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    Eco-environmental assessment model of the mining area in Gongyi, China

    Technical criterion for ecosystem status evaluationOn March 3, 2015, the Ministry of Ecology and Environment of the People’s Republic of China approved the “Technical Criterion for Ecosystem Status Evaluation” as the national environmental protection standard. This standard is based on the former standards released in 2006, and 48 relevant documents from 2006 to 2012 were searched to propose new standards and factor weights based on actual utilization effects and expert guidance. The eco-environment assessment uses a comprehensive index (eco-environmental status index, EI) to reflect the overall state of the regional eco-environment. The indicator system includes the biological abundance index, vegetation coverage index, river density index, land stress index, and pollution loading index. These indexes reflect the abundance of organisms in the evaluated area, the level of vegetation coverage, the abundance of water, the intensity of land stress, and the extent of the pollution load. Each indicator was calculated according to its weight to obtain an eco-environment assessment map (Table 1). All parameters involved in the calculation are derived from this standard.Table 1 Weights of the evaluation indicators.Full size tableThe calculation of the eco-environment status is as follows:$$begin{aligned} EI & = 0.35*biological ; abundance ; index + 0.25*vegetation ; coverage ; index hfill \ & quad + 0.15*river ; density ; index + 0.15*(1 – land ; stress ; index) hfill \ & quad + 0.1*(1 – pollution ; loading ; index) hfill \ end{aligned}$$
    (9)
    Biological abundance indexThe biological abundance index refers to the number of certain organisms in this area. The calculation method is as follows:$$Biological , abundance , index , = , left( {BI , + , HQ} right)/2$$
    (10)
    In this formula, BI is the biodiversity index and HQ is the habitat quality index. When the biodiversity index does not have dynamic data updates, the change in the biological abundance index is equal to the change in the HQ.Biodiversity is a general term for the complexity of species and their genetic variation and ecosystems in space over time. Biodiversity plays an important role in maintaining soil fertility, ensuring water quality, regulating the climate, stabilizing the environment, and maintaining ecological balance.The BI method is as follows:$$BI = NPP_{mean} *F_{pre} *F_{tem} *(1 – F_{alt} )$$
    (11)
    NPPmean is the net primary productivity. Fpre is the annual average precipitation. Ftem is the temperature parameter. Falt is the altitude parameter.NPP refers to the amount of organic matter accumulated per unit area and unit time of green plants. NPP is the remainder of the total amount of organic matter produced by photosynthesis after deducting autotrophic respiration and is usually expressed as dry weight. In this study, the estimation of NPP was based on the absorbed photosynthetically active radiation (APAR) and actual light-use efficiency (LUE) (ε) of the CASA ecosystem model40. The CASA model is a process-based remote sensing model that couples ecosystem productivity and soil carbon and nitrogen fluxes, driven by gridded global climate, radiation, soil, and remote sensing vegetation index datasets41. The model can be expressed generally as follows:$$NPP(x,t) = APAR(x,t)*varepsilon (x,t)$$
    (12)
    The entire study area is divided into 11,303 pixels on a 30 * 30 m grid. x indicates the location of each pixel, and t indicates time; the data were collected once a month. APAR(x,t) represents the photosynthetically active radiation absorbed by pixel x in that month (gC * m−2* month−1). Ɛ(x, t) is LUE (gC * MJ−1) of the vegetation42.Estimation of the fraction of APAR using RS data is based on the reflection characteristics of the vegetation on the infrared and near-infrared bands. The value of APAR is determined by the effective radiation of the sun and the absorption ratio of the vegetation to the effective photosynthetic radiation. The formula is as follows:$$APAR(x,t) = SOL(x,t)*FPAR(x,t)*0.5$$
    (13)

    where SOL(x,t) represents the total amount of solar radiation at pixel x in month t, FPAR(x,t) represents the absorption ratio of the vegetation layer to the incident photosynthetically active radiation, and a constant of 0.5 indicates the ratio of the effective solar radiation that can be utilized by the vegetation to the total solar radiation.Since there is a linear relationship between FPAR and NDVI within a certain range, this relationship can be determined according to the maximum and minimum values of a certain vegetation type NDVI and the corresponding FPAR maximum and minimum values.$$FPAR(x,t) = frac{{(NDVI(x,t) – NDVI_{i,min } )}}{{(NDVI_{i,max } – NDVI_{i,min } )}}*(FPAR_{max } – FPAR_{min } ) + FPAR_{min }$$
    (14)

    where NDVImax and NDVImin correspond to the NDVI maximum and minimum values of the ith planting type, respectively. There is also a good linear relationship between FPAR and the simple ratio index (SR) of vegetation, which is represented by the following formula:$$FPAR(x,t) = frac{{(SR(x,t) – SR_{i,min } )}}{{(SR_{i,max } – SR_{i,min } )}}*(FPAR_{max } – FPAR_{min } ) + FPAR_{min }$$
    (15)

    where the values of FPARmin and FPARmax are independent of vegetation type and are 0.001 and 0.95, respectively; SRi,max and SRi,min correspond to the 95% and 5% percentiles, respectively, of the ith NDVI. SR(x,t) is represented by the following formula:$$SR(x,t) = frac{1 + NDVI(x,t)}{{1 – NDVI(x,t)}}$$
    (16)
    A comparison of the estimated results of FPAR-NDVI and FPAR-SR shows that the FPAR estimated by NDVI is higher than the measured value, while the FPAR estimated by SR is lower than the measured value, but the error is less than that estimated directly by NDVI. As a result, these two values can be combined, and their weighted average value is taken as an estimate of the estimated FPAR, while ɑ means weight:$$FPAR(x,t) = alpha FPAR_{NDVI} + (1 – alpha )FPAR_{SR}$$
    (17)
    Light use efficiency (LUE) refers to the ratio of chemical energy contained in organic dry matter produced per unit area over a certain period of time to the photosynthetically active radiation absorbed by plants projected onto the same area at the same time. Different vegetation types and the same types of vegetation have different light energy utilization rates in different living environments43. The differences are mainly due to the characteristics of the vegetation itself, temperature, moisture, and soil44. Vegetation has the highest utilization rate of light energy under ideal conditions, but the maximum light energy utilization rate in the real environment is mainly affected by temperature and moisture, which can be expressed as follows:$$varepsilon left( {x,t} right) = T_{varepsilon 1} left( {x,t} right) cdot T_{varepsilon 2} left( {x,t} right) cdot W_{varepsilon } left( {x,t} right) cdot varepsilon_{max }$$
    (18)
    where Tε1(x,t) and Tε2(x,t) represent the stress effects of low temperature and high temperature on light energy utilization, respectively, Wε(x,t) is the effect of water stress on the maximum light energy utilization under ideal conditions, and εmax is the maximum light energy utilization under ideal conditions (gC * MJ−1). The maximum solar energy utilization rate εmax varies depending on the vegetation type. In this study, the maximum light energy utilization rate of different land use types simulated by an improved Carnegie-Ames-Stanford Approach (CASA) model is used as the input parameter of light energy utilization in the CASA model (Table 2). The monthly maximum light energy utilization rate is determined in three steps: first, calculate the APAR, temperature, and water stress factors of all pixels; then, select the NPP measured data of the same time period in the study area; finally, simulate the εmax of vegetation according to the principle of minimum error45. Figure 5 shows the calculation process of NPP. The weight of each habitat type in the HQ is shown in Table 3. The weight value is derived from the official document30. To facilitate the calculation, this paper normalizes the calculation results from 0 to 1 (Fig. 6a).Table 2 Maximum LUE rates of different land use types.Full size tableFigure 5NPP calculation process.Full size imageTable 3 Weight of each habitat type in the HQ.Full size tableVegetation coverage indexThe vegetation coverage index was obtained from the NDVI, which is a simple, effective, and empirical measure of surface vegetation status. The vegetation index mainly describes the difference between the reflection of vegetation in the visible and near-infrared bands and the soil background. This index also reduces the solar elevation angle and noise caused by the atmosphere and is thus the most widely used and effective calculation method. Each vegetation index can be used to quantitatively describe the growth of vegetation under certain conditions. The expression is as follows:$$NDVI = frac{NIR – R}{{NIR + R}}$$
    (19)

    where NIR and R are reflectance values in the near-infrared and red bands, respectively.NDVI values are obtained by processing the RS images of the Landsat 8 satellite. This satellite is equipped with an operational land imager (OLI) that includes nine bands with a spatial resolution of 30 m, including a 15-m panchromatic band. To facilitate the calculation, this paper normalizes the calculation results from 0 to 1 (Fig. 6b).Figure 6Eco-environment assessment indexes and evaluation rating map (the first quarter was used as an example). (a) Biological abundance index; (b) vegetation coverage index; (c) river density index; (d) land stress index; (e) pollution loading index; and (f) environmental status classification. The Figure is created using ArcGIS ver.10.3 (https://www.esri.com/).Full size imageRiver density indexThe river density index refers to the total length of rivers, lakes, and water resources in the assessed area as a percentage of the assessed area, which is used to reflect the abundance of water in the assessed area and is calculated as follows:$$begin{gathered} River ; density ; index = (A_{riv} *river ; length / area + A_{lak} *water ; area/area hfill \ + A_{res} {*}amount ; of ; resources/area , )/3 hfill \ end{gathered}$$
    (20)

    where Ariv is the normalization coefficient of river length, with a reference value of 84.3704, Alak is the normalization coefficient of the lake area, with a reference value of 591.7909, and Ares is the normalization coefficient of water resources, with a reference value of 86.387. Finally, the calculation results were normalized from 0 to 1 (Fig. 6c).Land stress indexThe land stress index is the degree to which the land quality in the assessment area is under stress. The weight of the land stress index evaluation is shown in Table 4.Table 4 Weight of the land stress index evaluation.Full size tableThe calculation method is as follows:$$begin{gathered} Land ; stress ; index = A_{ero} *(0.4*severe ; erosion ; area + 0.2*{text{mod}}erate ; erosion ; area hfill \ + 0.2*construction ; land ; area + 0.2*other ; land ; stress ; area)/area hfill \ end{gathered}$$
    (21)

