode measured/sequenced to date (Supplementary Table 1). It was sequenced to 192-fold coverage using Pacific Biosciences sequencing (fragment n50 of 16 kb), and 144-fold coverage using Illumina sequencing (150 bp Paired-end reads). The final, polished, contamination-free (Supplemental Fig. 2), assembly (v1.2) included ~179 Mbp contained within 395 scaffolds: 90% of the sequence is contained on scaffolds longer than 281,463 bp (n = 154). The assembly is a largely complete haploid representation of the diploid genome, as evidenced by core eukaryotic genes being largely present, complete and single copy (CEGMA 93.15% complete with an average of 1.12 copies each, and BUSCO (Eukaryota odb9) 79% complete with 8.2% duplicated—Supplementary Table 2). Over three million variants were phased into haplotypes (2029 blocks, N50 239.5 kb, covering 94.7% of the reference) which can be used to predict true protein variants (Supplementary data 1), and 601 larger structural variants were identified (Supplementary data 2).The trans-kingdom, lifestage-specific, transcriptomes of H. schachtii and A. thaliana provide a holistic view of parasitismWe devised a sampling procedure to cover all major life stages/transitions of the parasitic life cycle to generate a simultaneous, chronological, and comprehensive picture of nematode gene expression, and infection-site-specific plant gene expression patterns. We sampled cysts and pre-infective second-stage juveniles (J2s), as well as infected segments of A. thaliana root and uninfected adjacent control segments of root at 10 hours post infection (hpi – migratory J2s, pre-establishment of the feeding site), 48 hpi (post establishment of the feeding site), 12 days post infection females (dpi – virgin), 12 dpi males (differentiated, pre-emergence, most if not all stopped feeding), and 24 dpi females (post mating), each in biological triplicate (Fig. 1A). We generated approximately nine billion pairs of 150 bp strand-specific RNAseq reads (Supplementary data 3) covering each stage in biological triplicate (for the parasite and the host): in the early stages of infection we generated over 400 million reads per replicate, to provide sufficient coverage of each kingdom.Fig. 1: Trans-kingdom, lifestage-specific, transcriptome of H. schachtii and A. thaliana.A Schematic representation of the life cycle of H. schachtii infecting A. thaliana, highlighting the 7 stages sampled in this study. For each stage, the average number of trimmed RNAseq read pairs per replicate is shown, with the proportion of reads mapping to either parasite or host in parentheses. B Principle components 1 and 2 for H. schachtii and A. thaliana expression data are plotted. Arrows indicate progression through the life cycle/real-time. Hours post infection (hpi), days post infection (dpi).Full size imageStrand-specific RNAseq reads originating from host and parasite were deconvoluted by mapping to their respective genome assemblies (H. schachtii v.1.2 and TAIR10). For the parasite, ~500 million Illumina RNAseq read pairs uniquely mapping to the H. schachtii genome were used to generate a set of 26,739 gene annotations (32,624 transcripts – detailed further in the next section), ~77% of which have good evidence of transcription in at least one lifestage (≥10 reads in at least one rep). Similarly for the host, ~2.8 billion Illumina RNAseq read pairs uniquely mapping to the A. thaliana genome show that ~77% of the 32,548 gene models have good evidence of transcription in at least one stage (≥10 reads in at least one rep, even though we only sampled roots). A principal component analysis of the host and parasite gene expression data offers several insights into the parasitic process. Principle component 1 (60% of the variance) and 2 (19% of the variance) of the parasite recapitulate the life cycle in PCA space (Fig. 1B). The 12 dpi female transcriptome is more similar to the 24 dpi female transcriptome than to the 12 dpi male transcriptome. Principle components 1 (75% of the variance) and 2 (10% of the variance) of the host show that the greatest difference between infected and uninfected plant tissue is at the early time points (10 hpi), and that the transcriptomes of infected and uninfected plant material converge over time, possibly due to systemic effects of infection. A 12 dpi male syncytium transcriptome is roughly intermediate between a control root transcriptome and a 12 dpi female syncytium transcriptome. Given that at this stage most if not all of the males will have ceased feeding, this could be due to inadequate formation of the feeding site, or regression of the tissue. In any case, by comparing both principal component analyses, we can see that what is a relatively small difference in the transcriptomes of the feeding sites of males and females is amplified to a relatively large difference in the transcriptomes of the males