Philippa Kaur

Evapotranspiration measurements and above-ground biomassFigure 1 shows daily evapotranspiration (ET, mm day−1) of each CC tested before mowing (DOY, day of the year, 184) and at 2, 8, 17 and 25 days after mowing (DOY 190, 196, 205 and 213); bare soil was also included as a reference. Before mowing, ET rates showed significant differences between and within the three groups. CR plants had a mean ET of 8.1 mm day−1, which was lower, compared to the other two groups (10.6 and 18.6 mm day−1 for GR and LE, respectively) and the bare soil control (8.5 mm day−1). On DOY 184, values as high as 9.4 (Glechoma hederacea L., GH) and 9.8 mm day−1 (Trifolium subterraneum L. cv. Denmark, TS) were found (Fig. 1), while ranging around 7 mm day-1, Dichondra repens J.R.Forst. & G.Forst. (DR), Hieracium pilosella L. (HP), and Sagina subulata (Swartz) C. Presl (SS) ET were lower than soil evaporation itself.Figure 1Vertical bars represent the daily water use as referred to unit of soil (ET, mm day−1) for the bare soil (yellow) and all the cover crop species as divided into creeping plants (shades of blue), legumes (shades of green) and grasses (shades of orange). Evapotranspiration was measured though a gravimetric method before (i.e. − 4) and at 2, 8, 17 and 25 days after mowing. ET data are mean values ± SE (n = 4).Full size imageOn the same day, a large ET variation was recorded within the GR group as Festuca arundinacea Schreb. cv. Thor (FA) scored the highest daily ET values (13.4 mm day−1), whereas in Festuca ovina L. cv. Ridu (FO), water loss was reduced by 45% (7.5 mm day−1). Within the 15 CCs, LE registered the highest pre-mowing ET with Trifolium michelianum Savi cv. Bolta (TM) peaking at 22.6 mm day−1. However, within LE, Medicago polymorpha L. cv. Scimitar (MP) showed ET values as low as 12.1 mm day−1 (Fig. 1).Two days after mowing, all tested CCs recorded ET values lower than 9 mm day−1 (Fig. 1). Moreover, water use reduction among LE ranged between 56% (M. polymorpha, MP) and 73% (T. michelianum, TM), such that T. michelianum (TM, 6.1 mm day−1), Medicago truncatula Gaertn. cv. Paraggio (MT, 5.6 mm day−1) and M. polymorpha (MP, 5.2 mm day−1) registered ET values lower than the bare soil (7.0 mm day−1). Even though registering a consistent ET reduction after mowing, GR retained ET rates slightly higher than bare soil, except for F. ovina (FO), which recorded the lowest at 6.3 mm day−1. Subsequent samplings showed that most of the CCs had a progressive recovery in water use (Fig. 1) and data taken 17 days after mowing confirmed that Lotus corniculatus L. cv. Leo (LC) and all GR fetched pre-mowing ET rates. Medicago lupulina L. cv. Virgo (ML) registered a partial recovery with similar rates (about 13 mm day−1) at 17 and 25 days after the mowing event. F. ovina and all remaining LE stayed below 10 mm day−1 with ET values close to the control until the end of the trial. At 17 days from grass cutting, under a quite high exceeding-the-pot biomass, both G. hederacea (GH) and T. subterraneum (TS) reached ET values as high as 12.0 and 11.4 mm day−1, respectively. On the other hand, D. repens (DR), H. pilosella (HP), and S. subulata (SS) even though with slightly higher ET values than those registered at the beginning of the trial (DOY 184), remained close to the soil evaporation rates until DOY 213.Aboveground dry clipped biomass at the first mowing date (ADW_MW1, DOY 188) showed large differences among groups, as represented in Table 1. ADW_MW1 within LE was quite variable, as values ranged between 274.3 g m−2 (M. polymorpha, MP) and 750.0 g m−2 (T. michelianum, TM). With a mean value of 565.9 g m−2, LE aboveground biomass was 80% higher than the mean GR ADW_MW1 (110.2 g m-2). F. ovina (FO) scored the lowest value at 48.4 g m−2 among grasses, while within the creeping group, G. hederacea (GH) and T. subterraneum (TS) had biomass development outside the pot edges totalling 89.6 g m−2 and 23.2 g m−2, respectively.Table 1 Aboveground dry biomass clipped at the first mowing event (ADW _MW1), the corresponding leaf area surface index (LAI) and water use per leaf area unit (ETLEAF) of all cover crops tested.Full size tableLeaf area index (LAI, m2 m−2) at mowing showed the highest values in LE with LAI peaking at 12.4 (Table 1). Among GR, LAI did not show significant differences, being around 1.2. Concerning CR, LAI was assessed at 0.2 and 0.8 for T. subterraneum (TS) and G. hederacea (GH) respectively, while LAI estimated through photo analysis ranged between 1.3 (D. repens, DR) and 3.6 (T. subterraneum