Philippa Kaur

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The management of introduced species, whether kudzu or zebra mussels, is costly and complex. Now, a paper reports a workable, effective solution that harnesses network analyses of ecological phenomena.Invasive species can pose severe economic and environmental problems, costing more than US$1 trillion worldwide since 1970 (ref. 1). Yet managing this human-driven issue is difficult in itself. The regions involved can be vast — entire continents or countries, for instance — while budgets are typically limited. As well, the sites potentially affected and management options can be numerous. Real systems (for example, all the lakes in the United States) can have thousands of locations that could potentially be infested. By contrast, considering just 40 locations means dealing theoretically with over 1 trillion unique combinations (240) where management could be applied (for instance, to reduce the number of invasive species leaving infested areas or entering uninfested ones). Given these constraints, a key problem is how and where to deploy control measures such as invasive-species removal. While sophisticated optimization approaches exist2, which use mathematical rules to exclude most suboptimal combinations and quickly zoom in to which locations should be managed to minimize new invasions, these algorithms are generally unfeasible for very large systems. Now, writing in Nature Sustainability, Ashander et al.3 demonstrate that simpler network metrics revealing linkages between patches can provide solutions that are often comparable to the more complex optimization algorithms. More

Site descriptionThe experiment was conducted at the Beef Cattle Research Center of the Institute of Animal Science/APTA/SAA, Sertãozinho, São Paulo, Brazil (21°08′16″ S e 47°59′25″ W, average altitude 548 m), during two consecutive years. The climate in this region is Aw according to the Köppen’s classification, characterized as humid tropical, with a rainy season during summer and drought during winter. The meteorological data is reported in Fig. 1. The soil in the experimental area is classified as an Oxisol42. Before the experiment, soil samples were collected for chemical characterization (Table 4), which was performed following the methodology described in Van Raij et al.43. Samples were collected in 18 experimental paddocks, at the depths of 0- to 10- and 10- to 20-cm layers, from 10 distinct sampling points in each paddock, in order to create one composite sample per unit, totaling 36 samples analyzed.Figure 1Meteorological data during the study period, obtained from the meteorological station located at Centro de Pesquisa de Bovinos de Corte, Instituto de Zootecnia/Agência Paulista de Tecnologia dos Agronegócios (APTA)/Secretaria de Agricultura e Abastecimento de São Paulo (SAA), Sertãozinho, São Paulo, Brazil.Full size imageTable 4 Chemical attributes of the soil in the experimental area, before installing the experiment (November 2015).Full size tableThe nitrogen total (Nt) content was determined by the micro-Kjeldahl method44, and the soil nitrogen stocks (SN) were calculated using the following equation below, according to Veldkamp et al.45.$${text{SN }}left[ {{text{Mg ha}}^{ – 1} {text{ at a given depth}}} right], = ,({text{concentration }} times {text{ BD}}, times ,{1}/{1}0),$$ where concentration refers to the Nt concentration at a given depth (g kg−1), BD is the bulk density at a certain depth (average 1.24 kg dm−3), and 1 is the layer thickness (cm).Description of treatments and managementsThe experiment was carried out in a 16-ha area, divided into 18 paddocks of 0.89 ha each (Fig. 2), organized in a randomized blocks design with three replicates and six treatments, namely conventional crop system with grain maize production (CROP), conventional livestock system with beef cattle production in pasture using Marandu grass (LS), and four ICLS for the production of intercropped maize grain with beef cattle pasture. All production systems were sowed in December 2015, under a no-tillage system. The fertilization recommendations in the systems were based on the recommendation presented in the Boletim 10046.Figure 