and females themselves (Fig. 1B).The consequences, and possible causes, of large-scale segmental duplication in the Heterodera lineageTo understand the evolutionary origin(s) of the relatively large number of genes in H. schachtii in particular, and Heterodera spp. in general, we analysed the abundance and categories of gene duplication in the predicted exome. Compared to a related cyst nematode, Globodera pallida (derived using comparable methodology and of comparable contiguity) the exomes of H. schachtii and H. glycines are characterised by a relatively smaller proportion of single-copy genes (as classified by MCSanX toolkit17, and a relatively greater proportion of segmental duplications (at least five co-linear genes with no >25 genes between them), with relatively similar proportions of dispersed duplications (two similar genes with >20 other genes between them), proximal duplications (two similar genes with  +0.5 or  More

This study quantitatively assessed waterbody loss due to urban expansion of large Chinese cities. We first extracted multi-temporal urban boundaries to determine the expansion of cities of over one million in population from 1990 to 2018. The monthly surface-water dataset was then used to identify surface waterbodies in the study period. Depending on the ratio of surface waterbody area to urban area, cities were further divided into three categories (i.e. water-abundant, water-medium, water-deficient). Finally, we quantified the rate of waterbody loss and evaluated the spatial and temporal variation of waterbody loss as a function of urban expansion and according to city type.GUB datasetThe Global Urban Boundary (GUB) dataset (http://data.ess.tsinghua.edu.cn) was used to determine urban expansion. GUB provides data on built-up areas over 30 years, with a spatial resolution of 30 m. In the GUB dataset, nonurban areas (such as green space and water space) surrounded by artificial impervious areas are filled within the urban boundary and removed by the algorithm, which is consistent with global mapping methods. The continuous urban boundary was demarcated by morphological image processing methods, which have an overall accuracy of over 90%. In this dataset, extensive water and forests are excluded, and the impervious surface within the urban boundaries accounts for about 60% of the total surface area47. Compared with urban boundaries obtained from night-time light, GUB better separates urban areas from surrounding nonurban areas.Monthly waterbody datasetWe selected the JRC Monthly Water History V1.3 dataset(https://global-surface-water.appspot.com/), which is available from the Google Earth Engine, as the basis for representing surface waterbodies48. This data collection, which was produced by using images from the Landsat series, contains 442 images of global monthly waterbody area from March 1984 to December 2020. In this dataset, the validation confirmed that fewer than 1% of waterbodies were incorrectly detected, and fewer than 5% of waterbodies were missed altogether. We chose this dataset due to the long-term spatial distribution of waterbodies and due to mountain shadows and urban-constructions masking, which reflects the real changes in waterbodies.Theoretical backgroundIt is well known that cities have high concentrations of population and resources and expand spatially during development. There are many different perspectives on the size of cities, and studies have mostly used urban density and population to characterize them. However, because it is challenging to standardize data sources and quality, there is no unified quantitative standard49. Urban construction has concentrated human activity and brought about changes in land types. Cities are also identified as physical spaces, which can be defined as the built environment50,51. The built environment, which includes structures like buildings, roads, and other artificial constructions, is sometimes referred to as a non-natural environment52.Rural is the antithesis of urban. As large cities have spread outward in developing nations like Asia, a transitional fringe has been created by the gradual blurring of the line separating urban and rural areas53. According to McGee, good locations, easy access, and sizable agricultural land all contribute to the development potential of large cities. Thus, between urban and rural areas, there are transitional areas of active spatial morphological change known as desakota33,54. The peri-urban areas, like desakota, are gradually developed and incorporated into original built-up urban areas in urbanization. The original landscape, which included agricultural land, vegetation, and waterbodies, gradually changed into an urban land use type, i.e. impervious surface, and thus the city continues to expand outwards. Waterbody, an essential ecological element, has been heavily developed or filled