TS).Evapotranspiration per leaf area unit (ETLEAF) was notably higher in GR, ranging between 7.75 (F. ovina, FO) and 9.22 (Lolium perenne L. cv. Playfast, LP) mm m−2 day−1 (Table 1). In descending order, ETLEAF was the highest in D. repens (DR, 5.46 mm m−2 day−1). Similar ETLEAF was found when comparing some LE and CR species such as M. truncatula (MT, 3.40 mm m−2 day−1), M. lupulina (ML, 4.05 mm m−2 day−1), G. hederacea (GH, 3.68 mm m−2 day−1), H. pilosella (HP, 3.86 mm m-2 day-1) and T. subterraneum (TS, 2.74 mm m−2 day−1). T. michelianum (TM), with 1.81 mm m-2 day-1 scored the lowest ETLEAF of all species (Table 1).Plotting LAI versus the before-mowing ET yielded a significant quadratic relationship (R2  > 0.76) (Fig. 2a) which helped to distinguish two different data clouds. Till LAI values of about 6, the model was linear, having at its lower end all GR and CR species with the inclusion of M. polymorpha (MP) as a legume, while, at the other end, M. truncatula (MT), L. corniculatus (LC) and M. lupulina (ML) were grouped together. T. michelianum (TM) was isolated from all CCs at 22.56 mm day−1.Figure 2Panel (a): quadratic regression of leaf area index (LAI, m2 m−2) vs cover crop evapotranspiration per unit of soil (ET, mm day−1). Each data point is mean value ± SE (n = 4). The quadratic model equation is y = − 0.128×2 + 2.9968x + 5.4716, R2 = 0.76. Panel (b): the quadratic regression between LAI corresponding to the clipped biomass (m2 m−2) and cover crop ET reduction (%). Each data point is mean value ± SE (n = 4). Quadratic model equation is y = − 0.8985×2 + 16.503x + 5.1491, R2 = 0.94.Full size imageWhen regressing the fraction of ET reduction, compared to pre-mowing values vs LAI (Fig. 2b), the same quadratic model achieved a very close fit (R2 = 0.94, p  1 mm) root diameters as affected by soil cover.Full size tableThe highest values of diameter class length (DCL, mm cm−3) for very fine roots (DCL_VF,  1.0 mm) roots although, most notably, L. corniculatus roots showed the highest abundance for both DCL_M (23.08 cm cm−3) and DCL_C (0.54 cm cm−3).At the 10–20 cm soil depth, GR confirmed the highest values for both very fine and fine roots, with F. arundinacea reaching maximum DCL of 2.269 and 5.215 cm cm-3, respectively (Table 2). L. corniculatus largely outscored any other species for both medium and coarse root diameter (6.173 and 0.037 cm cm−3, respectively), with F. arundinacea ranking second (3.157 and 0.016 cm cm−3, respectively).The highest root dry weight (RDW, mg cm-3) within the topsoil layer was reached by L. corniculatus (8.7 mg cm−3) and F. arundinacea (7.6 mg cm-3). Notably, such values were significantly higher than those recorded on the remaining species, except for the F. arundinacea vs F. rubra commutata comparison (Table 2). At 10–20 depth, scant variation was recorded in RDW measured in grasses, whereas L. corniculatus held its supremacy within legumes (4.5 mg cm−3). Within the creeping type, D. repens (DR) and G. hederacea (GH) scored RDW values as high as those determined for grass species (namely F. arundinacea , P. pratensis and F. rubra commutata), whereas S. subulata (SS) essentially had no root development.Soil aggregates and mean weight diameter (MWD)Table 3 reports the proportional aggregate weight (g kg−1) for both 0–10 and 10–20 cm soil depths. Compared to bare soil, the largest increase in large macroaggregates (LM,  > 2000 µm) in the top 10 cm of soil was achieved by L. corniculatus with 461 g kg−1. L. corniculatus differed from the rest of the LE group, whose grand mean (90 g kg−1) was the lowest of the three tested groups. As a legume, T. subterraneum (TS, 122 g kg−1) recorded the lowest values compared to fellow CR species, ranging between 211 (D. repens, DR) and 316 g kg−1 (G. hederacea, GH). 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Conservationist Jim Groombridge in Hawaii (standing) performing a ‘heli-hook-up’, in which a net full of equipment is hooked up to the hovering helicopter, to save it needing to land.Credit: Jim Groombridge/Maui Forest Bird Recovery Project