    where Aero is the normalization coefficient of the land stress index, with a reference value of 236.0436. According to the “Classification criteria for soil erosion”46, the influencing factors of soil erosion, vegetation, soil texture, landform, and precipitation are ranked according to importance. In the calculation of the land stress index, all the land is divided into three categories, in which the weight of severe erosion is 0.4, the weight of non-erosion is 0, and the other erosion types such as moderate erosion and construction land are 0.2. The areas with severe erosion include vegetation coverage less than 30% and areas of soil erosion greater than 3.7 mm/a due to human activities. These areas are generally developed on highly erosive-sensitive soils. Cinnamon soil and loess soil in the study area are highly erosive-sensitive soils. Therefore, the industrial and mining areas of cinnamon and loess soil types are regarded as severe erosion areas. Areas with vegetation coverage greater than 50% are non-eroded areas, so water bodies and woodlands are divided into non-erodible areas. All areas except these two types have a weight of 0.2. Finally, the calculation results were normalized from 0 to 1 (Fig. 6d).Pollution loading indexThe pollution loading index refers to the load of pollutants in a certain area or an environmental element. In this study, the AQI was used to calculate the pollution loading index, and the results were normalized from 0 to 1 (Fig. 6e).The eco-environmental evaluation score was calculated based on the national environmental protection standard according to the weight of each indicator (Fig. 6f).Improved evaluation system and intelligent evaluation modelImproved evaluation systemConsidering that the evaluation factors in the national environmental protection standards are applicable to ordinary areas, areas affected by mines should have more evaluation factors than those in the standards. Thus, an improved evaluation system was proposed. The improved evaluation system has added factors that affect the environment of the mine based on the factors of the original system. The impact of the mine on the environment is reflected in the pollution of the atmosphere, such as dust from open pits and industrial waste from concentrators; the occupation of land by solid waste, such as ore piles and coal piles; soil pollution, such as the diffusion of heavy metals from coal piles, coal mine concentrator plants, and mines; and the increased likelihood of geological disasters, such as collapse caused by underground mining, spontaneous coal combustion and landslides caused by open-pit mining surfaces. Therefore, the improved evaluation system adds an air pollution range, a solid waste area, a geologic hazard range, and a metallic and non-metallic mine soil pollution buffer to the national environmental protection standards.The area of air pollution in mining areas is generally near open pits and concentrator plants. Therefore, the air pollution range was selected within 50 m around the open pits and the concentrator plants. Due to the dilution and dispersion of the air itself, an estimate of the pollution is the reciprocal of the wind speed (Fig. 7a).Figure 7New factors in the improved evaluation system. (a) Air pollution range; (b) solid waste area; (c) geological hazard range; (d1) non-metallic mine soil pollution buffer; and (d2) metal mine soil pollution buffer. The Figure is created using ArcGIS ver.10.3 (https://www.esri.com/).Full size imageMine solid waste pollution includes a large amount of waste rock from open-pit mining and pit mining, coal gangue produced by coal mining, tailings from beneficiation and slag from smelting. These solid wastes are generally piled up near the mining area. They not only occupy large areas of land and induce geological disasters such as landslides and mudslides but also cause chemical pollution, spontaneous combustion, and radiation from radioactive materials due to long-term stacking. This may affect the health and safety of humans and other biological organisms. The scope of the solid waste area is determined by the ore piles, coal piles, and dumping sites (Fig. 7b).Mine geological disasters are caused by a large number of mining wells and rock and soil deformation, as well as serious changes in the geological, hydrogeological, and natural environments of the mining area, endangering human life and property and destroying mining engineering equipment and mining resources. In this study, the geologic hazard range consists of areas with underground mining stopes and coal piles (Fig. 7c).After the pollutants generated by the mining operation enter the soil, physical and mechanical absorption, retention, colloidal physicochemical adsorption, chemical precipitation, bioabsorption, etc. of the suspended pollutants through the soil continue to accumulate in the upper soil. When pollutants reach a certain maximum, they cause deterioration of the soil composition, cycle, properties, and functions and begin to accumulate in plants, which affects the normal growth and development of plants, decreases crop yield and quality, and ultimately affects human health. Metallic and non-metallic minerals have different effects on soil pollution. The pollution of soil by non-metallic minerals mainly occurs in coal mines and coal piles, and the buffer zone is centred on the coal mines and coal piles. Coal production activities can cause heavy metals in coal piles to enter the soil and cause pollution. Due to different types of heavy metals, the range of soil contamination is different47. Combining the non-metallic mineral industrial squares and coal mine-based non-metallic minerals around the heavy metal soil pollution range, the buffers are graded at 30, 200, and 1000 m (Fig. 7d1)43. The metal mines in Gongyi are mainly aluminium ore and iron ore. Referencing the spread range of heavy metal pollution in the soil of aluminium ore and iron ore mines48,49,50,51, the buffers are graded at 50, 100, 300, and 500 m (Fig. 7d2).The four new elements in the improved evaluation system are normalized from 0 to 1 during the calculation.Intelligent evaluation modelArtificial neural networks, decision trees, and SVMs were calculated using IBM SPSS Modeler software to find an intelligent model suitable for environmental assessment of the mine in the study area. Then, several models with high evaluation accuracy were selected. The SVM, CART, and C5.0 models were chosen for further comparison. The sampling points were selected randomly; 700 sampling points were selected from the area away from the mining area; 100 sampling points were selected from the mining area after random sampling by mine type, and these points were used as training samples. Non-mining evaluation scores were based on the national environmental protection standard, while the mining area scores were based on field investigation. In the field investigation, preliminary scoring of the sampling points was conducted according to mining type, mining intensity, air quality, and surrounding environment. Then, a photo of the field was taken at every sampling point in the mine, and experts were invited to further score the area according to the photo. This score is the relative score obtained by referencing the national environmental protection standard.The index layers of the training samples were used as input, and the scores were used as the output to train the machine learning models. The trained models were applied to the entire study area, and all points except the training sample points were used for verification. After further comparison with SVM, CART, and C5.0, the evaluation accuracy rates of the three methods in the mining area and non-mining area were obtained. In the non-mining area, the model evaluation results of various land use types were compared with the national environmental protection standards. The accuracy in various land use types is shown in Table 5. In the mining area, the model evaluation score is compared with the score from the experts, and the obtained accuracy table is shown in Table 6.Table 5 Accuracy of each algorithm in various land use types in non-mining areas.Full size tableTable 6 Accuracy of each algorithm in the mining area.Full size tableIn non-mining areas, the accuracy of the SVM model is significantly better than that of the other two methods. However, in the mining area, the accuracy of the CART model is higher. Therefore, the SVM model was used to evaluate the area away from the mine, and the CART model was used to evaluate the mining area. The evaluation results of these two models were combined to obtain the evaluation map of the entire study area. More

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    Modelling dynamic ecosystem services

    1.Pan, Y. et al. Science 333, 988–993 (2011).CAS 
    Article 

    Google Scholar 
    2.Vanhaven, H. et al. (eds) Making Boreal Forests Work for People and Nature (IUFRO, 2012); https://www.iufro.org3.Stokland, J. N. For. Ecol. Manage. 488, 119017 (2021).Article 

    Google Scholar 
    4.Snäll et al. Nat. Sustain. https://doi.org/10.1038/s41893-021-00764-w (2021).5.Assessment Report on Land Degradation and Restoration (IPBES, 2018).6.Felipe-Lucia, M. R. et al. Nat. Commun. 9, 4839 (2018).Article 

    Google Scholar 
    7.Gamfeldt, L. et al. Nat. Commun. 4, 1340 (2013).Article 

    Google Scholar 
    8.Holland, R. A. et al. Biodivers. Conserv. 20, 3285–3294 (2011).Article 

    Google Scholar 
    9.National Forest Inventory: National Forest Monitoring (FAO, 2021); http://www.fao.org/national-forest-monitoring/areas-of-work/nfi/en/10.Simons, N. K. et al. For. Ecosyst. 8, 5 (2021).Article 

    Google Scholar 
    11.Sweden’s Forest Industry in Brief (FIS, 2021).12.Triviño, M. et al. J. Appl. Ecol. 54, 61–70 (2017).Article 

    Google Scholar  More

  • in

    Global topographic uplift has elevated speciation in mammals and birds over the last 3 million years

    1.von Humboldt, A. Ansichten der Natur mit Wissenschaftlichen Erlauterungen (J.G. Cotta, 1808).2.Perrigo, A., Hoorn, C. & Antonelli, A. Why mountains matter for biodiversity. J. Biogeogr. 47, 315–325 (2020).Article 

    Google Scholar 
    3.Badgley, C. et al. Biodiversity and topographic complexity: modern and geohistorical perspectives. Trends Ecol. Evol. 32, 211–226 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    4.Rahbek, C. et al. Building mountain biodiversity: geological and evolutionary processes. Science 365, 1114–1119 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Steinbauer, M. J. et al. Topography-driven isolation, speciation and a global increase of endemism with elevation. Glob. Ecol. Biogeogr. 25, 1097–1107 (2016).Article 

    Google Scholar 
    6.Fjeldså, J., Bowie, R. C. K. & Rahbek, C. The role of mountain ranges in the diversification of birds. Annu. Rev. Ecol. Evol. Syst. 43, 249–265 (2012).Article 

    Google Scholar 
    7.Hughes, C. & Eastwood, R. Island radiation on a continental scale: exceptional rates of plant diversification after uplift of the Andes. Proc. Natl Acad. Sci. USA 103, 10334–10339 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Antonelli, A. et al. Geological and climatic influences on mountain biodiversity. Nat. Geosci. 11, 718–725 (2018).CAS 
    Article 

    Google Scholar 
    9.Quintero, I. & Jetz, W. Global elevational diversity and diversification of birds. Nature 555, 246–250 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Gillooly, J. F., Allen, A. P., West, G. B. & Brown, J. H. The rate of DNA evolution: effects of body size and temperature on the molecular clock. Proc. Natl Acad. Sci. USA 102, 140–145 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Martin, A. P. & Palumbi, S. R. Body size, metabolic rate, generation time, and the molecular clock. Proc. Natl Acad. Sci. USA 90, 4087–4091 (1993).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Rohde, K. Latitudinal gradients in species diversity: the search for the primary cause. Oikos 65, 514–527 (1992).Article 

    Google Scholar 
    13.Allen, A. P., Gillooly, J. F., Savage, V. M. & Brown, J. H. Kinetic effects of temperature on rates of genetic divergence and speciation. Proc. Natl Acad. Sci. USA 103, 9130–9135 (2006).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    14.Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Igea, J. & Tanentzap, A. J. Angiosperm speciation cools down in the tropics. Ecol. Lett. 23, 692–700 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Schluter, D. Speciation, ecological opportunity, and latitude (American Society of Naturalists address). Am. Nat. 187, 1–18 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    17.Anderson, K. J. & Jetz, W. The broad-scale ecology of energy expenditure of endotherms. Ecol. Lett. 8, 310–318 (2005).Article 

    Google Scholar 
    18.Clarke, A. & Gaston, K. J. Climate, energy and diversity. Proc. R. Soc. B 273, 2257–2266 (2006).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    19.Dowle, E. J., Morgan-Richards, M. & Trewick, S. A. Molecular evolution and the latitudinal biodiversity gradient. Heredity 110, 501–510 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Brown, J. H. Why are there so many species in the tropics? J. Biogeogr. 41, 8–22 (2014).PubMed 
    Article 

    Google Scholar 
    21.Stevens, G. C. The latitudinal gradient in geographical range: how so many species coexist in the tropics. Am. Nat. 133, 240–256 (1989).Article 

    Google Scholar 
    22.Boucher-Lalonde, V. & Currie, D. J. Spatial autocorrelation can generate stronger correlations between range size and climatic niches than the biological signal — a demonstration using bird and mammal range maps. PLoS One 11, e0166243 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    23.Cutter, A. D. & Gray, J. C. Ephemeral ecological speciation and the latitudinal biodiversity gradient. Evolution 70, 2171–2185 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    24.Morales‐Barbero, J., Martinez, P. A., Ferrer‐Castán, D. & Olalla‐Tárraga, M. Á. Quaternary refugia are associated with higher speciation rates in mammalian faunas of the Western Palaearctic. Ecography 41, 607–621 (2018).Article 

    Google Scholar 
    25.Xing, Y. & Ree, R. H. Uplift-driven diversification in the Hengduan Mountains, a temperate biodiversity hotspot. Proc. Natl Acad. Sci. USA 114, E3444–E3451 (2017).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Lagomarsino, L. P., Condamine, F. L., Antonelli, A., Mulch, A. & Davis, C. C. The abiotic and biotic drivers of rapid diversification in Andean bellflowers (Campanulaceae). New Phytol. 210, 1430–1442 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Testo, W. L., Sessa, E. & Barrington, D. S. The rise of the Andes promoted rapid diversification in Neotropical Phlegmariurus (Lycopodiaceae). New Phytol. 222, 604–613 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Dowsett, H. et al. The PRISM4 (mid-Piacenzian) paleoenvironmental reconstruction. Climate 12, 1519–1538 (2016).
    Google Scholar 
    29.Hartley, A. J. Andean uplift and climate change. J. Geol. Soc. 160, 7–10 (2003).Article 

    Google Scholar 
    30.Aron, P. G. & Poulsen, C. J. in Mountains, Climate and Biodiversity (eds Hoorn, C., Perrugi, A. & Antonelli, A.) Ch. 8 (2018).31.Hewitt, G. The genetic legacy of the Quaternary ice ages. Nature 405, 907–913 (2000).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.Wallis, G. P., Waters, J. M., Upton, P. & Craw, D. Transverse Alpine speciation driven by glaciation. Trends Ecol. Evol. 31, 916–926 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    33.Luebert, F. & Muller, L. A. H. Effects of mountain formation and uplift on biological diversity. Front. Genet. 6, 54 (2015).34.Huang, S., Meijers, M. J. M., Eyres, A., Mulch, A. & Fritz, S. A. Unravelling the history of biodiversity in mountain ranges through integrating geology and biogeography. J. Biogeogr. 46, 1777–1791 (2019).Article 

    Google Scholar 
    35.Whittaker, R. J., Triantis, K. A. & Ladle, R. J. A general dynamic theory of oceanic island biogeography. J. Biogeogr. 35, 977–994 (2008).Article 

    Google Scholar 
    36.Li, Y. et al. Climate and topography explain range sizes of terrestrial vertebrates. Nat. Clim. Change 6, 498–502 (2016).Article 

    Google Scholar 
    37.Kisel, Y. & Barraclough, T. G. Speciation has a spatial scale that depends on levels of gene flow. Am. Nat. 175, 316–334 (2010).PubMed 
    Article 

    Google Scholar 
    38.Spooner, F. E. B., Pearson, R. G. & Freeman, R. Rapid warming is associated with population decline among terrestrial birds and mammals globally. Glob. Change Biol. 24, 4521–4531 (2018).Article 

    Google Scholar 
    39.Rowley, D. B. & Garzione, C. N. Stable isotope-based paleoaltimetry. Annu. Rev. Earth Planet. Sci. 35, 463–508 (2007).CAS 
    Article 

    Google Scholar 
    40.Mulch, A. Stable isotope paleoaltimetry and the evolution of landscapes and life. Earth Planet. Sci. Lett. 433, 180–191 (2016).CAS 
    Article 

    Google Scholar 
    41.Kuhn, T. S., Mooers, A. Ø. & Thomas, G. H. A simple polytomy resolver for dated phylogenies. Methods Ecol. Evol. 2, 427–436 (2011).Article 

    Google Scholar 
    42.Rolland, J., Condamine, F. L., Jiguet, F. & Morlon, H. Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient. PLoS Biol. 12, e1001775 (2014).43.Meredith, R. W. et al. Impacts of the Cretaceous Terrestrial Revolution and KPg Extinction on mammal diversification. Science 334, 521–524 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Britton, T., Anderson, C. L., Jacquet, D., Lundqvist, S. & Bremer, K. Estimating divergence times in large phylogenetic trees. Syst. Biol. 56, 741–752 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Drummond, A. J. & Rambaut, A. BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol. Biol. 7, 214 (2007).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    46.Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Redding, D. W. & Mooers, A. Ø. Incorporating evolutionary measures into conservation prioritization. Conserv. Biol. 20, 1670–1678 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    49.Rabosky, D. L. Automatic detection of key innovations, rate shifts, and diversity-dependence on phylogenetic trees. PLoS One 9, e89543 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    50.Moore, B. R., Höhna, S., May, M. R., Rannala, B. & Huelsenbeck, J. P. Critically evaluating the theory and performance of Bayesian analysis of macroevolutionary mixtures. Proc. Natl Acad. Sci. USA 113, 9569–9574 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Meyer, A. L. S., Román-Palacios, C. & Wiens, J. J. BAMM gives misleading rate estimates in simulated and empirical datasets. Evolution 72, 2257–2266 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    52.Rabosky, D. L., Mitchell, J. S. & Chang, J. Is BAMM flawed? Theoretical and practical concerns in the analysis of multi-rate diversification models. Syst. Biol. 66, 477–498 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    53.Mitchell, J. S., Etienne, R. S. & Rabosky, D. L. Inferring diversification rate variation from phylogenies with fossils. Syst. Biol. 68, 1–18 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    54.Title, P. O. & Rabosky, D. L. Tip rates, phylogenies and diversification: what are we estimating, and how good are the estimates? Methods Ecol. Evol. 10, 821–834 (2019).Article 