2Localization and representation of the area of the experiment carried out in the study. Google Earth version Pro was used to construct the map (http://www.google.com/earth/index.html).Full size imageIn the CROP system, the maize Pioneer P2830H was cultivated, sowed in a spacing of 75 cm and sowing density of 70 thousand plants. Applications of 32 kg ha−1 of nitrogen (urea), 112 kg ha−1 of P2O5 (single superphosphate) and 64 kg ha−1 of KCl (potassium chloride) were performed. Complementarily, a topdressing fertilization was made using 80 kg ha−1 of nitrogen (urea) and 80 kg ha−1 of KCl. Sowing was carried out for two consecutive years (December 2015 and 2016), providing two harvests of maize grains (May 2016 and 2017), and between one harvest and the other, the soil remained in fallow without any cover crop. The total amount of fertilizer applied in two years was 224 kg ha−1 of nitrogen (urea), 224 kg ha−1 of P2O5 (single superphosphate) and 288 kg ha−1 of KCl (potassium chloride).For the LS treatment, Urochloa brizantha (Hoechst. ex A. Rich) R.D. Webster cv. Marandu (syn. Brachiaria brizantha cv. Marandu) was sowed in a spacing of 37.5 cm, with a density of 5 kg ha−1 of seeds (76% of crop value) for the pasture assemblage. Marandu grass seeds were mixed with the planting fertilizer, applying 32 kg ha−1 of nitrogen (urea), 112 kg ha−1 of P2O5 (as single superphosphate) and 64 kg ha−1 of KCl. Applications of 40 kg ha−1 of nitrogen, 10 kg ha−1 of P2O5 and 40 kg ha−1 of KCl were also performed as topdressing fertilization in October 2016 and March 2017. 90 days after sowing, the pasture was ready to be grazed (March 2016). Three grazing periods were carried out in continuous stocking systems, with the first period between March and April 2016, the second period between August and October 2016 and the third between November 2016 and December 2017. The total amount for 2 years was 112 kg ha−1 of nitrogen (urea), 132 kg ha−1 of P2O5 (single superphosphate) and 144 kg ha−1 of KCl (potassium chloride).The same cultivar, spacing, sowing density and fertilization rates described in the CROP treatment were used in all ICLS, as well as the same density of Marandu grass seeds and topdressing fertilization adopted in the pasture of the LS treatment. The total amount for two years was 192 kg ha−1 of nitrogen (urea), 132 kg ha−1 of P2O5 (single superphosphate) and 224 kg ha−1 of KCl (potassium chloride). In ICLS-1, Marandu grass was sowed in lines simultaneously with maize, while in ICLS-2, the sowing was also simultaneous, but the application of an under-dose of 200 mL of the herbicide Nicosulfuron was used, 20 days after seedlings emergence. In the ICLS-3, Marandu grass seeds were sown the time of topdressing fertilization of maize, thus the grass seeds were mixed with the fertilizer, and sowing was carried out in the interlines of maize, using a minimum cultivator. In ICLS-4, the sowing of Marandu grass was performed simultaneously with maize, but the grass seeds were sowed in both rows and inter-rows of maize, resulting in a spacing of 37.5 cm. In this treatment, the application of 200 mL of the herbicide Nicosulfuron was adopted, 20 days after seedlings emergence.In all ICLS treatments, maize harvest was carried out in May 2016. Ninety days after harvesting the plants, the pastures were ready to be grazed. Therefore, two grazing periods were made in continuous stocking, being the first period between August and October 2016 and the second period between November 2016 and December 2017. The method for animal stocking in treatments LS and ICLS was continuous with a stocking rate (put and take) being defined according to Mott47. Caracu beef cattle with 14 months of age were used at the beginning of the experiment, with an average body weight of 335 ± 30 kg.Estimations of the nutrient balance (NB) and nutrient use efficiency (NUE)In this study, the inputs and outputs of N were assessed at the farm level48,49. The NB was calculated by the equation below19,45,50.$${text{NB}}_{{text{N}}} = {text{ Input}}_{{text{N}}} {-}{text{ Output}}_{{text{N}}}$$As for the NUE, this parameter was evaluated as defined by the EU Nitrogen Expert Panel51, being calculated as the ratio between outputs and inputs of nitrogen.