Since his undergraduate degree, Jim Groombridge has been part of several teams that work with critically endangered animals, including the Mauritius kestrel (Falco punctatus), which was brought back from the brink of extinction. But he has also experienced the devastation of some species being lost forever, despite all possible interventions. After receiving his PhD from Queen Mary University of London in 2000, he worked as a project coordinator at the Maui Forest Bird Recovery Project in Makawao, Hawaii. Conservation science spans many topics including climate change, working with local communities, epidemiology, genomics and designing protected areas. Projects can range from single-species conservation to ecosystem-level or landscape conservation, such as restoring whole islands. Now a professor in biodiversity conservation at the University of Kent’s Durrell Institute of Conservation and Ecology in Canterbury, UK, Groombridge teaches bachelor’s and master’s students about leadership of conservation teams and how to motivate them in the face of setbacks.What is special about leading conservation teams?Conservation field teams are slightly quirky, and those quirks can define what makes a team work well or not. One is that team leaders are rarely trained in management tasks, such as overseeing a budget, interacting with project partners and local governments, dealing with team members who feel passionate about what they do and facing the high stakes involved. Team members are enthusiastic, passionate and seldom motivated by money.Another quirk is that, in a small conservation team of four to six people, there is often a mix of skill sets and experience. You can have highly experienced specialists in a particular area, such as screening parrots for diseases, or reintroduction biology, and you might also have volunteers with only passion and enthusiasm to offer.How do you lead a team with such variable experience?Even with those different levels of expertise, you still need to meet high standards for specimen and data collection. At the moment, for example, I’m sequencing the genome of the pink pigeon (Nesoenas mayeri), using samples collected in the 1990s. There’s a sense of responsibility, especially if you’re working with species that are rare, because if you mess it up, they could go extinct. It’s not unusual to have volunteers with only two or three weeks’ worth of experience handling extremely rare samples or working with valuable data sets. Their learning curve is pretty steep. As a leader, you need to make sure that you understand the details — ranging from tasks such as collecting data and monitoring and recording invasive species to, for example, knowing how to trap a mongoose — so that you can make sure that everyone is collecting the data in the same way.

Jim Groombridge (far left), who studies biodiversity conservation at the University of Kent, UK, with one of the field crews involved in an operation to translocate a bird called the po‘ouli in Hawaii.Credit: Jim Groombridge/Maui Forest Bird Recovery Project

What do team members tend to have in common?They often share a passion for nature. They want to save the environment, they want to save a species from going extinct, they want to make a difference. That level of emotion is important. It creates an energy, which needs to be channelled proactively and positively into the project to make it a success.In 2002, for example, I was leading a team working to save a bird called the po‘ouli (Melamprosops phaeosoma) on the island of Maui, part of the Hawaiian archipelago. We were trying to translocate one of the last known birds into the range of another one to give them the opportunity to breed. There was huge excitement, but after four weeks of failing to catch the bird, there was also a lot of frustration.How do you manage a team with such strong emotions?Morale is really important. So is being able to deal with difficulties when they arise. That’s what gets small teams through tough times. With the po‘ouli, I had to make sure that the team had fun, and that people genuinely enjoyed themselves. That meant taking time out with the team in the evenings and ensuring that everyone had a bit of a laugh, so it wasn’t deadly serious all the time. Also, I made sure that team members got to perform the aspects of the job that they were good at, to increase their confidence and well-being. We eventually trapped the po‘ouli and moved it, but even though the birds were in the same territory, they didn’t breed.How do you manage expectations amid failure?I had to remind the team about the broader picture of what we had achieved. This was the first time anyone had followed the po‘ouli in the forest for ten days. I think we learnt more about the ecology of that species in that time than anyone had learnt in 30 years. We held the translocated bird for about two hours before we released it, and it took food items from us, which showed that the birds could be kept in captivity if necessary. We learnt a huge amount that could be applied to another project.