    Google Scholar 
    55.Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Amante, C. & Eakins, B. W. ETOPO1 Arc-minute Global Relief Model: Procedures, Data Sources and Analysis. NOAA Technical Memorandum NESDIS NGDC-24 (NOAA, 2009).57.Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).Article 

    Google Scholar 
    58.Brown, J. L., Hill, D. J., Dolan, A. M., Carnaval, A. C. & Haywood, A. M. PaleoClim, high spatial resolution paleoclimate surfaces for global land areas. Sci. Data 5, 180254 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Lefcheck, J. S. piecewiseSEM: Piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).Article 

    Google Scholar 
    60.Bivand, R. & Piras, G. Comparing implementations of estimation methods for spatial econometrics. J. Stat. Softw. 63, v063i18 (2015).
    Google Scholar  More

  • in

    High rates of short-term dynamics of forest ecosystem services

    1.Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).CAS 

    Google Scholar 
    2.Carpenter, S. R. et al. Science for managing ecosystem services: beyond the Millennium Ecosystem Assessment. Proc. Natl Acad. Sci. USA 106, 1305–1312 (2009).CAS 

    Google Scholar 
    3.Nelson, E. et al. Modeling multiple ecosystem services, biodiversity conservation, commodity production, and tradeoffs at landscape scales. Front. Ecol. Environ. 7, 4–11 (2009).
    Google Scholar 
    4.Maes, J. et al. An indicator framework for assessing ecosystem services in support of the EU Biodiversity Strategy to 2020. Ecosyst. Serv. 17, 14–23 (2016).
    Google Scholar 
    5.Maes, J. et al. Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst. Serv. 1, 31–39 (2012).
    Google Scholar 
    6.Millennium Ecosystem Assessment. Ecosystems and Human Well-Being: Synthesis (Island Press, 2005).7.Summary for Policymakers. In Global Assessment Report on Biodiversity and Ecosystem Services (IPBES, 2019).8.Martínez-Harms, M. J. & Balvanera, P. Methods for mapping ecosystem service supply: a review. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 8, 17–25 (2012).
    Google Scholar 
    9.Hauck, J. et al. ‘Maps have an air of authority’: potential benefits and challenges of ecosystem service maps at different levels of decision making. Ecosyst. Serv. 4, 25–32 (2013).
    Google Scholar 
    10.Balvanera, P. et al. Conserving biodiversity and ecosystem services. Science 291, 2047 (2001).CAS 

    Google Scholar 
    11.Dick, J., Maes, J., Smith, R. I., Paracchini, M. L. & Zulian, G. Cross-scale analysis of ecosystem services identified and assessed at local and European level. Ecol. Indic. 38, 20–30 (2014).
    Google Scholar 
    12.UK National Ecosystem Assessment. The UK National Ecosystem Assessment Technical Report. (UNEP-WCMC, 2011); http://uknea.unep-wcmc.org/13.Orsi, F., Ciolli, M., Primmer, E., Varumo, L. & Geneletti, D. Mapping hotspots and bundles of forest ecosystem services across the European Union. Land Use Policy 99, 104840 (2020).
    Google Scholar 
    14.Holland, R. A. et al. The influence of temporal variation on relationships between ecosystem services. Biodivers. Conserv. 20, 3285–3294 (2011).
    Google Scholar 
    15.Renard, D., Rhemtull, J. M. & Bennett, E. M. Historical dynamics in ecosystem service bundles. Proc. Natl Acad. Sci. USA 112, 13411–13416 (2015).CAS 

    Google Scholar 
    16.Rukundo, E. et al. Spatio-temporal dynamics of critical ecosystem services in response to agricultural expansion in Rwanda, East Africa. Ecol. Indic. 89, 696–705 (2018).
    Google Scholar 
    17.Stürck, J., Schulp, C. J. E. & Verburg, P. H. Spatio-temporal dynamics of regulating ecosystem services in Europe—the role of past and future land use change. Appl. Geogr. 63, 121–135 (2015).
    Google Scholar 
    18.Rau, A. L. et al. Temporal patterns in ecosystem services research: a review and three recommendations. Ambio 49, 1377–1393 (2020).
    Google Scholar 
    19.Sutherland, I. J., Bennett, E. M. & Gergel, S. E. Recovery trends for multiple ecosystem services reveal non-linear responses and long-term tradeoffs from temperate forest harvesting. For. Ecol. Manage. 374, 61–70 (2016).
    Google Scholar 
    20.Hansen, M. C., Stehman, S. V. & Potapov, P. V. Quantification of global gross forest cover loss. Proc. Natl Acad. Sci. USA 107, 8650–8655 (2010).CAS 

    Google Scholar 
    21.Vanhanen, H. et al. Making Boreal Forests Work for People and Nature (IUFRO, 2012).22.Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).CAS 

    Google Scholar 
    23.Moen, J. et al. Eye on the Taiga: removing global policy impediments to safeguard the boreal forest. Conserv. Lett. 7, 408–418 (2014).
    Google Scholar 
    24.Global Forest Industry (Swedish Forest Industries, 2019); https://www.forestindustries.se/forest-industry/statistics/global-forest-industry/25.Saastamoinen, O., Kangas, K. & Aho, H. The picking of wild berries in Finland in 1997 and 1998. Scand. J. For. Res. 15, 645–650 (2000).
    Google Scholar 
    26.Gamfeldt, L. et al. Higher levels of multiple ecosystem services are found in forests with more tree species. Nat. Commun. 4, 1340 (2013).
    Google Scholar 
    27.Hou, Y., Li, B., Müller, F., Fu, Q. & Chen, W. A conservation decision-making framework based on ecosystem service hotspot and interaction analyses on multiple scales. Sci. Total Environ. 643, 277–291 (2018).CAS 

    Google Scholar 
    28.Blumstein, M. & Thompson, J. R. Land-use impacts on the quantity and configuration of ecosystem service provisioning in Massachusetts, USA. J. Appl. Ecol. 52, 1009–1019 (2015).
    Google Scholar 
    29.Fernandez-Campo, M., Rodríguez-Morales, B., Dramstad, W. E., Fjellstad, W. & Diaz-Varela, E. R. Ecosystem services mapping for detection of bundles, synergies and trade-offs: examples from two Norwegian municipalities. Ecosyst. Serv. 28, 283–297 (2017).
    Google Scholar 
    30.Gissi, E., Fraschetti, S. & Micheli, F. Incorporating change in marine spatial planning: a review. Environ. Sci. Policy 92, 191–200 (2019).
    Google Scholar 
    31.Maxwell, S. M., Gjerde, K. M., Conners, M. G. & Crowder, L. B. Mobile protected areas for biodiversity on the high seas. Science 367, 252–254 (2020).CAS 

    Google Scholar 
    32.Willcock, S. et al. Do ecosystem service maps and models meet stakeholders’ needs? A preliminary survey across sub-Saharan Africa. Ecosyst. Serv. 18, 110–117 (2016).
    Google Scholar 
    33.Jonsson, M., Bengtsson, J., Gamfeldt, L., Moen, J. & Snäll, T. Levels of forest ecosystem services depend on specific mixtures of commercial tree species. Nat. Plants 5, 141–147 (2019).
    Google Scholar 
    34.Pohjanmies, T. et al. Impacts of forestry on boreal forests: an ecosystem services perspective. Ambio 46, 743–755 (2017).
    Google Scholar 
    35.Miina, J., Hotanen, J.-P. & Salo, K. Modelling the abundance and temporal variation in the production of bilberry (Vaccinium myrtillus L.) in Finnish mineral soil forests. Silva Fenn. 43, 577–593 (2009).
    Google Scholar 
    36.Hertel, A. G. et al. Berry production drives bottom–up effects on body mass and reproductive success in an omnivore. Oikos 127, 197–207 (2018).
    Google Scholar 
    37.Thiffault, E. Boreal forests and soils. Dev. Soil Sci. 36, 59–82 (2019).
    Google Scholar 
    38.Jonsson, M., Bengtsson, J., Moen, J., Gamfeldt, L. & Snäll, T. Stand age and climate influence forest ecosystem service delivery and multifunctionality. Environ. Res. Lett. 15, 0940a8 (2020).
    Google Scholar 
    39.Stokland, J. N. Volume increment and carbon dynamics in boreal forest when extending the rotation length towards biologically old stands. For. Ecol. Manage. 488, 119017 (2021).
    Google Scholar 
    40.Harmon, M. E., Ferrell, W. K. & Franklin, J. F. Effects on carbon storage of conversion of old-growth forests to young forests. Science 247, 699–702 (1990).CAS 

    Google Scholar 
    41.Mazziotta, A. et al. Applying a framework for landscape planning under climate change for the conservation of biodiversity in the Finnish boreal forest. Glob. Change Biol. 21, 637–651 (2015).
    Google Scholar 
    42.Triviño, M. et al. Optimizing management to enhance multifunctionality in a boreal forest landscape. J. Appl. Ecol. 54, 61–70 (2017).
    Google Scholar 
    43.Qiu, J. & Turner, M. G. Spatial interactions among ecosystem services in an urbanizing agricultural watershed. Proc. Natl Acad. Sci. USA 110, 12149–12154 (2013).CAS 

    Google Scholar 
    44.Felipe-Lucia, M. R. et al. Multiple forest attributes underpin the supply of multiple ecosystem services. Nat. Commun. 9, 4839 (2018).
    Google Scholar 
    45.Eggers, J., Räty, M., Öhman, K. & Snäll, T. How well do stakeholder-defined forest management scenarios balance economic and ecological forest values? Forests 11, 86 (2020).
    Google Scholar 
    46.Eyvindson, K., Repo, A. & Mönkkönen, M. Mitigating forest biodiversity and ecosystem service losses in the era of bio-based economy. For. Policy Econ. 92, 119–127 (2018).
    Google Scholar 
    47.Rusch, A., Bommarco, R., Jonsson, M., Smith, H. G. & Ekbom, B. Flow and stability of natural pest control services depend on complexity and crop rotation at the landscape scale. J. Appl. Ecol. 50, 345–354 (2013).
    Google Scholar 
    48.Schipanski, M. E. et al. A framework for evaluating ecosystem services provided by cover crops in agroecosystems. Agric. Syst. 125, 12–22 (2014).
    Google Scholar 
    49.Hufnagel, J., Reckling, M. & Ewert, F. Diverse approaches to crop diversification in agricultural research. A review. Agron. Sustain. Dev. 40, 14 (2020).
    Google Scholar 
    50.Guerry, A. D. et al. Modeling benefits from nature: using ecosystem services to inform coastal and marine spatial planning. Int. J. Biodivers. Sci. Ecosyst. Serv. Manage. 8, 107–121 (2012).
    Google Scholar 
    51.Wikström, P. et al. The Heureka Forestry Decision Support System: An Overview. Math. Comput. For Nat.-Resour. Sci. 3, 87–94 (2011).
    Google Scholar 
    52.Forest Statistics (Swedish University of Agricultural Sciences, 2020).53.Eriksson, A., Snäll, T. & Harrison, P. J. Analys av miljöförhållanden ‐ SKA 15. Report 11 (Swedish Forest Agency, 2015).54.Axelsson, A.-L. et al. in National Forest Inventories—Pathways for Common Reporting (eds Tomppo, E. et al.) 541–553 (Springer, 2010).55.Marklund, L. G. Biomass Functions for Pine, Spruce and Birch in Sweden (1988).56.Petersson, H. & Ståhl, G. Functions for below-ground biomass of Pinus sylvestris, Picea abies, Betula pendula and Betula pubescens in Sweden. Scand. J. For. Res. 21, 24–83 (2006).
    Google Scholar 
    57.Miina, J., Pukkala, T. & Kurttila, M. Optimal multi-product management of stands producing timber and wild berries. Eur. J. For. Res. 135, 781–794 (2016).
    Google Scholar 
    58.Schröter, M. & Remme, R. P. Spatial prioritisation for conserving ecosystem services: comparing hotspots with heuristic optimisation. Landsc. Ecol. 31, 431–450 (2016).
    Google Scholar 
    59.Wu, J., Feng, Z., Gao, Y. & Peng, J. Hotspot and relationship identification in multiple landscape services: a case study on an area with intensive human activities. Ecol. Indic. 29, 529–537 (2013).CAS 

    Google Scholar 
    60.Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Contr. 19, 716–723 (1974).
    Google Scholar 
    61.R: A Language and Environment for Statistical Computing (R Development Core Team, 2014); https://www.R-project.org/ More