in during urbanization, which may present dangerous ecological risks. In this paper, we identified the urban boundaries based on physical space to explore the encroachment activities on waterbodies during the urbanization of large cities. We determined whether existing waterbodies were transformed into urban waterbodies or encroached upon and whether waterbodies were increased in the expansion of urban boundaries, thus proposing strategies for protecting waterbodies in the future.Extracting the extent of large Chinese cities from GUB datasetTo characterize urban expansion, GUB data are selected as the original data for urban boundary selection. The Chinese administrative scale of municipalities is not exclusively urban, but also includes rural areas. In our study, cities were defined as municipal districts excluding the vast countryside within the administrative boundaries of prefecture-level cities. We identified urban areas based on the physical boundaries from the perspective of remote sensing, which can precisely track urban expansion51.In this work, we selected 159 cities with a population of over one million in 2018 based on the average annual population of urban districts from the 2019 China City Statistical Yearbook (Fig. S1). Taiwan, Hong Kong, and Macau are omitted. According to statistics, China had 160 cities with populations exceeding one million in 2018. However, due to the lack of data for the built-up area in 1990, Guang’an was not included in the study. We thus obtained 159 cities from the GUB dataset. Due to numerous fragmented patches within the administrative boundary, the population identified the main urban areas, and max patch areas were comprehensively based on the urban boundaries. Through manual detection and adjustment of the map, we determined that the location of the extracted urban area was consistent with that of the municipal government, and the boundary was extracted for each period. We took the growth area as the expansion area, with the original area being the city at the onset of each period (Fig. S3).We used the average annual urban growth (AUG) rate to characterize the rate of urban expansion, as is widely done to evaluate urban expansion55,56. It is calculated as$${text{AUG}} = left[ {frac{{Land_{t1} }}{{Land_{t0} }}^{{frac{1}{t1 – t0}}} – 1} right] times 100% ,$$
where (Land_{t0}) and (Land_{t1}) represent the urban land area at time t0 and t1, where t0 and t1 are the start and end of the given study period.Identification of urban waterbodiesUrban waterbodies contain all the components of urban flow networks above the ground and include natural waterbodies such as lakes, rivers, streams, and wetlands and artificial waterbodies such as parks and ponds48. We identified all waterbodies existing within the urban boundary as urban waterbody. Considering urban expansion, urban waterbodies vary as urban boundary shift at different stages. Our study explored how the original waterbodies changed under urban expansion, including whether they were kept as urban waterbodies or encroached upon. Considering the dryness or wetness of each year, we used the data for 3 years (36 months) around each period (1990, 1995, 2000, 2005, 2010, 2015, and 2018) to describe the waterbody. Not all waterbodies could be detected for each month of the year; for example, freezing may prevent waterbodies from being detected. To cover seasonal and permanent waterbodies, we used the waterbody frequency index (WFI), which is calculated as the fraction of waterbody months within the 3 years to identify stable waterbodies pixel by pixel57. The spatial distribution of each waterbody was then mapped comprehensively for each period. By comparing the extracted waterbody with the long-time-series high-resolution remote-sensing images from Google Earth, we found that the extracted waterbodies fit the actual waterbody distribution quite well (Fig. S2):$$WFIleft( i right) = frac{WMleft( i right)}{{DMleft( i right)}}$$
where WFI(i) is the water occurrence for pixel i in the images before and after the given year, and i is the pixel number for the study area. WM(i) is the number of months during which the waterbody is detected in i pixel over the 3 years. DM(i) is the number of months during which the data are available in pixel i. If the waterbody frequency index of a pixel is greater than 25%, this pixel is considered as a waterbody; otherwise, it is not.City classification based on surface waterbodyCities with over one million in population may not be short of waterbodies, but significant differences remain in surface waterbody abundance. Due to large differences in city size, it is inappropriate to use waterbody area as a criterion. Considering the influence of urban expansion, we ranked 159 cities according to the indicator of waterbody fraction (WF), namely the fraction of the original surface water within the urban boundary in 2018. Waterbodies not impacted by urbanization were taken as the original surface waterbody, which used the average surface waterbody from 1985 to 1991 as baseline. We used the natural break method to divide cities into abundant, moderate, and deficient levels (referred to as Type I, Type II, and Type III, respectively) and evaluate the abundance of waterbodies in cities. Based on the waterbody fraction (WF) value, which is calculated as follows:$${text{WF}} = frac{{Water_{origin} }}{{Land_{2018} }}$$