$${text{NUE}}_{{text{N}}} = , left[ {{text{Output}}_{{text{N}}} /{text{ Input}}_{{text{N}}} } right]$$where NB is the nutrient balance, N is nitrogen, Input is the N concentration in the mineral fertilizer (urea), Output is the nitrogen concentration in export (maize grain and animal tissue), and NUE is the use efficiency of the nutrient.The amount of N exported in maize grains, the grain production results (Table 2) were multiplied by the mean value of N, consulted in Crampton and Harris52.In order to estimate the amounts of nutrient exported by the animals in their tissues, the values of live weight gain were considered [kg ha-1 of live weight (PV)] (Table 2), as well as the nitrogen values of the tissue, according to the methodology proposed by Rasmussen et al.21. Those authors reported that for animals weighting less than 452 kg/PV, it represents 2.7%, while heavier animals have a 2.4% nitrogen content representation of their body weight.The inputs and outputs of N in each production system are represented in Figs. 3, 4 and 5. Biological N fixation, atmospheric deposition, denitrification, leaching, rainfall, and volatilization and absorption of ammonia were not considered in the calculation of NB.Figure 3Representation of inputs and outputs of nitrogen and organic residues generated in the crop system.Full size imageFigure 4Representation of inputs and outputs of nitrogen and organic residues generated in the livestock system.Full size imageFigure 5Representation of inputs and outputs of nitrogen and organic residues generated in the integrated systems.Full size imageData for animal tissue, animal excreta, and N concentration in grains were obtained from key manuscripts from the scientific literature in order to estimate the N balance.Calculation of nitrogen quantity and valuation of organic residuesThe amount of N in the organic residues was determined as a function of the system (Figs. 3, 4, 5). The residue considered in the CROP was the straw derived from maize, while for LS it was the litter deposited (LD) in the grass Marandu, and animal manure (feces and urine). The ICLS were considered as the straw, LD, and animal manure.The N concentration in straw and LD was determined following the methods of AOAC (1990). Straw was sampled immediately after maize grain harvest, using a 1-m2 frame in the field. The material was collected in two spots of the plot that were chosen randomly. All straw deposited on the soil was sampled, weighted and dried in an oven with air circulation (60 °C) until constant weight, for the determination of dry matter in kg of straw per hectare (Table 2). The LD in the pasture system (Table 2) was analyzed according to Rezende et al.53.In order to estimate the daily amount of excreta, we considered the stocking rate adopted in the experiment (Table 2) and the values proposed by Haynes and Williams54. According to those authors, adult beef cattle can defecate on average 13 times a day and urinate 10 times a day, totaling a daily amount of 28.35 kg of feces and 19 L of urine.The valuation was calculated based on the mean value of urea for the last 10 years in the fertilizer market55,56,57, namely $0.28 kg−1 ha−1 of urea, and considering the loss of nitrogen by volatilization, which according to Freney et al.58 and Subair et al.59 can reach up to 28%.Statistical analysisThe experiment was assembled in a randomized blocks design. The model adopted for the analysis of all response variables included the block’s and treatments fixed effects (3 blocks and 6 treatments), in addition to the random error. Statistical analysis were carried out by the function “dbc()” of the package “ExpDes.pt” of the software R Development Core Team60, and the mean values were compared by the Tukey’s test at a 5% probability level. More