  • in

    Amazonian forest degradation must be incorporated into the COP26 agenda

    These authors contributed equally: Celso H. L. Silva Junior, Nathália S. Carvalho, Ana C. M. Pessôa.Tropical Ecosystems and Environmental Sciences Laboratory (TREES), São José dos Campos, São Paulo, BrazilCelso H. L. Silva Junior, Nathália S. Carvalho, Ana C. M. Pessôa, João B. C. Reis, Aline Pontes-Lopes, Juan Doblas, Wesley Campanharo, Henrique Cassol, Yosio E. Shimabukuro, Liana O. Anderson & Luiz E. O. C. AragãoInstituto Nacional de Pesquisas Espaciais (INPE), São José dos Campos, São Paulo, BrazilCelso H. L. Silva Junior, Nathália S. Carvalho, Ana C. M. Pessôa, Aline Pontes-Lopes, Juan Doblas, Wesley Campanharo, Henrique Cassol, Luciana Gatti, Ana P. Aguiar, Yosio E. Shimabukuro & Luiz E. O. C. AragãoUniversidade Estadual do Maranhão (UEMA), São Luís, Maranhão, BrazilCelso H. L. Silva JuniorCentro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN), São José dos Campos, São Paulo, BrazilJoão B. C. Reis & Liana O. AndersonUniversity of Bristol, Bristol, UKViola Heinrich & Joanna HouseInstituto de Pesquisa Ambiental da Amazônia (IPAM), Brasília, Distrito Federal, BrazilAne Alencar, Camila Silva & Paulo BrandoUniversidade Estadual de Campinas (UNICAMP), Campinas, São Paulo, BrazilDavid M. LapolaEcología del Paisaje y Modelación de Ecosistemas (ECOLMOD), Universidad Nacional de Colombia (UNAL), Bogota, ColombiaDolors ArmenterasUniversidade de Brasília, Brasília, Distrito Federal, BrazilEraldo A. T. MatricardiUniversity of Oxford, Oxford, UKErika BerenguerLancaster University, Lancaster, UKCamila Silva, Erika Berenguer & Jos BarlowSouth Dakota State University, Brookings, SD, USAIzaya NumataEmpresa Brasileira de Pesquisa Agropecuária (EMBRAPA) Amazônia Oriental, Belém, Pará, BrazilJoice FerreiraUniversity of California, Irvine, CA, USAPaulo BrandoWoodwell Climate Research Center, Falmouth, MA, USAPaulo BrandoInstituto Nacional de Pesquisas da Amazônia (INPA), Manaus, Amazonas, BrazilPhilip M. FearnsideJet Propulsion Laboratory (JPL), Pasadena, CA, USASassan SaatchiUniversity of California, Los Angeles, CA, USASassan SaatchiUniversidade Federal do Acre (UFAC), Cruzeiro do Sul, Acre, BrazilSonaira SilvaUniversity of Exeter, Exeter, UKStephen Sitch & Luiz E. O. C. AragãoStockholm Resilience Centre, Stockholm University, Stockholm, SwedenAna P. AguiarSchool of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, FL, USACarlos A. SilvaEuropean Commission, Joint Research Centre (JRC), Ispra, VA, ItalyChristelle Vancutsem, Frédéric Achard & René BeuchleCenter for International Forestry Research (CIFOR), Bogor, IndonesiaChristelle Vancutsem More

  • in

    Flight performance and the factors affecting the flight behaviour of Philaenus spumarius the main vector of Xylella fastidiosa in Europe

    1.EFSA. Effectiveness of in planta control measures for Xylella fastidiosa. EFSA J. 17(5). https://doi.org/10.2903/j.efsa.2019.5666 (2019).2.Hopkins, D. L. Xylella fastidiosa: Xylem-limited bacterial pathogen of plants. Annu. Rev. Phytopathol. 27(1), 271–290. https://doi.org/10.1146/annurev.py.27.090189.001415 (1989).Article 

    Google Scholar 
    3.Saponari, M., Boscia, D., Nigro, F. & Martelli, G. P. Identification of Dna sequences related to Xylella fastidiosa in oleander, almond and olive trees exhibiting leaf scorch symptoms in Apulia (southern Italy). J. Plant Pathol. 95(3), 668. https://doi.org/10.4454/JPP.V95I3.035 (2013).Article 

    Google Scholar 
    4.EPPO. Xylella fastidiosa in EPPO region. EPPO Bulletin. 49(2) (2019).5.Fierro, A., Liccardo, A. & Porcelli, F. A lattice model to manage the vector and the infection of the Xylella fastidiosa on olive trees. Sci. Rep. 9, 8723. https://doi.org/10.1038/s41598-019-44997-4 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Saponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D. & Saldarelli, P. Xylella fastidiosa in olive in apulia: Where we stand. Phytopathology 109(2), 175–186. https://doi.org/10.1094/PHYTO-08-18-0319-FI (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    7.Mannino, M. R. et al. Horizon scanning for plant health: Report on 2017–2020 activities. EFSA Support. Publ. https://doi.org/10.2903/sp.efsa.2021.EN-2010 (2021).Article 

    Google Scholar 
    8.EFSA. Scientific opinion on the risks to plant health posed by Xylella fastidiosa in the EU territory, with the identification and evaluation of risk reduction options. EFSA J. 13(1), 3989. https://doi.org/10.2903/j.efsa.2015.3989 (2015).9.Cornara, D. et al. An overview on the worldwide vectors of Xylella fastidiosa. Entomol. Gen. 39(3–4), 157–181. https://doi.org/10.1127/entomologia/2019/0811 (2019).Article 

    Google Scholar 
    10.Finke, D. L. Contrasting the consumptive and non-consumptive cascading effects of natural enemies on vector-borne pathogens. Entomol. Exp. Appl. 144, 45–55. https://doi.org/10.1111/j.1570-7458.2012.01258.x (2012).Article 

    Google Scholar 
    11.Martini, X., Hoffmann, M., Coy, M. R., Stelinski, L. L. & Pelz-Stelinski, K. S. Infection of an insect vector with a bacterial plant pathogen increases its propensity for dispersal. PLoS ONE 10(6), 1–16. https://doi.org/10.1371/journal.pone.0129373 (2015).CAS 
    Article 

    Google Scholar 
    12.Almeida, R. P. P. et al. Addressing the new global threat of Xylella fastidiosa. Phytopathology 109(2), 172–174. https://doi.org/10.1094/PHYTO-12-18-0488-FI (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    13.Cornara, D., Bosco, D. & Fereres, A. Philaenus spumarius: When an old acquaintance becomes a new threat to European agriculture. J. Pest. Sci. 91(3), 957–972. https://doi.org/10.1007/s10340-018-0966-0 (2018).Article 

    Google Scholar 
    14.Halkka, O., Raatikainen, M., Vasarainen, A. & Heinonen, L. Ecology and ecological genetics of Philaenus spumarius (L.) (Homoptera). Ann. Zool. Fenn. 4, 1–18 (1967).
    Google Scholar 
    15.Lavigne, R. Biology of Philaenus leucophthalmus (L.) in Massachusetts. J. Econ. Entomol. 52(5), 904–907. https://doi.org/10.1093/jee/52.5.904 (1959).Article 

    Google Scholar 
    16.Ossiannilsson, F. The Auchenorrhyncha (Homoptera) of Fennoscandia and Denmark. Part 2: The families Cicadidae, Cercopidae, Membracidae, and Cicadellidae (excl. Deltocephalinae). Fauna Entomol. Scand. 7(2), 223–593 (1981).
    Google Scholar 
    17.Weaver, C. R. The seasonal behavior of meadow spittlebug and its relation to a control method. J. Econ. Entomol. 44(3), 350–353. https://doi.org/10.1093/jee/44.3.350 (1951).MathSciNet 
    CAS 
    Article 

    Google Scholar 
    18.Weaver, C. R. & King, D. R. Meadow spittlebug, Philaenus leucophthalmus (L.). Research Bulletin; Ohio Agricultural Experiment Station. (ed. Wooster, OH, USA, 1954).19.Drosopoulos, S. & Asche, M. Biosystematic studies on the spittlebug genus Philaenus with the description of a new species. Zool. J. Linn. Soc. 101(2), 169–177. https://doi.org/10.1111/j.1096-3642.1991.tb00891.x (2008).Article 

    Google Scholar 
    20.Grant, J. F., Lambdin, P. L. & Folium, R. A. Infestation levels and seasonal incidence of the meadow spittlebug (Homoptera: cercopidae) on musk thistle in Tennessee. J. Agric. Urban Entomol. 15, 83–91 (1998).
    Google Scholar 
    21.Halkka, O. Equilibrium populations of Philaenus spumarius L. Nature 193(4810), 93–94. https://doi.org/10.1038/193093a0 (1962).ADS 
    Article 

    Google Scholar 
    22.Freeman, J. A. Studies in the distribution of insects by Aerial currents. J. Anim. Ecol. 14, 128 (1945).Article 

    Google Scholar 
    23.Reynolds, D. R., Chapman, J. W. & Stewart, A. J. A. Windborne migration of Auchenorrhyncha (Hemiptera) over Britain. Eur. J. Entomol. 114, 554–564. https://doi.org/10.14411/eje.2017.070 (2017).Article 

    Google Scholar 
    24.Gutierrez, A. P., Nix, H. A., Havenstein, D. E. & Moore, P. A. The ecology of Aphis Craccivora Koch and subterranean clover stunt virus in south-east Australia. III. A regional perspective of the phenology and migration of the Cowpea Aphid. J. Appl. Ecol. 11(1), 21–35. https://doi.org/10.2307/2402002 (1974).Article 

    Google Scholar 
    25.Pienkowski, R. L. & Medler, J. T. Synoptic weather conditions associated with long-range movement of the potato leafhopper, Empoasca fabae, into Wisconsin. Ann. Entomol. Soc. Am. 57(5), 588–591. https://doi.org/10.1093/aesa/57.5.588 (1964).Article 

    Google Scholar 
    26.Drake, V. A. Radar observations of moths migrating in a nocturnal low-level jet. Ecol. Entomol. 10(3), 259–265. https://doi.org/10.1111/j.1365-2311.1985.tb00722.x (1985).Article 

    Google Scholar 
    27.Wallin, J. R. & Loonan, D. V. Low-level jet winds, aphid vectors, local weather, and barley yellow dwarf virus outbreaks. Phytopathology 61(9), 1068. https://doi.org/10.1094/PHYTO-61-1068 (1971).Article 

    Google Scholar 
    28.Sedlacek, J. D. & Freytag, P. H. Aspects of the field biology of the Blackfaced Leafhopper (Homoptera: Cicadellidae) in corn and pastures in Kentucky. J. Econ. Entomol. 79(3), 605–613. https://doi.org/10.1093/jee/79.3.605 (1986).Article 

    Google Scholar 
    29.Zhu, M., Radcliffe, E. B., Ragsdale, D. W., MacRae, I. V. & Seeley, M. W. Low-level jet streams associated with spring aphid migration and current season spread of potato viruses in the U.S. northern Great Plains. Agric. For. Meteorol. 138(1–4), 192–202. https://doi.org/10.1016/j.agrformet.2006.05.001 (2006).ADS 
    Article 

    Google Scholar 
    30.Bodino, N. et al. Dispersal of Philaenus spumarius (Hemiptera: Aphrophoridae), a vector of Xylella fastidiosa, in olive grove and meadow agroecosystems. Environ. Entomol. https://doi.org/10.1093/ee/nvaa140 (2020).Article 
    PubMed Central 

    Google Scholar 
    31.Lago, C. et al. Dispersal of Neophilaenus campestris, a vector of Xylella fastidiosa, from olive groves to over-summering hosts. J. Appl. Entomol. https://doi.org/10.1111/jen.12888 (2021).Article 

    Google Scholar 
    32.Minter, M. et al. The tethered flight technique as a tool for studying life-history strategies associated with migration in insects. Ecol. Entomol. 43(4), 397–411. https://doi.org/10.1111/een.12521 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    33.Ávalos-Masó, J. A., Martí-Campoy, A. & Soto, T. A. Study of the flying ability of Rhynchophorus ferrugineus (Coleoptera: Dryophthoridae) adults using a computer-monitored flight mill. Bull. Entomol. Res. 104(4), 462–470. https://doi.org/10.1017/S0007485314000121 (2014).Article 

    Google Scholar 
    34.Yu, E. Y., Gassmann, A. J. & Sappington, T. W. Using flight mills to measure flight propensity and performance of western corn rootworm, diabrotica virgifera virgifera (Leconte). J. Vis. Exp. 152, e59196. https://doi.org/10.3791/59196 (2019).Article 

    Google Scholar 
    35.Riley, J. R., Downham, M. C. A. & Cooter, R. J. Comparison of the performance of Cicadulina leafhoppers on flight mills with that to be expected in free flight. Entomol. Exp. Appl. 83(3), 317–322. https://doi.org/10.1046/j.1570-7458.1997.00186.x (1997).Article 

    Google Scholar 
    36.Zhang, Y., Wang, L., Wu, K., Wyckhuys, K. A. G. & Heimpel, G. E. Flight performance of the Soybean Aphid, Aphis glycines (Hemiptera: Aphididae) under different temperature and humidity regimens. Environ. Entomol. 37(2), 301–306. https://doi.org/10.1603/0046-225X(2008)37[301:FPOTSA]2.0.CO;2 (2008).Article 
    PubMed 