where WF is used to judge the urban waterbody abundance in cities. (Water_{1990}) is the origin surface waterbody area (used the year in 1985–1991) in the urban boundary of 2018, (Land_{2018}) the urban land area in the urban boundary of 2018.Temporal characteristic of waterbody loss and gainTo understand the spatial–temporal features of surface waterbodies, we used five normalized indicators to compare waterbody variations between cities during urban expansion from the overall perspective and from the city perspective.The variation in original natural waterbodies reflects the intensity of the natural resource development in urban expansion. We summarized the reduction and preservation of original waterbodies in urban expansion areas with a population of over one million to represent the encroachment of urban expansion on waterbodies:$$WL = frac{{sum NWL_{t0_t1} left( i right)}}{{sum W_{t0} left( i right)}} times 100%$$$$WP = frac{{sum (W_{t0} left( i right) – NWL_{t0_t1} left( i right))}}{{sum W_{t0} left( i right)}} times 100%$$
where i labels the city within the 159 cities, WL and WP are the fractions of waterbody loss and preservation in urban expansion areas of all cities, (NWL_{t0_t1}) is the net waterbody loss during period t0–t1 (, and W_{t0}) is the natural waterbody in the urban expansion area at time t0.To estimate the net waterbody loss caused by urban expansion at various stages, we used the standardized indicator, annual average net waterbody loss rate (ANWL), to compare waterbody loss speeds over time. This indicator is independent of the difference in waterbody abundance and can be compared over time. Waterbody loss is one part of the impact of urbanization; the other is waterbody gain. We used the same method to evaluate the annual average net waterbody gain rate (ANWG). The formulas are$$A{text{NWL}} = frac{{NWL_{t0_t1} }}{{W_{t0} left( {t1 – t0} right)}} times 100%$$$$ANWG = frac{{NWG_{t0 – t1} }}{{W_{t0} left( {t1 – t0} right)}} times 100%$$
where NWL and NWG are the net waterbody loss and gain, respectively, and the other abbreviations are the same as above.Considering the direct impact of urban expansion, we used a normalized indicator, the average net waterbody loss velocity of urban expansion ((AWLV)), which refers to the amount of waterbody encroachment per unit urban expansion area. It quantifies the time-heterogeneity of waterbody loss due to urban expansion and is calculated as follows:$$AWLV = frac{{NWL_{t0_t1} }}{{Land_{t1} – Land_{t0} }}$$We calculated these indicators for the six expansion periods (1990–1995, 1995–2000, 2000–2005, 2005–2010, 2010–2015, and 2015–2018) (Fig. 3). In the study, if the waterbody pixel count is zero at the onset of the period, the indicator for the period is abnormal and thus excluded. More

Basic characteristics of river sedimentsA considerable variation was found in the distribution of clay (81 to 48.4 g kg−1), silt (145 to 656 g kg−1), and sand (38 to 821 g kg−1) particles among sediment materials. The associated coefficient of variations (CV) was 57, 59.5, and 41%, respectively. Statistical data related to the physicochemical properties of sediments and their main elements are reported in Table 2. The variations in particle size distribution located sediment material in seven textural classes ranging from loamy sand to silty clay. The high variability in particle size distribution suggests that different sets of geogenic and anthropogenic processes are enacted in the development and distribution of sediments in the rivers. The pH and CCE ranged from 7.4 to 8.2 and 31 to 251 g kg−1, respectively, indicating the dominancy of alkaline-calcareous condition. None of the sediment samples exhibited salinity conditions (EC  > 4 dS m−1) with EC in the range of 0.3 to 1.4 dS m−1. A relatively low range of OM was found in all samples ranging from 7 to 61 g kg−1 with a mean value of 19 g kg−1. This range of OM coincides with the corresponding values in regional soils47. Except for pH, other sediments properties demonstrated above 35% of CV illustrating a wide range of variability in sediments’ physicochemical properties across the study rivers.Table 2 Summary statistics of sediment properties.Full size tableThe highest concentration among major elements was observed in SiO2, varying between 37.5 and 55.2%, with a mean percentage of 44.9%. This element