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Climate and land cover dataOur study of tropical dry season dynamics required climatic variables with high temporal resolution (i.e., daily) and full coverage of tropic regions. To reduce uncertainties associated with the choice of precipitation (P) and evapotranspiration (Ep or E) datasets, we used an ensemble of eight precipitation products, three reanalysis-based products for Ep, and one satellite-based land E product. These precipitation datasets were derived four gauge-based or satellite observation (CHIRPS58, GPCC59, CPC-U60 and PERSIANN-CDR61), three reanalyses (ERA-562, MERRA-263, and PGF64) and a multi-source weighted ensemble product (MSWEP v2.865). The potential evapotranspiration (Ep) was calculated using the FAO Penman–Monteith equation66 (Eqs. (1, 2)), which requires meteorological inputs of wind speed, net radiation, air temperature, specific humidity, and surface pressure. We derived these meteorological variables from the three reanalysis products (ERA-5, MERRA-2, and GLDAS-2.067). Since PGF reanalysis lacked upward short- and long-wave radiation output and thus net radiation, we used available meteorological outputs from GLDAS-2.0 instead, which was forced entirely with the PGF input data.$${Ep}=frac{0.408cdot triangle cdot left({R}_{n}-Gright)+gamma cdot frac{900}{T+273}cdot {u}_{2}cdot left({e}_{s}-{e}_{a}right)}{triangle +{{{{{rm{gamma }}}}}}cdot left(1+0.34cdot {u}_{2}right)}$$
(1)
$${VPD}={e}_{s}-{e}_{a}=0.6108cdot {e}^{frac{17.27cdot T}{T+237.3}}cdot left(1-frac{{RH}}{100}right)$$
(2)
Where Ep is the potential evapotranspiration (mm day−1). Rn is net radiation at the surface (MJ m−2 day−1), T is mean daily air temperature at 2 m height (°C), ({u}_{2}) is wind speed at 2 m height (m s−1), ((,{e}_{s}-{e}_{a})) is the vapor pressure deficit of the air (kPa), ({RH}) is the relative air humidity near surface (%), ∆ is the slope of the saturation vapor pressure-temperature relationship (kPa °C−1), γ is the psychrometric constant (kPa °C−1), G is the soil heat flux (MJ m−2 day−1, is often ignored for daily time steps G ≈ 0).We derived the daily evapotranspiration data from the Global Land Evaporation Amsterdam Model (GLEAM v3.3a68), which is a set of algorithms dedicated to developing terrestrial evaporation and root-zone soil moisture data. GLEAM fully assimilated the satellite-based soil moisture estimates from ESA CCI, microwave L-band vegetation optical depth (VOD), reanalysis-based temperature and radiation, and multi-source precipitation forcings. The direct assimilation of observed soil moisture allowed us to detect true soil moisture dynamic and its impacts on evapotranspiration. Besides, the incorporation of VOD, which is closely linked to vegetation water content69,70, allowed us to detect the effect of water stress, heat stress, and vegetation phenological constraints on evaporation. Other observation-driven ET products from remote-sensing physical estimation and flux-tower are not included due to their low temporal resolution (i.e., monthly)71 or short duration72,73. ET outputs of reanalysis products are not considered in our analysis, because the assimilation systems lack explicit representation of inter-annual variability of vegetation activities and thus may not fully capture hydrological response to vegetation changes62,63,67.We used land cover maps for the year 2001 from the Moderate-Resolution Imaging Spectroradiometer (MODIS, MCD12C1 C574) based on the IGBP classification scheme to exclude water-dominated and sparely-vegetated pixels (like Sahara, Arabian Peninsula). All climate and land cover datasets mentioned above were remapped to a common 0.25° × 0.25° grid and unified to daily resolution. The main characteristics of the datasets mentioned above are summarized in Supplementary Table 1.Outputs of CMIP6 simulationsTo understand how modeled dry season changes compare with observed changes, we analyzed outputs from the “historical” (1983-2014) runs of 34 coupled models participating in the 6th Coupled Model Inter-comparison Project75 (CMIP6, Supplementary Table 3). We used these models because they offered daily outputs of all climatic variables needed for our analysis, including precipitation, latent heat (convert to E), and multiple meteorological variables for Ep (air temperature, surface specific humidity, wind speed, and net radiation). All outputs were remapped to a common 1.0° × 1.0° grid and unified to daily resolution.Defining dry season length and timingFor each grid cell and each dry season definition (P  More

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