    Google Scholar 
    37.Cheng, Y., Luo, L., Jiang, X. & Sappington, T. Synchronized oviposition triggered by migratory flight intensifies larval outbreaks of beet. PLoS ONE https://doi.org/10.1371/journal.pone.0031562 (2012).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Jones, C. M. et al. Genomewide transcriptional signatures of migratory flight activity in a globally invasive insect pest. Mol. Ecol. 24(19), 4901–4911. https://doi.org/10.1111/mec.13362 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.White, S. M., Bullock, J. M., Hooftman, D. A. P. & Chapman, D. S. Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy. Biol. Invasions 19(6), 1825–1837. https://doi.org/10.1007/s10530-017-1393-5 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Jones, V. P., Naranjo, S. E. & Smith, T. J. Insect ecology and behavior: Laboratory flight mill studies. Accessed 22 July 2021. (2010). http://entomology.tfrec.wsu.edu/VPJ_Lab/Flight-Mill41.Martí-Campoy, A. et al. Design of a computerised flight mill device to measure the flight potential of different insects. Sensors (Switzerland) 16(4), 485. https://doi.org/10.3390/s16040485 (2016).ADS 
    Article 

    Google Scholar 
    42.Kees, A. M., Hefty, A. R., Venette, R. C., Seybold, S. J. & Aukema, B. H. Flight capacity of the walnut twig beetle (coleoptera: Scolytidae) on a laboratory flight mill. Environ. Entomol. 46(3), 633–641. https://doi.org/10.1093/ee/nvx055 (2017).Article 
    PubMed 

    Google Scholar 
    43.Morente, M. et al. Distribution and relative abundance of insect vectors of Xylella fastidiosa in olive groves of the Iberian peninsula. Insects 9(4), 175. https://doi.org/10.3390/insects9040175 (2018).Article 
    PubMed Central 

    Google Scholar 
    44.Morente, M., Cornara, D., Moreno, A. & Fereres, A. Continuous indoor rearing of Philaenus spumarius, the main European vector of Xylella fastidiosa. J. Appl. Entomol. 142(9), 901–904. https://doi.org/10.1111/jen.12553 (2018).Article 

    Google Scholar 
    45.Guthery, F. S., Burnham, K. P. & Anderson, D. R. Model Selection and multimodel inference: A practical information-theoretic approach. J. Wildl. Manag. 67, 655 (2003).Article 

    Google Scholar 
    46.Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R. (ed. Springer Sci. Bus. Media, 2009).47.Strona, G., Carstens, C. J. & Beck, P. S. A. Network analysis reveals why Xylella fastidiosa will persist in Europe. Sci. Rep. 7(1), 1–8. https://doi.org/10.1038/s41598-017-00077-z (2017).CAS 
    Article 

    Google Scholar 
    48.Whittaker, J. B. Density regulation in a population of Philaenus spumarius (L.) (Homoptera: Cercopidae). J. Anim. Ecol. 42(1), 163–172. https://doi.org/10.2307/3410 (1973).Article 

    Google Scholar 
    49.Wiman, N. G., Walton, V. M., Shearer, P. W., Rondon, S. I. & Lee, J. C. Factors affecting flight capacity of brown marmorated stink bug, Halyomorpha halys (Hemiptera: Pentatomidae). J. Pest Sci. 88(1), 37–47. https://doi.org/10.1007/s10340-014-0582-6 (2015).Article 

    Google Scholar 
    50.Strona, G. et al. Small world in the real world: Long distance dispersal governs epidemic dynamics in agricultural landscapes. Epidemics 30, 100384. https://doi.org/10.1016/j.epidem.2020.100384 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Irwin, M. E. & Tresh, J. M. Long-range aerial dispersal of cereal aphids as virus vectors in North America. Philos. Trans. R. Soc. London. B Biol. Sci. 321(1207), 421–446. https://doi.org/10.1098/rstb.1988.0101 (1988).ADS 
    Article 

    Google Scholar 
    52.Chapman, J. W., Reynolds, D. R. & Wilson, K. Long-range seasonal migration in insects: Mechanisms, evolutionary drivers and ecological consequences. Ecol. Lett. 18(3), 287–302. https://doi.org/10.1111/ele.12407 (2015).Article 
    PubMed 

    Google Scholar 
    53.Fereres, A., Irwin, M. E. & Kampmeier, G. E. Aphid movement: Process and consecuences. in Aphids as crop pests. (ed.2 Emden, H. F. van, Harrington, R.). 196–224. https://doi.org/10.1079/9781780647098.0196 (CABI Publishing, 2017).54.Petrovskii, S., Mashanova, A. & Jansen, V. A. A. Variation in individual walking behavior creates the impression of a Levy flight. PNAS 108, 8704–8707. https://doi.org/10.1073/pnas.1015208108 (2011).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    55.Okano, K. Sublethal effects of a neonicotinoid insecticide on the sharpshooter vectors of Xylella fastidiosa. Doctoral dissertation (UC Berkeley, 2009).56.Robinet, C., David, G. & Jactel, H. Modeling the distances traveled by flying insects based on the combination of flight mill and mark-release-recapture experiments. Ecol. Modell. 402, 85–92. https://doi.org/10.1016/j.ecolmodel.2019.04.006 (2019).Article 

    Google Scholar 
    57.Taylor, R. A. J., Bauer, L. S., Poland, T. M. & Windell, K. N. Flight performance of agrilus planipennis (Coleoptera: Buprestidae) on a flight mill and in free flight. J. Insect Behav. 23(2), 128–148. https://doi.org/10.1007/s10905-010-9202-3 (2010).Article 

    Google Scholar 
    58.Srygley, R. B. & Lorch, P. D. Coping with uncertainty: Nutrient deficiencies motivate insect migration at a cost to immunity. Integr. Comp. Biol. 53, 1002–1013. https://doi.org/10.1093/icb/ict047 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    59.Nilakhe, S. S. & Buainain, C. M. Observations on movement of spittlebug adults. Pesqui. Agropecuária Bras. Brasília 23, 123–134 (1988).
    Google Scholar 
    60.Neuman-Lee, L. A., Hopkins, G. R., Brodie, E. D. & French, S. S. Sublethal contaminant exposure alters behavior in a common insect: Important implications for trophic transfer. J. Environ. Sci. Heal. Part B Pestic. Food Contam. Wastes 48(6), 442–448. https://doi.org/10.1080/03601234.2013.761839 (2013).CAS 
    Article 

    Google Scholar 
    61.Wilson, D. M. The central nervous control of flight in a locust. J. Exp. Biol. 38(2), 471–490 (1961).Article 

    Google Scholar 
    62.Yamanaka, T., Tatsuki, S. & Shimada, M. Flight characteristics and dispersal patterns of fall webworm (Lepidoptera: Arctiidae) males. Environ. Entomol. 30(6), 1150–1157. https://doi.org/10.1603/0046-225X-30.6.1150 (2001).Article 

    Google Scholar 
    63.Blackmer, J. L., Hagler, J. R., Simmons, G. S. & Henneberry, T. J. Dispersal of Homalodisca vitripennis (Homoptera: Cicacellidae) from a point release site in citrus. Environ. Entomol. 35(6), 1617–1625. https://doi.org/10.1093/ee/35.6.1617 (2006).Article 

    Google Scholar 
    64.Bodino, N. et al. Phenology, seasonal abundance and stage-structure of spittlebug (Hemiptera: Aphrophoridae) populations in olive groves in Italy. Sci. Rep. 9(1), 1–17. https://doi.org/10.1038/s41598-019-54279-8 (2019).CAS 
    Article 

    Google Scholar 
    65.Minuz, R. L., Isidoro, N., Casavecchia, S., Burgio, G. & Riolo, P. Sex-dispersal differences of four phloem-feeding vectors and their relationship to wild-plant abundance in vineyard agroecosystems. J. Econ. Entomol. 106(6), 2296–2309. https://doi.org/10.1603/ec13244 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    66.Waloff, N. Dispersal by flight of leafhoppers (Auchenorrhyncha: Homoptera). J. Appl. Ecol. 10, 705 (1973).Article 

    Google Scholar 
    67.Johnson, C. G. Physiological factors in insect migration by flight. Nature 198(4879), 423–427. https://doi.org/10.1038/198423a0 (1963).ADS 
    Article 

    Google Scholar 
    68.Drake, V. A. & Gatehouse, A. G. Insect Migration. Tracking Resources through Space and Time. (ed. Cambridge University Press). 7(3) Cambridge UK. https://doi.org/10.1007/s10841-006-9039-4 (1995).69.Sappington, T. W. & Showers, W. B. Reproductive maturity, mating status, and long-duration flight behavior of agrotis ipsilon (Lepidoptera: Noctuidae) and the conceptual misuse of the oogenesis flight syndrome by entomologists. Environ. Entomol. 21(4), 677–688. https://doi.org/10.1093/ee/21.4.677 (1992).Article 

    Google Scholar 
    70.Zhao, X. C. et al. Does the onset of sexual maturation terminate the expression of migratory behaviour in moths? A study of the oriental armyworm, Mythimna separata. J Insect Physiol. 55(11), 1039–432009. https://doi.org/10.1016/j.jinsphys.2009.07.007 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    71.Tigreros, N. & Davidowitz, G. Flight-fecundity tradeoffs in wing-monomorphic insects. Adv. Insect Phys. 56, 1–41. https://doi.org/10.1016/bs.aiip.2019.02.001 (2019).Article 

    Google Scholar 
    72.Drake, V. A. & Farrow, R. A. The influence of atmospheric structure and motions on insect migration. Ann. Rev. Entomol. 33(1), 183–210. https://doi.org/10.1146/annurev.en.33.010188.001151 (1988).Article 

    Google Scholar 
    73.Burt, P. J. A. & Pedgley, D. E. Nocturnal insect migration: Effects of local winds. Adv. Ecol. Res. 27, 61–92. https://doi.org/10.1016/S0065-2504(08)60006-9 (1997).Article 

    Google Scholar 
    74.Gordh, G. & McKirdy, S. The Handbook of Plant Biosecurity (Springer, 2014). https://doi.org/10.1007/978-94-007-7365-3Book 

    Google Scholar  More

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    The impact of anthropogenic noise on individual identification via female song in Black-capped chickadees (Poecile atricapillus)