followed in magnitude by Al2O3 (8.9–15.9%), CaO (5–14.3%), Fe2O3 (4.8–10%), MgO (2.4–17.2%), K2O (1.2–3.1%), Na2O (0.68–2.7%), SO3 (0.01–4.8% g kg−1) (Table 2). Considering the semi-arid climatic condition of the study region, higher levels of SiO2 and lower levels of Al2O3 may indicate that the silicate minerals forming the sediments of the area have not been subjected to severe weathering processes. Likewise, the Na2/K2O ratio was greater than 1 in the majority of sediment samples, implying an enrichment of potassium feldspar and the relatively intense weathering of Na-bearing minerals in the region48,49. The CIA value was in the range of 64.9 to 85.7% with a mean percentage of 72.9%, representing a moderate chemical weathering intensity of lithological materials (65%  Pb  > Cu  > Cd which varied largely among the sampling points. The level of Zn, Cu, Cd, Pb, and Ni varied in the ranges of 32.6–87.5, 14.2–33.3, 0.42–4.8, 14.5–69.5, and 20.1–183.5 mg kg-1, respectively, for winter, and 35.3–92.5, 15.6–35.1, 0.47–5.1, 15.5–73.1, 23.2–188.3 mg kg−1 for summer. The obtained ranges are comparable with data found in previous studies in Asia4,54,55,54.Figure 2The comparison of the mean concentration of Zn, Cu, Cd, Pb, and Ni elements in the study rivers’ sediments during summer and winter. Different letters show significant differences in metal content among rivers pooled over seasons at P  More

This study aimed to compare the extent of contrafreeloading in kea to that in Grey parrots, given that the two species exhibit very different levels of play: specifically, kea exhibit complex and frequent play29,30,35,36, whereas Greys exhibit considerably less play than several parrot species29. We found that, at the group level, although the overall amounts of kea classic contrafreeloading were nonsignificant, as a percentage of behaviour, kea generally contrafreeloaded more than Grey parrots in Experiment 1, whereas the opposite was true for Experiment 2. We compare the various behaviour patterns in detail, and propose explanations for our results below.The most interesting comparisons for Smith et al.’s hypothesis are the results from classic contrafreeloading. In Experiment 1, kea performed this behaviour at non-negligible levels, given the supposed rarity of the behaviour5 (two birds at 50%; the others varying between 39 and 47%). In contrast, although one Grey did classically contrafreeload at a statistically significant level, the other three were at ≤ 36%. These data suggest that the kea may have found the task more engaging than did the Greys. However, given that only two kea chose to pop the lid of an empty cup in control trials significantly above chance, whereas three of the four Greys did so significantly above chance and one at chance, we doubt that the kea found the task inherently rewarding. We note that this comparison between both species must be interpreted cautiously due to differences in methodology: For the Greys, the control trials were performed at the end of the study, by which point they may have learnt to associate lid-popping with reward. However, the data from experimental trials in Smith et al.13 are such that their birds would have been primed in the opposite direction: For example, three of those four birds rarely chose the empty lidded cup when free food was available, nor did they classically or super contrafreeload to any significant extent13; an association-driven explanation is therefore unlikely. In contrast, the kea experienced this control condition at the start of the experiment, allowing them 20 trials to become acquainted with the affordances of both options that would be available throughout the study (lid-popping versus not lid-popping). This opportunity was important for kea, as this species has been previously shown to learn about object properties through extensive object manipulation37. That kea popped lids at or above chance in these first 20 control trials suggested two possibilities: (1) After these 20 trials, the task may have been familiar enough to no longer be of much interest (i.e., no longer novel and worthy of consideration) by the time rewarded trials began (recall nonsignificant downward trends for Harley Quinn and Blofeld). (2) They acquired some interest in popping the lids. This latter case seems more likely, as the lid-popping task still likely provided some added value. Kea engaged in non-negligible levels of classic contrafreeloading, such that the chance to pop a lid and eat could be considered more interesting than simply eating an identical but freely available reward. Furthermore, three kea chose a lidded, empty cup over a free, least-preferred reward at least half the time, again suggesting that the activity held some appeal of its own.In Experiment 2 (which corresponds to classic contrafreeloading), all kea preferred freeloading for the walnut without