Treading carefully: saving frankincense trees in Yemen
You have to manage people’s expectations and have goals that are achievable. If you are starting a project on a species with fewer than ten individuals left in the wild, and your goal is to have thousands, that’s a difficult leap of imagination. Instead, perhaps start with finding a food that a species would eat in captivity. People need to remain connected with what’s achievable. There’s a delicate balance between being aspirational and being pragmatic.As a team member, what do you wish more conservation leaders knew?Often, there is too much emphasis placed on the command structure. Innovation in a conservation team is undersold, and easily quashed by a type of line-manager approach. The hierarchy in a team is important because people know what to do and who to report to, but you also have to encourage team members to use their initiative and ask questions. I remember when my team and I were in the cloud forests, tropical mountain regions covered by clouds for most of the year in Hawaii, we were struggling with baiting rats, which prey on eggs and fledglings of native birds. It’s one of the wettest places on Earth, and the rat poison basically turns to cottage cheese. However, one of my colleagues designed a bait box, which kept the bait dry for many weeks. When you’re working with critically endangered species and in field conditions, ingenuity is crucial.
This interview has been edited for length and clarity. More

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Species distribution modeling, spatial climate segregation and niche widthTo explore the selective regimes of the four species on external environmental factors, we first constructed species distribution modeling (SDM). We obtained a dataset including 22 environmental factors represented by climate, relief, and vegetation variables from 620 localities for DS, 1028 localities for OS, 581 localities for MM and 332 localities for PR, covering most of the species’ distribution ranges (Supplementary Fig. 1). The distribution areas of the four species overlapped widely. The contributions of environmental factors to SDMs showed similarities among the four species. The summer NDVI made important contributions for DS (41.0), OS (44.8), MM (32.5) and PR (8.1), and sand cover contributed significantly to PR (72.7) and DS (16.0) (Fig. 1c). Then, we assessed which set of environmental variables was most closely associated with species distribution via principal component analysis. The bioclimatic space occupied by the four species revealed a large overlap (Fig. 1d), which was consistent with SDM (Supplementary Fig. 1). The distribution of OS was more closely associated with higher-precipitation areas, whereas MM seemed to prefer areas with higher temperatures. Finally, we evaluated the macrohabitat niche breadth of each species. The breadths of environmental space occupation were similar for DS (0.527), MM (0.571), and PR (0.548) and slightly higher for OS (0.622), which suggests that niche selection among the four species is partially overlapping. In total, the four species are mostly similar in the selection of external environmental factors.High-quality genomic landscapes of the four desert rodentsTo investigate the genetic mechanism for desert adaptation of the four sympatric desert rodents, we generated four high-quality de novo genomes (Supplementary Fig. 2). The DS was sequenced using a combined strategy and generated 377.67 Gb of data from Illumina reads, 261.01 Gb from PacBio long reads, 299.51 Gb from 10X Genomics reads, and 389.13 Gb from Hi-C reads (Supplementary Table 1). The final genome size was 2.81 Gb with contig N50 of 31.41 Mb and ~472X mean coverage (Table 1, Supplementary Fig. 3, and Supplementary Tables 2, 3). The contigs for DS were further assembled into pseudochromosomes with lengths on the order of full chromosomes and a scaffold N50 size of 147.24 Mb (Fig. 2a, b, Table 1, and Supplementary Fig. 4). The OS, MM and PR were sequenced using the same hybrid strategy and generated 162.58 Gb, 172.22 Gb, and 214.34 Gb Illumina reads and 183.09 Gb, 161.34 Gb, and 186.45 Gb Oxford Nanopore Technologies long reads, respectively (Supplementary Table 1). The final assembly of OS, MM and PR was 2.83 Gb, 2.43 Gb, and 2.16 Gb with contig N50 of 25.87 Mb, 24.08 Mb, and 42.68 Mb, respectively (Table 1, Supplementary Fig. 4, and Supplementary Tables 2, 3).Table 1 Genome assembly statistics of the four desert rodents.Full size tableFig. 