    SubjectsIn total, twenty-two black-capped chickadees (nine males and 13 females) were tested between May and December 2019, and 16 black-capped chickadees (seven males and nine females) completed the experiment. One male and one female failed to learn Pretraining, and one female failed to learn Non-differential training (see descriptions below for training information); as a result, all three were removed from the experiment. In addition, one male and two females died of natural causes during the course of the study (see Ethics Declaration). For all birds, sex was determined by deoxyribonucleic acid analysis of blood samples37. All birds were captured in Edmonton (North Saskatchewan River Valley, 53.53°N, 113.53°W; Mill Creek Ravine, 53.52°N, 113.47°W), Alberta, Canada in January 2018 and January 2019 and were at least one year of age at capture, verified by examining outer tail rectrices38.Prior to the current experiment, all chickadees were individually housed in Jupiter Parakeet cages (30 × 40 × 40 cm; Rolf C. Hagen, Inc., Montreal, QB, Canada) in a single colony room. Therefore, birds did not have physical contact with each other, but did have visual and auditory contact. Birds had ad libitum access to food (Mazuri Small Bird Maintenance Diet; Mazuri, St. Louis, MO, USA), water with vitamins supplemented on alternating days (Monday, Wednesday, Friday; Prime Vitamin Supplement; Hagen, Inc.), a cup containing grit, and a cuttlebone. Additional nutritional supplements included three to five sunflower seeds daily, one superworm (Zophobas morio) three times a week, and a mixture of hard-boiled eggs and greens (spinach or parsley) twice a week. The colony rooms were maintained at approximately 20 °C and on a light:dark cycle that followed the natural light cycle for Edmonton, Alberta, Canada.One bird had previous experience with one operant experiment involving chick-a-dee calls but showed no difference in responding in comparison to the naïve birds. The remaining 15 birds had no previous experimental experience with black-capped chickadee-produced fee-bee songs or any experimental paradigm.Recordings of acoustic stimuliThe following acoustic stimuli were used in our previous published operant study which indicated that male and female chickadees can identify individual females via their song36. Stimuli included the songs of six female black-capped chickadees. All females were captured in Edmonton (North Saskatchewan River Valley, 53.53°N, 113.53°W; Mill Creek Ravine, 53.52°N, 113.47°W), Alberta, Canada in January 2010, 2011, 2012, and 2014, and all females were at least one year of age at capture, verified by examining outer tail rectrices38. Four females were recorded in Spring 2012 and two females were recorded in Fall 2014. Each recording session lasted approximately 1 h and all recordings took place after colony lights turned on at 08:00, specifically at 8:15. All females were recorded in silence, individually, within their respective colony room cages. Colony room cages were placed in sound-attenuating chambers for recording (1.7 m × 0.84 m × 0.58 m; Industrial Acoustics Company, Bronx, NY). An AKG C 1000S (AKG Acoustics, Vienna, Austria) microphone (positioned 0.1 m above and slightly behind the cage) was connected to a Marantz PMD670 (Marantz America, Mahwah, NJ) digital recorder (16 bit, 44,100 Hz sampling rate) and was used for all recordings. Audio recordings were analyzed and cut into individual files (songs) using SIGNAL 5.03.11 software (Engineering Design, Berkley, CA, USA).Acoustic stimuliFor the current study, a total of 150 vocalizations were used as stimuli, these vocalizations were comprised of 25 fee-bee songs produced by each of six recorded female chickadees. We ensured that all 150 were of high quality, meaning no audible interference, and all stimuli were bandpass filtered (lower bandpass 500 Hz, upper bandpass 14,000 Hz) using GoldWave version 6.31 (GoldWave, Inc., St. John’s, NL, Canada) in order to reduce any background noise outside of the song stimuli spectrum. For each song stimulus, 5 ms of silence was added to the leading and trailing portion of the vocalization and each stimulus was tapered to remove transients, in addition amplitude was equalized peak to peak using SIGNAL 5.03.11 software. When triggered, stimuli were presented at approximately 75 dB peak SPL as measured by a calibrated Brüel & Kjær Type 2239 (Brüel & Kjær Sound & Vibration Measurement A/S, Nærum, Denmark) sound pressure meter (A-weighting, slow response), a level that corresponds with the natural chickadee vocalizations amplitudes39,40,41. All dB measurements were made at the level of the request perch where birds trigger stimuli and where birds are required to remain for the length of the stimuli and all dB measurements refer to SPL.Noise stimuliAnthropogenic noise stimuli were originally created and used by Potvin and MacDougall-Shackleton42 and by Potvin, Curcio, Swaddle, and MacDougall-Shackleton43. The stimuli were recorded from an urban area in Melbourne, Victoria, Australia and other anthropogenic noise stimuli of various trains, cars, motorcycles, and lawnmowers downloaded from Soundbible.com were used. Within Victoria44 and Alberta45,46, urban traffic noise averages 60–80 dB SPL. The files used varied in length, with those recorded in Melbourne all being 10 min in length and those downloaded from Soundbible.com varying between 1–10 minutes42,43. In total 10 tracks were used with 30 total minutes of noise stimuli. Three anthropogenic noise conditions were used in the study, including Silence (no noise), Low noise (anthropogenic noise stimuli played at ~ 40 dB peak SPL), and High noise (anthropogenic noise stimuli played at ~ 75 dB peak SPL) replicating the variation of traffic noise experienced in urban areas42,43. For the Low and High noise conditions the 10 tracks repeated on a randomized loop during data collection (natural light of light/dark cycle) with, thus noise exemplars overlapped songs by chance, to further emulate urban areas. Noise stimuli had natural variations and modulations in frequency and amplitude over the course of the sound files. All dB measurements for noise stimuli included in this study refer to SPL. See Fig. 1 for female song and traffic noise stimuli spectrograms and power spectra.Figure 1(A) Spectrogram of a female fee-bee song in silence. (B) Power spectrum of female fee-bee song in silence . (C) Spectrogram of female fee-bee song in low noise. (D) Power spectrum of female fee-bee song (black) in low noise (grey). (E) Spectrogram of female fee-bee song in high noise. (F) Power spectrum of female fee-bee song (black) in high noise (grey).Full size imageApparatusFor the duration of the experiment, birds were housed individually in modified colony room cages (30 × 40 × 40 cm; described above) which were placed inside a ventilated, sound-attenuating operant chamber. See Fig. 2 for illustration of operant conditioning chamber. All chambers were lit with a full spectrum LED bulb (3 W, 250 lm E26, Not-Dim, 5000 K; Lohas LED, Chicago, IL, USA), and maintained the natural light:dark cycle for Edmonton, Alberta. Each cage within each operant chamber contained two perches and an additional perch fitted with an infrared sensor (i.e., the request perch). See Fig. 2C. Each cage also contained a water bottle, grit cup, and cuttlebone See Fig. 2G-2H. Birds had ad libitum access to water (with vitamins supplemented on alternating days; Monday, Wednesday, Friday), grit, and cuttlebone and were provided two superworms daily (a morning and afternoon worm). An opening (11 × 16 cm) located on the left side of the cage allowed the birds to access a motorized feeder, with a red LED light, and equipped with an infrared sensor47. See Fig. 2B,D–F. The purpose of the sensor was so that food was only available as a reward for correct responses to auditory stimuli during the operant discrimination task. We should note that performance of the discrimination task is required for access to food and thus maintains motivation. For operation and data collection, a personal computer connected to a single-board computer48 scheduled trials and recorded responses to stimuli. Stimuli were played from a personal computer hard drive through a Cambridge Integrated Amplifier (model A300 or Azur 640A; Cambridge Audio, London, England). Data is downloaded once a day in order to reduce stress on subjects as all equipment must be tested following download, requiring contact with subjects. Stimuli played in the chamber through a Fostex full-range speaker (model FE108 Σ or FE108E Σ; Fostex Corp., Japan; frequency response range 80–18,000 Hz) located beside the feeder. See Sturdy and Weisman49 for a detailed description of the apparatus. See Fig. 2 for an illustration of the operant conditioning chamber set-up.Figure 2Illustration of the operant conditioning chamber, including: (A) speaker, (B) automated feeder, (C) request perch fitted with infrared photo-beam assembly, (D) feeder cup, (E) electrical inputs, (F) red LED, (G) water bottle, (H) and cuttlebone. Also shown is the feeder opening, and additional perches. To simplify, the sketch the front and floor of the chamber, and the enclosure’s acoustic lining are not included.Full size imageProcedureOperant conditioningOur current operant conditioning go/no-go set-up is used to understand how birds perceive auditory stimuli. By training the birds to respond to particular stimuli and withhold responding to other stimuli we can compare responses to both types of stimuli. The go/no-go paradigm requires the birds to learn which stimuli require correct responses (go), providing reinforcement (food), and which stimuli require birds to withhold responding (no-go), resulting in the avoidance of punishment (lights out).The current study follows nine stages, after learning to use the operant conditioning set-up, birds then go through Non-differential training (stage 1) where they will be exposed to all stimuli that will be used in the experiment and to ensure that the birds respond to the stimuli equivalently. Then birds complete Discrimination training (stage 2) where birds on two categories of sounds. One category is rewarded, the other category is punished. Then the Discrimination-85 (stage 3) phase prepares birds for future trials where there is no reward nor punishment. After this point, birds will follow three series (Silence; Low; High) of Discrimination-85 with noise (stage 4, 6, 8) and a corresponding Probe with noise (stage 5, 7, 9), meaning that that each subject will repeated the two discrimination tasks three times with different noise conditions, with the order of noise conditions randomized among individuals. The detailed procedures for each stage are described in the following.Non-differential trainingThe purpose of Non-differential training is to engender a high level of responding on all trials, across all stimuli. Once a bird learned to use the request perch fitted with a sensor as well as learned to use the feeder to obtain food then Pretraining began. During Pretraining, birds were trained to respond to a 1 s tone (1,000 Hz) in order to receive access to food. Pretraining occurred over an approximately 15-day period in order to allow acclimatization to the chamber, feeder, and speaker. Following Pretraining was Non-differential training. During Non-differential training, birds received food for responding to all fee-bee song stimuli. All trials began when a bird landed on the request perch and remained on the perch for between 900–1100 ms, at which point a randomly-selected song stimulus played. Songs were presented in random order from trial to trial until all 150 stimuli had been triggered and played without replacement; once all 150 stimuli were played, a new random sequence initiated. In the event that the bird left the request perch during a stimulus presentation, the trial was deemed interrupted, and resulted in a 30 s lights out of the operant chamber. If the bird entered the feeder within 1 s after the stimulus (any stimulus) was played, it was given 1 s access to food, followed by a 30 s intertrial interval. If a bird remained on the request perch during the stimulus presentation and the 1 s following the completion of the stimulus, then the bird received a 60 s intertrial interval with the lights on. Birds continued on Non-differential training until they completed six 450-trial blocks at ≥ 60% responding on average to all stimuli, at least four 450-trial blocks at ≤ 3% difference in responding to future rewarded versus future unrewarded Discrimination stimuli, at least four 450-trial blocks at ≤ 3% difference in responding to future rewarded versus unrewarded Discrimination stimuli. Then following a day of free feed (during which birds had ad libitum access to a food cup) birds completed a second round of Non-differential training in which they completed at least one 450-trial block that met each of the above requirements. A 450-trial block consisted of the bird experiencing each of the 150 stimuli three times. For the current study the average time to complete Non-differential training ranged from 10 to 41 days (M = 21.43, SD = 9).Discrimination trainingDiscrimination training procedures included only 114 out of the 150 training stimuli that were previously presented in non-differential training, and responses to these stimuli were now differentially reinforced. Specifically, correct responses to half of the stimuli (“rewarded stimuli”, S+) were positively reinforced with 1 s access to food, and incorrect responses to the other half (“unrewarded stimuli”, S−) were instead punished with a 30-s intertrial interval of lights off within the operant chamber. In regard to criterion, Discrimination training continued until a bird completed six 342-trial blocks with a discrimination ratio between their respective S+ and S− of greater than 0.80 with the last two blocks being consecutive. For discrimination ratio calculations see Response Measures below.The current subjects were randomly assigned to either a True category discrimination group (n = 10) or Pseudo category discrimination group (n = 6). Furthermore, chickadees in the True category discrimination group were divided into two subgroups: (a) True 1 (n = 5; three females and two males) discriminated between 57 rewarded fee-bee songs produced by three individual female chickadees (S+) and 57 unrewarded fee-bee songs produced by another three individual female chickadees (S−); and (b) True 2 (n = 5; two females and three males) discriminated between the same songs with opposite rewards, properly, the 57 rewarded (S+) fee-bee songs were the S− from True 1 and the 57 unrewarded (S−) fee-bee songs were the S+ from True 1. For birds in the True category discrimination the average number of blocks completed per day for Discrimination training ranged from 2.4–4.4 blocks (3.3 ± 0.7 blocks).In similitude, the Pseudo category discrimination group was divided into two subgroups: (a) Pseudo 1 (n = 3; two females and one male) discriminated between 57 randomly-selected rewarded (S+) fee-bee songs and 57 randomly-selected unrewarded (S−) fee-bee songs; and (b) the second subgroup Pseudo 2 (n = 3; one female and two males) discriminated between the same songs with opposite rewards, meaning, the 57 rewarded (S+) fee-bee songs were the S− from Pseudo 1 and the 57 unrewarded (S−) fee-bee songs were the S+ from Pseudo 1 (S+) fee-bee songs and 57 randomly-selected unrewarded (S−) fee-bee songs. To explicate, the purpose of the two Pseudo groups was to include a control in which subjects are required to memorize each vocalization independent of the producer rather than be trained to categorize songs according to individual chickadees as the True groups have been. All birds remained in their respective groups (True 1 and 2; Pseudo 1 and 2) for the duration of the study. For birds in the Pseudo category discrimination the average number of blocks completed per day for Discrimination training ranged from 3.3–6.1 blocks (4.34 ± 1.2 blocks).Discrimination-85 phaseDiscrimination-85 was identical to the above Discrimination training except that rewarded songs were reinforced with a reduced probability, P = 0.85. Therefore, for 15% of trials when a rewarded stimulus was played and a bird correctly responded, no access to food was triggered. Instead, a 30 s lights on intertrial interval occurred. The change in reinforcement occurs in order to prepare birds for Probe trials in which novel song stimuli were neither rewarded with access to food nor unrewarded with a lights out, instead nothing occurs. Discrimination-85 continued until birds completed two consecutive 342-trial blocks with a discrimination ratio of at least 0.80.Discrimination-85 phase with noiseAll subjects followed three series (Silence; Low; High) of Discrimination-85 with noise and a corresponding Probe with noise and the order of noise stimuli was randomly-selected for each bird. Discrimination-85 with noise was identical to the Discrimination-85 phase except one of the three noise stimuli conditions (Silence; Low noise, 40 dB SPL; High noise, 75 dB SPL) was played over the song stimuli. The noise stimuli condition was randomly-selected for each bird. Each bird went through three series of Discrimination-85 with noise (Silence; Low; High) until reaching criteria: two consecutive 342-trial blocks with a discrimination ratio of at least 0.80. Here, we were interested in how the addition of noise would impact discrimination between rewarded and unrewarded female song stimuli.Probe phase with noiseFollowing each Discrimination-85 phase with noise was a corresponding Probe phase with noise. During Probe the reinforcement contingencies from Discrimination-85 were maintained. In addition to the 114 stimuli from Discrimination training, this stage included 12 novel fee-bee songs (i.e., Probe stimuli), two from each of the six individual females. For True groups, six of these novel songs were categorized as P + and the other six as P-, based on whether they were produced by the same birds as the S+ or the S− training stimuli. For Pseudo groups, the novel songs were not assigned to categories. For both groups, the 12 novel stimuli were neither rewarded (no food access) nor unrewarded (no lights out). The birds completed six 126-trial blocks in which the 114 familiar discrimination stimuli repeated once per block and the 12 probe sequences played once per block. In addition, one of the three noise stimuli conditions (Silence; Low noise, 40 dB SPL; High noise, 75 dB SPL) was played over the song stimuli, and each bird went through three series of Probe with noise (Silence; Low; High) which corresponded with the birds previous Discrimination-85 phase with noise condition. Thus, all birds completed all three Discrimination-85 phases with noise conditions followed by the corresponding Probe with noise conditions, and the order of noise stimuli condition was randomly-selected for each bird. In Probe phases we are interested if subjects can categorize novel stimuli to previously rewarded or unrewarded female birds.Response measuresFor each 342-block trial during training (Discrimination-85 with noise; Probe with noise), proportion response was calculated (R + /(N-I)): R + represents the number of trials in which the bird went to the feeder, N represents the total number of trials, and I represents the number of interrupted trials in which the bird left the perch before the entire stimulus played. For Discrimination training and the Discrimination-85 phase, a discrimination ratio was calculated by dividing the mean proportion response to all S+ stimuli by the mean proportion response to S+ stimuli plus the mean proportion response to S− stimuli. A discrimination ratio = 0.50 specifies equal response to rewarded (S+) and unrewarded (S−) stimuli, a discrimination ratio = 1.00 specifies a perfect discrimination between S+ and S− stimuli. We also collected data regarding the number of blocks and days per stage (Discrimination training; Discrimination-85 training with noise) in order to examine the latency of discrimination learning.Statistical analysesAll statistical analyses were conducted using SPSS (Version 20, Chicago, SPSS Inc.). In order to compare the number of trials needed to reach criterion and the discrimination ratios between True and Pseudo groups for Discrimination Training we conducted an analysis of variance (ANOVA). For Discrimination-85 with noise (Silence, Low noise, High noise), an ANOVA was conducted to compare the number of trials needed to reach criterion and the discrimination ratios between True and Pseudo groups. We also conducted post-hoc tests in order to reveal any sex differences between groups.And for Discrimination-85 with noise and Probes with noise repeated measures ANOVA was conducted to compare proportion response to training stimuli and probe stimuli between True groups and Pseudo groups. Lastly, we conducted post-hoc tests in order to reveal any differences in the number of trials to reach criterion during Discrimination training and to Discrimination-85 with noise.Ethics declarationThroughout the experiment, birds remained in the testing apparatus to minimize the transport and handling of each bird. One male and two female subjects died from natural causes during operant training. Following the experiment, healthy birds were returned to the colony room for use in future experiments.All procedures were conducted in accordance with the Canadian Council on Animal Care (CCAC) Guidelines and Policies with approval from the Animal Care and Use Committee for Biosciences for the University of Alberta (AUP 1937), which is consistent with the Animal Care Committee Guidelines for the Use of Animals in Research. Birds were captured and research was conducted under an Environment Canada Canadian Wildlife Service Scientific permit (#13-AB-SC004), Alberta Fish and Wildlife Capture and Research permits (#56,066 and #56,065), and the City of Edmonton Parks permit. All methods are reported in accordance with ARRIVE guidelines. More