a shell; two Greys, in contrast, nut contrafreeloaded at a statistically significant extent. This variability in behaviour at both the individual and species levels reveals the significance of a task’s proximate and potentially ultimate values in parrots’ choice to contrafreeload. Interestingly, although species like kea are hypothesized to prefer food items requiring high manipulation38,39, nut-cracking—chosen as an activity to provide direct comparison with the Greys13—is not prevalent in kea diet40, and that activity thus may not have been appropriate as an ethologically relevant one for kea. Greys, in contrast, are known to crack nuts in nature41. Future research could use a more ecologically relevant task for the kea, such as working to access food via digging or scraping32.As with Smith et al.’s Greys13, kea in Experiment 1 performed calculated contrafreeloading to a statistically significant extent. All kea did so on over 83% of trials; for the Greys, three birds were close to 90% but one was at only 67%. Kea consistently selected their preferred food out of the two options provided, suggesting that the lid-popping action did not deter kea from selecting their preferred reward. In related trials, where the lid-status of food paired with an empty cup varied, kea, like some Greys13, preferred lidded food over an empty lidless cup, again showing that lid-popping for food was an acceptable task.When examining situations in which food was discarded after contrafreeloading, we found that this choice in Experiment 1 was most common for Bruce. Notably, Bruce lacks a top mandible, making many of the manipulative behaviours more difficult to execute42. Bruce demonstrated consistent food preferences throughout the experiment, however, indicating that the reason some foods were discarded was, indeed, because they were too difficult for him to manipulate. In Experiment 2, Harley Quinn was the most likely to discard the nut, and did so exclusively in trials in which she chose the walnut without the shell (freeloaded). In these occasions, Harley Quinn was observed choosing the nut by tapping on it or the cup.Like the Greys, the kea failed to super contrafreeload to a statistically significant extent. Furthermore, contrafreeloading trials in which a lid was popped but the food underneath was not consumed occurred most often with the least-preferred food. Given kea’s performance on control trials, the super contrafreeloading results are not surprising. Interestingly, when lid-status of food paired with an empty cup varied, some Greys very rarely—and depending on food desirability—preferred to pop the empty cup’s lid rather than consume the free food; as noted earlier, three of eight kea did so on at least half the trials when the food in the lidless cup was their least preferred option (sultanas). Both kea and Greys thus likely placed the appeal of the task along some “value scale” along with that of the available food rewards, the combination influencing their behaviour when the two variables were presented in various permutations. Notably, even in control trials, where no food was involved, no bird of either species found the task aversive, engaging in the behaviour at least 50% of the time. Future research could investigate how a different, more rewarding task would influence this balance and thus contrafreeloading for both species.One possible alternative explanation for kea’s higher rates of contrafreeloading relative to those of Greys could be their natural tendency to probe and manipulate objects, thus causing them to pry off cup lids rather than manipulate lidless (open) cups. Were this action exploratory in nature, we would have observed significant decreases in behaviour as the experiment progressed, but note that we found no significant changes in any bird. Were they consistently drawn to lids and this behaviour were hard-wired, then we should have observed lid-popping appear significantly above chance across all three types of contrafreeloading. However, as discussed previously, kea did not significantly contrafreeload in the classic condition and actively freeloaded in super contrafreeloading conditions, suggesting that they were not simply interacting with lidded cups preferentially, but rather attending to the contents in the two cups and avoiding the additional manipulation of the lid when it led to a less (or, more often than not, equally) preferred food reward.Another potential explanation for the differences observed between kea and Greys might be found in the theoretical overlap between contrafreeloading and play, and how individuals might view the contrafreeloading action as a type of play. As a seemingly nonfunctional, intrinsically motivating behaviour occurring in low-stress environments, incurring a positive mood, varying between conspecifics, and often incomplete and/or repeated14,15, play shares