Philippa Kaur

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

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

Insect rearingThe cotton mealybugs used in this study were originally collected from Rose of Sharon, Hibiscus syriacus L. (Malvales: Malvaceae) in Jinhua, Zhejiang Province, China, in June 2016. They were maintained on fresh tomato plants (cv. Hezuo-903, Shanghai Changzhong Seeds Industry Co., Ltd, China) in a climatically controlled chamber maintained at 27 ± 1 °C, 75% relative humidity (RH), and a photoperiod of 14:10 (L:D). For detailed insect rearing and tomato cultivation methods see ref. 56.Scanning electron microscopy (SEM) of P. solenopsis waxSEM was used to observe changes in wax on the body surface of adult P. solenopsis females according to the methods of Huang et al.57. Briefly, collected insects were taped onto a stub and dried in an ion sputter (Hatachi, Tokyo, Japan) under a vacuum. After gold sputtering, the samples were observed using a TM-1000 SEM (Hatachi, Tokyo, Japan). Photos were scanned from the dorsal part of the third thoracic segment. Thirty insects were used for both RNAi-treated and control groups.Chemical composition analysis of mealybug waxA small soft brush was used to collect wax filaments from the body surface of P. solenopsis females. Prior to use, the brush was washed successively by 70% ethanol, sterile water, and 1× sterile phosphate-buffered saline (PBS, pH 7.4). The wax was collected into a clean chromatography vial for the following experiments. Two vials of wax, each collected from 1000 adult females, were dissolved in 1 ml of methanol and 1 ml of n-hexane, respectively. The vials were stirred gently for 3 min, kept at room temperature for 30 min, and then put into an S06H ultrasonic vibrator (Zealway, Xiamen, China) for 30 min to dissolve the wax sufficiently. The samples were analyzed on a TRACE 1310 (Thermo Scientific, Waltham, USA) gas chromatograph (GC) equipped with an ISQ single quadrupole MS and interfaced with the Chromeleon 7.2 data analysis system (Thermo Scientific, Waltham, USA), with a constant flow of helium at 1 ml/min. For each sample, a splitless injection of 1.0 μl was respectively made into a polar TG-WaxMS (Thermo Scientific, Waltham, USA) and a nonpolar TG-5MS (Thermo Scientific, Waltham, USA) 30 m × 0.25 mm × 0.25 μm capillary column. The temperature program for polar column samples was as follows: 40 °C for 2 min, then 5 °C/min to 240 °C, hold 10 min; the program for nonpolar column samples was: 40 °C for 2 min, then 5 °C/min to 300 °C, hold 5 min. Injector and detector temperatures were, respectively, set at 250 and 230 °C for polar column samples, and at 300 and 300 °C for nonpolar column samples. Mass detection for all samples was run under an EI mode with a 70 eV ionization potential and an effective m/z range of 35–450 at a scan rate of 5 scan/s. Chemical compounds were identified by mapping against the NIST database. The relative content of each compound was calculated by peak area which was determined using the Agilent MassHunter system.RNA extraction and RT-qPCRTotal RNA was isolated using TRIzol reagent (Invitrogen, Carlsbad, CA) following the manufacturer’s instructions, and RNA quality was accessed using agarose gel electrophoresis and a Biodrop μLite. 800 ng of total RNA was used for cDNA synthesis using the HiScript III RT SuperMixfor qPCR (+gDNA wiper) (Vazyme Biotech Co., Ltd., Nanjing, China), according to the manufacturer’s instructions. Quantitative RT-PCR (RT-qPCR) was conducted using an AriaMx real-time PCR system (Agilent Technologies, USA), using a 20 μl reaction containing 2 μl of 10-fold diluted cDNA, 0.8 μl of each primer, and 10 μl ChamQ SYBR Color qPCR Master Mix (Vazyme Biotech Co., Ltd., Nanjing, China). The RT-qPCR thermocycling protocol was 95 °C for 30 s, followed by 40 cycles of 95 °C for 10 s and 60 °C for 30 s. The PsActin gene was used as an internal control. At least three biological replicates were used for each experiment. Quantitative variations were evaluated using the relative quantitative method (2−ΔΔCt)58.Transcriptome analysis of integumentary and non-integumentary tissuesTo obtain the integument and other tissues, adult P. solenopsis females were dissected in 1× sterile PBS (pH 7.4) on a sterile Petri dish. Dissected fresh tissues were directly used or frozen in liquid nitrogen and stored at −80 °C for follow-up experiments. We sequenced the transcriptomes of integumentary and non-integumentary tissues (all other tissues without integument) dissected from 150 adult females, with each sample being repeated in triplicate. mRNAs were purified from total RNA via oligo (dT) magnetic beads, and the fragmented mRNAs were then reverse transcribed into cDNA using random primers. Constructed pair-end libraries were sequenced using an Illumina HiSeq X Ten platform in Novogene (Beijing, China). After quality control, the clean RNA-Seq data of the six libraries were aligned with the P. solenopsis genome (http://v2.insect-genome.com/Organism/624) using HISTAT259. Then featureCounts60 and DESeq261 were used for the differential expression analysis of genes. The threshold for differentially expressed genes (DEGs) was defined by log2fold ≥ 1 or ≤−1 and a padj-value  More