2: High-quality assembly of Dipus sagitta genome and genomic elements of the four sequenced desert rodents.a Hi-C heat map of Dipus sagitta genome assembly. b CIRCOS plot showing the distribution of GC content, transposable elements (TE), and coding sequences (CDS) in the D. sagitta genome. c Orthologous coding sequences composition inferred for thirteen rodents’ genomes. Mcar Mus caroli, Mmus Mus musculus, Mpah Mus Pahari, Mmer Meriones meridianus, Mung Meriones unguiculatus, Cgri Cricetulus griseus, Prob Phodopus roborovskii, Sgal Spalax galili, Osib Orientallactaga sibirica, Dsag Dipus sagitta, Jjac Jaculus jaculus, Hgla Heterocephalus glaber, Cpor Cavia porcellus. d Proportion of transposable elements (TEs). The barplots show the proportions of different types of TEs in corresponding species on the phylogenetic tree.Full size imageAnalyses of the four draft genomes showed that 92.9–95.9% of mammalian BUSCOs were complete, and the GC content was 41.38–42.16% (Table 1 and Supplementary Table 3). Whole-genome annotation was performed via three complementary methods: ab initio prediction, homology-based prediction and RNA-seq based prediction. A total of 23,482, 22,859, 22,533, and 22,314 protein-coding genes were annotated for DS, OS, MM, and PR, respectively (Fig. 2c, Supplementary Fig. 5, Supplementary Table 4). Approximately 98.8–99.1% of genes were functionally annotated for the four species (Supplementary Table 4). Transposable elements (TEs) accounted for 31.38–53.02% of genome assemblies, which predominantly consisted of long-terminal repeats (LTRs), long interspersed nuclear elements (LINEs) and other unknown TEs (Fig. 2d). DS and OS displayed significant LTR expansion of 47.39% and 50.88% in four sequenced genomes, while MM showed an unexpectedly high LINE expansion of 28.99% and sharp LTR contraction to 9.38% (Supplementary Table 5).Phylogenetic relationship and evolutionary historyUsing 5,102 single-copy orthologous groups, we constructed a high-confidence phylogenetic tree using the maximum-likelihood algorithm, including time calibrations based on fossil records and previous studies (Figs. 1b, 2c)22. The phylogenetic tree strongly supported nodes uniting the subfamilies Murinae and Gerbillinae, which together represented the family Muridae (Supplementary Fig. 6). This group was sister to a clade containing cricetids. Spalacidae was recovered as the earliest divergent lineage from Muridae and Cricetidae in the superfamily Muroidea. The split of the most recent common ancestor of Dipodoidea and Muroidea dated to ~56.5 Mya (Fig. 1b, Supplementary Fig. 7). In the Miocene epoch (23 Mya–5.3 Mya), accelerated global geotectonic movement aggravated global climate drying and cooling23. Geological disruptions that modified landscapes and offered new habitats favored the early adaptive radiation of extant desert rodents. The ancestors of four sequenced species emerged separately during this period (Supplementary Note 1). Our phylogenetic tree is consistent with previous evolutionary research on rodents22 and supports the independent evolution of desert adaptations in Jerboas, Gerbils and Hamsters.Expanded and contracted gene familiesComparative genomic analysis revealed 23/32, 4/22, 39/73, and 22/83 gene families exhibiting significant expansion/contraction in the genomes of DS, OS, MM, and PR, respectively (Fig. 1b and Supplementary Fig. 8). Genes belonging to the expanded/contracted families were functionally enriched (Fisher Exact  More

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