many proximate-level attributes with contrafreeloading13. Our results demonstrate that kea subjects inhabiting a low-stress, captive environment repeatedly chose to engage in classic contrafreeloading to a non-negligible extent and calculated contrafreeloading to a significant extent, varied in their behaviour between individuals, and at times, left the task incomplete (e.g., left food uneaten). Furthermore, evidence for intrinsic motivation to perform a given task is suggested by the kea’s overall differential behaviour between the two experiments, as well as inter-individual differences.Importantly, this study serves only as a first step into determining whether play manifests as a form of contrafreeloading, but cannot ascertain that this is the only possible explanation for the presence or degree of contrafreeloading in the two species. Several alternative explanatory theories regarding the occurrence of contrafreeloading are enumerated in the discussion of Smith et al. (e.g., work ethic; information gathering; relief from boredom)13, and various other potential explanations (beyond playfulness) may reside at the species-level. Grey parrots (Psittacidae) and kea (Strigopidae) are separated by 50–80 million years of evolution43 and differ in their neurobiology (i.e., the size of the shell region related to vocal and possible cognitive abilities44). Differing ecological evolutionary pressures are also likely relevant: an island-based habitat39, a lack of natural predators30,45, and generalist diets40,46,47 are thought to have shaped the playfulness and cognitive abilities of kea30,40,46,47. Greys, in contrast, evolved predominantly on a continent (i.e., although they can be found on islands such as Principe, the Congo Grey is endemic to central Africa48,49), are subject to considerable predation48,50,51,52, and have a relatively less generalist diet (diverse but almost exclusively vegetarian and in which nuts play a significant role; see review in50). Such disparate evolutionary trajectories may offer other potential explanations for the differences in contrafreeloading observed between the two species, and future research could examine differences at genetic and/or neurological levels.The varying rates of contrafreeloading observed between the species could have also been influenced by other factors. For example, although both parrot groups studied here inhabit enriched environments, are habituated to participating in experimental trials, and have access to food ad libitum, their habitats are markedly different. Notably, the Grey subjects live in “man-made” settings (i.e., Griffin and Athena in a lab; Pepper, Franco, and Lucci in private homes), whereas the kea inhabit a naturalistic zoo enclosure. Physical enrichment, although somewhat different in kind, is unlikely to have differed in quantity, as all birds are provided routine naturalistic foraging, and Lucci lives in a free-flight aviary. More likely is the difference in sociality: Relatively more subjects reside together in the kea group (15) compared to the Greys (two groups of two Greys and one Grey living with two birds of differing species), and thus variables such as social stimulation and flock-based foraging techniques could have contributed to the expression of contrafreeloading (note that subadult male kea are known to obtain food through kleptoparasitism32). In order to elucidate the role of habitat on contrafreeloading, future studies could examine the behaviour of species residing in more comparable captive conditions.Future work should aim not only to apply these same methodologies to a broader range of parrot species, but also objectively quantify frequency and complexity of play across a wide range of parrots to allow a direct correlation between play and contrafreeloading over phylogeny in the parrot order. The apparent link between play behaviour and encephalisation in parrots53 offers another possible avenue for cross-species comparisons on contrafreeloading. Future research could also employ cognitive bias tests to quantify the mood of birds before and following contrafreeloading54, directly manipulate subjects’ participation in play behaviours or other control behaviours and observe whether engaging in play can increase contrafreeloading rates at the individual level, or perform behavioural coding of playfulness and/or arousal before and after contrafreeloading. Future research could incorporate more ecologically relevant contrafreeloading tasks to examine this behaviour at both the individual and species level, and approach the phenomenon by using both genetic and neuroscience techniques.In sum, contrafreeloading is, by its very nature, an enigma whose study presents many difficulties. It varies across the diverse contexts within which it is studied, and given that it is rarely exhibited to a statistically significant extent, analyses that require comparing nonsignificant behaviour patterns across individuals and/or species is a challenging undertaking. Many explanations have been proposed, but contrafreeloading is still poorly understood, and its correlation with play is likely only one of several logical rationales. Nevertheless, our findings suggest that interest in play should not be discounted as a contributing factor. More