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    The homogenous alternative to biomineralization: Zn- and Mn-rich materials enable sharp organismal “tools” that reduce force requirements

    Because biological materials are often viscoelastic composites, with properties dependent on orientation as well as spatial and temporal scales, all tests were designed to mimic natural conditions of tool use. For example, because of possible anisotropies, indentations were made on contact surfaces instead of cross sections of the “tools”; abrasion resistance was measured in the contact direction. Fracture tests were designed to mimic the tension on the side of a tooth or sting subjected to lateral forces; the velocities in our impact tests were between 0.1 and 1 m/s, typical for tool interactions, though 1/10 as fast as for some organisms48,49.OrganismsAll organisms, except the salmon, were housed alive in our laboratory until just before testing. Samples were measured within 24 h of removal from the organism, and were maintained in high-humidity environments during the preparation period. This is particularly important because mechanical properties can depend strongly on water content and this dependence can differ between materials. For example, non-HEBs can harden more than HEBs with drying30,50,51.

    1.

    Leafcutter ants, Atta cephalotes, were obtained from colonies we collected in Flores, Guatemala and Arena Forest Reserve, Trinidad. They were maintained on Himalayan blackberry leaves, Rubus armeniacus, Portugese laurel, Prunus lusitanica, and Japanese spurge, Pachysandra terminalis.

    2.

    Nereid worms, Neanthes brandti (synonymous with Alitta brandti), were collected from trenches dug in bars near the mouth of the Coos River, Charleston, Oregon. They were kept in sea-water in a laboratory refrigerator at about 5 °C.

    3.

    Scorpions, Hadrurus arizonensis, from Arizona, were obtained from commercial suppliers (e.g. Bugs of America, http://bugsofamerica.com) and fed House crickets, Acheta domesticus, and mealworms, Tenebrio molitor larvae.

    4.

    Spiders, Araneus diadematus, were collected seasonally around the University of Oregon campus, and kept alive in a laboratory refrigerator at about 5 °C. Tarantulas, Aphonopelma hentzi were obtained from Carolina (https://www.carolina.com).

    5.

    Chitons, Katharina tunicata, and Cryptochiton stelleri were collected in rocky intertidal regions along the coast near Charleston, Oregon and kept alive in a laboratory refrigerator.

    6.

    Salmon heads, Oncorhynchus sp., were obtained fresh from seafood stores.

    7.

    Leaf cutter bees, Megachile rotundata, were obtained from Mason Bees for Sale (www.masonbeesforsale.com).