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Retrieval of E. coli plasmid sequencesAll E. coli sequences were downloaded from the NCBI FTP server in May 2020. To establish an initial collection of plasmids, only complete genomes with an associated plasmid were retained. All genomes were verified for belonging to the species E. coli using kmerfinder (https://cge.cbs.dtu.dk/services/KmerFinder/). Sequence type (ST) was determined via multi-locus sequence typing (MLST) based on the 7-gene Achtman scheme using pubMLST (https:/github.com/tseemann/mlst). Only genomes with exact matches were assigned for each ST and used for subsequent analysis. To ensure our sequences were sufficiently representative of E. coli pathogens expected in nature, a systematic literature search (see description below and Fig. S1) was conducted to establish an expected distribution of STs (Table S1). This information was used to update our initial collection to match the top 4 most prevalent STs (131, 11, 73, and 95). Specifically, to identify supplementary plasmid sequences, genome accession IDs were chosen from EnteroBase based on the following criteria: the strain was matched to the correct ST and had a high-quality genome sequence (based on N50  > 20,000 and the number of contigs  0.1, 2-tailed student t test). For the second method, all kanR plasmids were used, and instead changed the hosts such that DH5αPro cells were in competition with DH5αPro containing a spontaneous rifampicin-resistant mutant (rifR). Any rifR strain was quantified on rifampicin-containing plates, and the second strain was quantified by rifampicin CFU minus CFU obtained on blank plates. We established that rifR exhibited no fitness defects by (1) growth rates between the wild-type (WT) strain (W) and rifR (M) (Fig. S5D), and (2) directly competing the two control strains (Fig. S5E). In both cases, results were statistically indistinguishable (p  > 0.1, two-tailed student t test). KanR/cmR and WT/rifR experiments were each conducted in LB or M9CAG, respectively. In all cases, experiments were repeated with at least three independent biological replicates.Time-kill measurements in the presence of carbenicillinAll strains were grown as previously described. Time-kill experiments entailed hourly measurements of CFU in presence of carbenicillin at either 3.75 μg/mL (3x IC50) or 5 μg/mL (4x IC50) over a span of 2 or 3 h, including time 0. Specifically, overnight cultures were first diluted 1:100 into LB media containing 1 mM IPTG and 50 μg/mL kanamycin and sub-cultured for two hours in a 37 °C incubator with shaking at 250 rpm. Following this, cell density was adjusted as necessary to achieve a starting OD600 of ~0.15 in all cases. Adjusted subcultures were then aliquoted into a 96-well plate and the appropriate carbenicillin treatments were added directly to the well. Plates were sealed with a paper film and placed in a 37 °C incubator with shaking at 250 rpm. Initial collection for time=0 was acquired before carbenicillin treatment. Thereafter, 10 μL of culture was removed from the well every hour, 10-fold serial dilutions were performed and 10 μL was plated on blank LB agar with three technical replicates at each time point. Colonies were counted after plates were grown for 16 h in a 37 °C incubator to determine CFU. This procedure utilized 14 strains of DH5αPro transformed with kanR plasmids of interest – ctrl, katG, lpxM, yfbR, aroH, pld, fdtC, agp, eptC, arcA, argF, mmuM, ahr, and fabG. CFUs were averaged for all technical replicates, and experiments were conducted with at least three independent biological replicates.Oxygen consumption rateOxygen consumption rates (OCR) were obtained with the Resipher device from Lucid Scientific. The selected strains were grown overnight as previously described. Overnight cultures were resuspended in M9CAG media with 1 mM IPTG and 50 μg/mL kanamycin, and placed in 25 °C for one hour to initiate gene expression. Following this, cells were diluted 10x into M9CAG media containing kanamycin and IPTG, and 100 μL was aliquoted per well into a 96-well microtiter plate according to the manufacturer’s instructions. Plates were placed at 30 °C to minimize growth, and oxygen concentration (μM) was measured immediately thereafter. 24 wells were measured consisting of 6 technical replicates for each strain. Given the clear well-well variability (Fig. S8B, C), data shown are for one biological replicate. However, qualitative trends were consistently reproduced in multiple independent experiments.StatisticsIn all cases where t tests and ANOVA’s were used, data was first verified to be normally distributed using Kolmogorov test for normality. Otherwise, Mann-Whitney U-tests were conducted. For panels with multiple tests, Bonferroni correction was used to adjust the p values. To determine whether any metabolic category was significantly dependent on incompatibility groups, we implemented logistic regressions in MATLAB with the function fitglm. Random forest classification was used to establish the relative importance of prevalent metabolic genes and gene categories predicting the presence of antibiotic resistance genes. Chi-square tests were conducted to determine significant co-occurrence of individual antibiotic resistant and metabolism genes. Dissociative relationships were distinguished by the odds ratios from the chi-square tests. To investigate whether the strong associations and disassociations were driven by evolutionary constraints, or simply artifacts of a common ancestor, we re-ran our statistical analysis using Coinfinder [29] to take in our gene presence-absence data, along with the genome phylogeny, and compute the Bonferroni-corrected statistical likelihood of coincidence (either associations or dissociations), thereby accounting for evolutionary relatedness. 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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Cline and colleagues analysed spatiotemporal datasets covering 5000 km of popular trout rivers from 1983 to 2017, finding that fishing pressure was four times higher in cold-water sections of rivers than adjacent cool-water sections of rivers, with fisher spending in cold-water sections generating US$500,000 km−1 year−1 and cool-water sections generating US$60,000 km−1 year−1. Overall, 17% and 35% of the current cold-water habitats are projected to be warmer than 18 °C (the threshold for trout thermal extremes) by 2040 and 2080, respectively, with some river sections possibly experiencing habitat losses in excess of 80% by 2080. The combined effects of cold-water habitat loss and increased frequency and severity of drought on fishing pressure could result in 64% declines in fishing river sections by 2040 and 76% declines by 2080. The cumulative impacts of these environmental changes in fishing spending across these rivers could put a total of US$103 million year−1 and US$192 million year−1 at risk by 2040 and 2080, respectively. More