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Similar non-native and invasive flora, such as the fever tree (pictured) are found in regions previously occupied by the same European empire.Credit: Alamy

In 1860, a British expedition raided the highland forests of South America, looking for a hot commodity: Cinchona seeds. The bark of these ‘fever’ trees produces the anti-malarial compound quinine, and the British Empire sought a stable source of the drug for its soldiers and civil service in India. After cultivation in the United Kingdom, young Cinchona trees were planted across southern India and what is now Sri Lanka.The British quinine scheme failed — instead, a species introduced to Java, now part of Indonesia, by the Dutch Empire later dominated the global market — but Cinchona trees are still common in parts of India.Such botanical legacies of imperial rule are common, finds a study published on 17 October in Nature Ecology & Evolution1. Regions that were once occupied by the same European colonial power — such as India and Sri Lanka — tend to have similar species of non-native and invasive plants. The longer the regions were occupied, the more their populations of invasive species resemble each other, the research found.Alien floraThe link between European colonialism and invasive species is intuitive, and has been noted by other researchers, says Bernd Lenzner, a macro-ecologist at the University of Vienna who led the study. To test the association, his team turned to the Global Naturalized Alien Flora database, which maps the distribution of nearly 14,000 invasive plant species.
The imperial roots of climate science
Across more than 1,100 regions, including 404 islands, the researchers found that regions once occupied by the British Empire had more similarities in their invasive flora than did ‘artificial’ empires that the team assembled from random regions. This was also the case for regions once part of the Dutch Empire (former Spanish and Portuguese colonies had alien-plant compositions similar to those of the artificial empires).Climate and geography play an important part in explaining the overlap in the diversity of invasive species, modelling by Lenzner’s team found, but so does the length of time regions were occupied by an imperial power. Regions that were central to trade, such as southern India for the British Empire and Indonesia for the Dutch Empire, formed clusters with considerable overlap in invasive-plant composition.The analysis did not look at when individual plant species were introduced or why. But anecdotally, many of the plants that were commonly taken to former empires were once of economic value and their populations were probably established on purpose, says Lenzner.Global trade impactsThe study’s conclusions might be “super obvious”, but they have important implications for conservation, says Nussaïbah Raja, a palaeontologist at Friedrich-Alexander University of Erlangen–Nürnberg in Erlangen, Germany. “We should be taking this history into consideration when we think about management of species.” Appreciating the history of introduced plants — as well as their place in today’s ecosystems — could help conservationists to handle future changes in biodiversity, such as those driven by climate change, Raja adds.Global trade is beginning to overwrite the colonial legacy of introduced plants. For example, the analysis showed similarities between invasive plant populations in Fujian, China, and some parts of Australia. Although both places were once connected by the British Empire, more recent global trade might also be partly responsible for the overlap.“We are still seeing these imprints of the colonial-empire legacies from centuries ago,” Lenzner says. “So what we’re doing and the species we’re redistributing today will be visible far into the future.” More

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The Asiatic cheetah (Acinonyx jubatus venaticus) once roamed throughout the Middle East and central India. Today there remain only an estimated 20 free-ranging individuals in central Iran and 5 in captivity. International economic sanctions against Iran have had devastating effects on its cheetah conservation and management (see go.nature.com/3suohzb; in Farsi). To help overcome these effects, we suggest that Iran work with the Indian government, which is conducting a rewilding programme for cheetahs.
Competing Interests
The authors declare no competing interests. More