    Hardness, modulus of elasticity and damping measurementsHardness, modulus of elasticity and dynamic mechanical property measurements were made by pressing a sharp diamond probe into specimens and measuring the resulting indentation as it changed in time. A higher modulus of elasticity indicates that a structure is stiffer and suffers less elastic (quickly recovered) deformation. A higher hardness value indicates that the material will undergo less plastic (non-recovered) deformation and thus will have a smaller pit left behind after the indentation. A material with a higher loss tangent will absorb more energy of vibration (higher damping), and is characterized by lagging surface deformation and recovery (viscoelasticity) as the indention force changes. Damping can reduce damage because the energy absorbed and converted to heat is not available for breaking bonds in the material.We used an Atomic Force Microsocpe (AFM; NanoScope IIIa, Digital Instruments, Santa Barbara, CA) with an add-on force/displacement transducer (TriboScope, Hysitron Inc., Minneapolis, Minnesota). The Hysitron transducer held a polished diamond probe in place with capacitors that were used to sense the position of the probe and to impart vertical forces for indenting and imaging the specimen. Measurement regions were selected for minimal slope and surface topography as evidenced by the depth variation in AFM scans and the symmetry of the residual indents.In order to make the most biologically relevant measurements, indentations were made on regions of the external surface of structures that directly contact the environment. However, we also made measurements on cross-sections of the arthropod “tools” as part of preliminary SEM and indentation investigations to ensure that the thickness of epicuticle or other surface material would not distort the results for HEBs. Figure 2A shows an indentation on an un-polished surface—the original surface topography is visible as linear scratches that are small compared to the pyramidal indentation. When we could not avoid indentation-scale topography, we hand-polished the surface with 2000–12,000 grit sandpaper (Micro-mesh sheets, http://micro-surface.com), to smooth the surface on the scale of the test indents.Figure 2Images of testing samples for each of the measured properties. (A) AFM image of a residual indentation on the natural surface of the Mn-HEB region of the sting of a scorpion, Hadrurus arizonensis. Indentations were used in measuring hardness, modulus of elasticity and damping properties. Natural “scratches” are visible on the original surface around the triangular indentation made by the cube-cornered indenter. (B) Mandible of an ant, Atta cephalotes, before and after an abrasion testing session. The tip of the zinc-rich distal tooth in the “before” image has been flattened during a preliminary abrasion session. (C) Images from before and after energy of fracture testing of a 12 μm thick Zn-HEB test piece made from the nearly-flat side-surface of the fang of a spider, Araneus diadematus. The original fang shape is evident with the tip of the fang towards the top and the proximal side to the left. (D) Images from before and after impact resistance testing of a 12 μm thick Zn-HEB test piece made from the fang of a spider, Araneus diadematus. The test piece covers a 50 μm diameter backing-pit milled (using FIB-SEM) into a (reflective) silicon chip. The piece has been shattered from the impact in the image on the right.Full size imageSpecimens were mounted on atomic force microscopy (AFM) specimen disks (TedPella Inc., Redding, CA) in a mound of epoxy composite. The composite was prepared by mixing approximately 0.45 g of 400-grit aluminum oxide powder (Buehler, Evanston, Ill., Ted Pella Inc., Redding CA, or Kramer Industries, Piscataway, NJ—the later preferred because it was less reflective) with 0.075 mL each of resin and hardener (Quick Set Epoxy; Loctite, Rocky Hill, CT, and 5 m Quik-Cure Epoxy, Bob Smith Industries, Atascadero CA). The composite was stiff enough that the unpolished specimen had to be pressed in and could be oriented before curing so that the desired indentation region would retro-reflect a light beam sent through the eyepiece of a dissecting microscope back into the microscope, ensuring that the desired region would be flat for AFM scanning and indentation. The mounted specimens were placed in an oven at 39 °C for at least 1 h to cure the epoxy composite. Samples that were not tested immediately were kept on moist paper in a container in a refrigerator and were tested within 24 h to avoid dehydration and other changes. Additionally, the epoxy composite served as a barrier to reduce loss of water through the cut surfaces.The epoxy composite mounting technique was tested to ensure that small (about the same size as the biological specimens) “floating” glass cover-slip pieces would yield the same hardness and modulus of elasticity values as large, flat-mounted cover-slip pieces, and re-checked when relevant products were changed.In order to test whether there were any rapid changes in hardness or modulus of elasticity of HEBs, we tested a H. arizonensis sting, that was mounted for AFM measurements while still attached to an anaesthetized scorpion. We did not find a significant trend with time for 14 measurements made between 10 or 20 min (Zn-HEB and Mn-HEB respectively) after separating the live scorpion from the sting, and 5 h (largest R2: 0.006). These fast results were also comparable to results made using the standard technique, indicating that the technique described above was sufficient to prevent significant changes from dehydration. We also made preliminary measurements on scorpion joint cuticle, armour teeth, and other non-HEB regions of the cuticle, and found none that were harder than the region at the base of the sting, used here to represent non-HEB cuticle.We used a pyramid-shaped diamond probe with cubic corner facets (90° between the three faces)14,31. The steeper angle of the cubic tip, relative to a more commonly used Berkovich tip, made it easier to avoid surface features such as hairs. The diamond probe was positioned on the specimen using a 30× extra short focus monocular (M1030, Specwell Corporation, Tokyo, Japan). The indentation sequence began with the force being ramped linearly from 0 to 2 milliNewtons (mN) in 0.1 s, maintained at 2 mN for 10 s. The force was then ramped down to 1.5 mN over 0.1 s and then the force was varied sinusoidally at 10 Hz (for 25 cycles) with a peak-to-peak amplitude of 1.0 mN, in order to measure dynamic properties. The force was then ramped to 0 in 0.1 s.Probe-extension (Oliver–Pharr) and image-based measurementsTwo methods of obtaining the modulus of elasticity and hardness were employed14. In the first method, values were obtained only from force–displacement curves using the Oliver–Pharr technique52,53. The modulus of elasticity was obtained from the slope of the force–displacement curve at the beginning of withdrawal of the indenter. Oliver–Pharr hardness values were calculated from the intercept of this sloped line with the line of zero force.In addition to the hardness value obtained from the force–displacement curves, we also obtained a hardness value based on measurements of the size of the residual indentation. These image-based hardness values were calculated as H = F/A, where “F” is the maximum force applied to the probe, and “A” is the projected area of the residual indentation, obtained from the perimeter of the indentation measured on an AFM image (e.g. Fig. 2A) made by scanning the indenting probe itself minutes after indenting the specimen.The Oliver–Pharr method is inaccurate if the indentation force causes the surface of the specimen to move, such as for improperly backed specimens, because it assumes probe extension is a measure of indentation depth. In contrast, the image-based method is nearly insensitive to global displacements of the specimen because it is based only on the applied force and measurements of the residual indentation. We found it useful to obtain both values to check each other: on several occasions differences between the two measured values indicated support problems. This is important for biological specimens with multiple layers, voids, etc. For example, if there is a lumen under the shaft but not the tip, the tip may appear harder because it displaces less. In addition, calibration problems, such as from fractured silica, were quickly identified by differences in the image and Oliver–Pharr values. Finally, the image method is not sensitive to other artifacts, such as “pile-up”, that are associated with estimating contact region from probe extension53,54,55,56.There is a potential difference between the Oliver–Pharr and image hardness values associated with the different time scales. The Oliver–Pharr hardness is measured during the probe withdrawal while the image of the residual indent is obtained a couple of minutes after indentation. If the indent partially recovers in the interim, the image technique would be based on a smaller residual indentation resulting in a higher hardness value. We prefer the image method not only because it is robust to imperfectly supported specimens, but also because we would like our hardness measurement to reflect the long-term indentation damage done to the tips and blades. To test that the indent size had stabilized by the time we measured it, we re-measured an indentation in the zinc-region of a scorpion sting after more than 6 months and found that the indentation diameter had decreased little, by about 15%.We also calculated an image-based modulus of elasticity value that has been suggested for materials that produce “pile-up” artifacts53. The area measured from the image of the residual indentation (used for the image-based hardness), was substituted for the contact area calculated from probe extension in Eq. (6) of Oliver & Farr, 2004 53.Notwithstanding the differences in image-based and probe-extension based (Oliver–Pharr) measurements, there was little practical difference in results, as shown in Fig. 3A,B. The metals and plastics that we measured for Fig. 3 tended to have slightly higher Oliver–Pharr hardness values than image-based values, possibly because of “pile-up” artifacts.Figure 3Comparison of probe-extension (Oliver–Pharr) and image-based techniques for hardness (A) and reduced modulus of elasticity (B) for our indentation data. Pile-up may account for higher (above the line) Oliver–Pharr values of some of the metals and plastics.Full size imageIn the “Results” section, we plot the values obtained using the images, but the Oliver–Pharr values are included along with image-based values in the results table, Table 1.Loss tangentDynamic mechanical properties were measured from the sinusoidal segments of the indentation sequence by comparing the amplitude and phase of the displacement to the applied sinusoidal force57. Phase lags associated with the transducer and electronics were determined assuming zero true-lag from a fused silica standard obtained using the same indentation sequence and force. The loss tangents obtained in this way were for the high-stress regimes associated with indentation, as compared to low-stress tests involving bending without plastic deformation.Calibration for Oliver–Pharr measurementsIn order to obtain the contact area from the indent depth, the shape of the indenting tip must be known. We characterized the indenting tip shape directly using scanning electron microscope images, so that we could make deep, micron-scale indents that would be evident on un-polished biological surfaces. We could not make indents in silica as deep as the indents desired for our biological specimens without fracturing the silica (fracture for the cubic cornered tip began at about 3 mN) so we could not use the usual technique of estimating the tip shape at depth by calibrating with fused silica. The tip shape was characterized by three measurements: first, the angle of the three-sided pyramidal tip (α), second, a measure of the bluntness of the tip (B), the distance between the apex of the tip if it were an ideal pyramid and the actual blunt tip, and third, a measure of the distance from the blunt tip beyond which the shape of the tip was not distinguishable from an ideal pyramid (I). The value “I” was used as a limit: only indents with greater depth than “I” were used to calculate mechanical properties. For these deeper indents, the following description of the projected area (A) of the contact region between the tip and the specimen was used:$$A= frac{(0.433)(4){(D+B)}^{2}}{frac{1}{{mathrm{tan}}^{2}left(frac{alpha }{2}right)}-0.3333},$$where D is the depth of the indent, determined by the extension of the indenting probe, 0.433 is the ratio of the area of an equilateral triangle to the square of the length of a side, and 0.3333 is tan2 (30°). As an example, the tip used for the majority of measurements was characterized by α = 89.9°, B = 115 nm, I = 100 nm. Thus, for indents with a depth greater than 100 nm, for our tip, A = 2.58 (D + 115 nm)2.Measurement of residual indentation areaThe area of the residual indent was measured using an AFM image, obtained minutes after the indentation, using the indenting tip as the imaging tip. To minimize inaccuracies in indent perimeter determination, caused by finite size of the imaging probe or other systematic errors, we calibrated our area measurements so that we obtained a median value of 70 GPa for measurements of the modulus of elasticity on fused silica. Because there is some subjectivity in measuring the size of the indentation, operator-specific calibrations, based on each operator’s measurements of fused silica, were used for most of the measurements.Test piece preparation for impact resistance and energy of fracture measurementsWe measured resistance to impact and fracture using custom miniature versions of testing devices that fracture or damage standardized “test pieces” of materials. We prepared test pieces as follows: the fresh (usually immediately after removal from the organism) specimens were adhered to one end of a glass slide using a marine epoxy (Loctite, Rocky Hill, CT) which required a curing time of 2 h at 39 °C, or with cyanoacrylate adhesive (Krazy Glue, all purpose, Elmer’s Products)14, which required no extra curing time. A flat region ( > 100 μm diameter, but not wide enough to reduce the thickness of the HEB region in the center to less than 12 μm) was polished on a specimen by grinding the slide with the specimen against a sequence of flat 2000, 6000 and 12,000 grit sandpaper (Micro-mesh sheets, http://micro-surface.com). The specimen was removed from the adhesive using a scalpel, inverted, and the polished-flat region was adhered to the glass with a thin film of water and surrounded by a small bead of marine epoxy. The water film kept the epoxy from getting pulled under the specimen by capillary action. The epoxy was cured and the specimen polished to a thickness of 12 ± 2 μm as determined with a digital micrometer, using the same sandpaper sequence. The resulting test piece was then freed by scraping the epoxy from around the edges using a scalpel blade. The area of the pieces varied according to the size of flat regions and was, typically, hundreds of microns on a side.Maintaining hydration was especially important for these test pieces because they were only 12 μm thick and so they could dry quickly. To reduce artifacts from drying or other changes in the tested material, all preparation and testing took place in a ~ 15 m3 enclosure maintained at greater than 90% relative humidity.Although we used FIB-SEM (Focused Ion Beam-Scanning Electron Microscope) to shape specimens of the materials for molecular fragment analysis, we did not use this technique for preparing our micron-scale test pieces for several reasons: potential material property changes caused by beam damage, subjection to vacuum, and because some of the test pieces needed to be large enough that they would be difficult to make with FIB-SEM.Impact resistance measurementsA custom testing device was built to compare the energy required for a swinging pendulum to shatter test pieces of the different materials (Fig. 2D). A 12 ± 2 μm thick test piece was adhered by the moisture in the high-humidity enclosure and held in place with an adhesive (spots of cyanoacrylate, Krazy Glue, all purpose, Elmer’s Products, www.elmers.com, or 5-min epoxy gel) over a 50 μm-diameter circular pit milled in a silicon wafer using a FIB-SEM apparatus. The pendulum, made of carbon fiber and aluminum (length: 0.2 m, moment of inertia: 4.25 x 10−6 kg m2) with a diamond impactor tip polished to a diameter of 20 μm, was held by miniature bearings and electronically released from increasing heights until the test piece fractured. The energy required to fracture the specimen was calculated from the release height from which the pendulum fractured the test piece. This energy was normalized by the measured thickness of the specimens to give joules required per meter of thickness. Nevertheless, we consider this test to be a relative test that is not expected to be generalizable to all impacts, as the energy to fracture is likely to depend not only on thickness but also on variables such as the diameter of the impactor tip and the diameter of the backing hole.This impact test differs from Charpy and Izod tests in that the energy required to fracture the specimen was measured by releasing the pendulum from increasing heights until the specimen fractured, rather than by releasing it from a height sufficient to fracture all specimens, and measuring the residual energy of the pendulum after impact. The advantage of our threshold technique is that the threshold of fracture is likely the biologically important quantity, and direct determination of the fracture threshold avoids the possibility that the energy deposited in a single highly-energetic impact might be partially expended in plastic deformation, leading to an overestimate of the threshold energy. A drawback of our technique is that impacts that do not break the specimen may produce damage that weakens the specimen for subsequent impacts. Nevertheless, all specimens were subjected to the same series of increasingly energetic impacts, until fracture, and were thus comparable.Energy of fracture measurementsWe measured the energy or work required to slowly break a test piece in two (Fig. 2c) using a custom fracture toughness measuring device14. The device drove apart two microscope cover slips bridged by the test piece until it split in two, while recording the required force and the displacement (work is the product of force and incremental distance). This work, divided by the area of the new post-fracture surfaces, is the energy of fracture, reported in Joules per meter squared. It is a measure of the energy required, per unit area, to break the bonds that originally held the two pieces together (as long as the kinetic energy is relatively small—the pieces do not fly away), and is one indication of the resistance of a material to fracture.The length of the fracture was measured using a microscope and multiplied by the thickness of the test piece to obtain the fracture area. This area was used to normalize force–displacement curves. The work of fracture per unit area of the fracture was obtained by numerically integrating these normalized force–displacement curves. The load cell was designed to be stiff in order to minimize storage of energy within the apparatus as the specimen underwent tension58. Fracture planes were perpendicular to the original surface and approximately perpendicular to the long axis of the “tool” (Fig. 1C).The test protocol was altered from that used previously14 because the test pieces used here were smaller. Test pieces were not notched in order to avoid fractures from the notching process, and the specimens were adhered to the test apparatus in place (Krazy Glue, all purpose, Elmer’s Products, www.elmers.com) to avoid premature fracture. To improve the bonding of the cyanoacrylate adhesive to the glass cover slips in the high humidity atmosphere, we treating the cover slips with a 10-s dip in a 2% (by volume) 3-aminopropyltriethoxysilane (Sigma Chemical Co., www.sigmaaldrich.com), 98% acetone solution, followed by rinses in deionized water and air drying. The test protocol for the ant mandibular teeth varied from the others in that whole teeth were fractured instead of 12μ polished test pieces.A consistency test with AFM data was developed to identify cases of imperfect bonding to the cover slips, when part of the measured energy was expended in partially pulling the test piece out of the cyanoacrylate adhesive. For samples subject to this problem (usually specimens with small adhesive contact areas, such as the fang specimen in Fig. 1C), we required that the force–displacement curve be consistent with the stiffness of the test piece, expected from a model based on the shape of the individual test piece and the slope of the force–displacement curve for the insertion portion of the nano-indentation sequence for that material. When a piece partially pulled out and failed this test, the apparent stiffness was much lower than the expected stiffness (from nano-indentation) and slight stretch marks were often visible in the adhesive on close inspection.This stiffness consistency test was also found to be useful in identifying cases where part of the fracture was pre-existing but had not been visible in the test piece. The pre-existing fracture would tend to reduce the effective width of the specimen and thus could be identified by a lower than expected stiffness under tension.Abrasion resistance measurementsWe measured the energy required to abrade away a volume of material from our specimens by holding them against a rotating abrasive disk. The energy used in eroding the material is given by the force of friction multiplied by the distance traveled over the abrasive paper (work is the product of the force and incremental displacement), with units of Joules per meter cubed of volume worn away.The “pin on disk” type testing device, developed for testing pieces of crab cuticle14, was used with modified procedures for the smaller specimens here. Instead of cylindrical core samples, whole, approximately conical tips of teeth, fangs or stings were used (Fig. 2B). The samples were affixed with cyanoacrylate gel adhesive (Maxi-Cure, Bob Smith Industries, Atascadero CA) to a steel pin held in the head of the wear tester. This head was mounted on a custom-made load cell that measured the horizontal force produced by friction between the specimen pin and the abrasive turntable. During the wear test, the specimen pin was held against the turntable with adjustable weights that, for the standard test, produced a downward force of 0.019 Newtons. The surface of the turntable was covered with 600 grit abrasive paper (#413Q, 3 M Corporation, www.3M.com). The turntable rotation period was usually set to about 4 s, resulting in an interaction velocity of 0.027 m/s.The volume worn away was calculated from “before” and “after” measurements of microscope images (image J software) taken from the side (e.g. Fig. 2B) to measure the height of the approximate cone of worn material, and from face-on in order to measure the area of the base and top of the frustum of worn-away material. The horizontal force was recorded continuously during the wear period. The wear rate (w), defined as the volume worn away per unit energy expended, was approximated as follows:$$w= frac{V}{Fd} ,$$where F is the average force of friction measured during the wear period by the load cell, d is the distance traveled by the pin over the abrasive paper and “V” is the worn volume, approximated as a frustum:$$V = 1{/}3,Delta L , (A1 + left( {A1*A2} right)^{1/2} + A2)$$where “A1” and “A2” are the areas of the worn surface before and after the wear sessions (the area of any voids or internal lumens was subtracted from the area of the cross sections) and ΔL, the change in the length of the specimen due to wear. We defined wear resistance as the inverse of the wear rate, 1/w. While we expect this test to be most useful for relative comparisons, and the value is expected to vary somewhat with abrasive properties and normal forces, we found no statistically distinguishable difference in values from 4 samples that were re-run using a ten-times greater force to press them against the abrasive paper14.Molecular composition and nanometer-scale structureIn order to better understand the composition and structure of the HEBs—down to an atomic scale—we examined a representative HEB using Atom Probe Tomography (APT). We checked the APT results and studied their generality using Time-of-Flight–Secondary Ion Mass Spectrometry (ToF–SIMS). Both of these techniques use a pulsed beam (laser and ion respectively) to break the specimen into molecular fragments that are accelerated to a detector; for a particular charge, heavier fragments travel more slowly and arrive later at the detector. The arrival time differences are used to identify the fragments by their mass, giving information about, for example, the atoms attached to zinc atoms in the specimen and, from APT, the spatial distribution of zinc atoms on a nanometer scale.Atom probe tomography (APT)APT is a 3D nanoscale characterization method in which field evaporated ions from a sharpened needle specimen are analyzed by a position-sensitive single-particle detector, in order to provide an isotopically resolved three-dimensional representation of the real-space specimen elemental distribution59. The field evaporation of non-conductive samples is achieved using a pulsed laser focused on the needle specimen apex.A FIB-SEM based lift-out procedure was used to prepare needle-shaped APT specimens using FEI Helios 600i at the University of Oregon CAMCOR facility, and a Helios Dual Beam Nanolab 600 FIB-SEM housed at Environmental Molecular Sciences Laboratory, PNNL.The APT analysis was carried out using a CAMECA LEAP (local electrode atom probe) 4000X HR system equipped with a 355 nm wavelength picosecond pulsed UV laser. A 30 K sample base temperature and a 100 or 200 kHz laser pulse repetition rate was used. Atom probe data reconstruction and analysis was performed using Cameca IVAS software.
    Development of APT techniques for these organic materials.APT has not typically been used to examine organic materials, so we began by examining standards (such as zinc picolinate) and adjusting beam current densities and other parameters in both the FIB-SEM preparation of APT samples and in APT itself in order to minimize physical damage detected with SEM and to minimize differences between the chemical formulae and APT results for standards60. We found that current densities often employed in FIB-SEM milling were much too high for our organic specimens, resulting in beam damage visible in SEM.Based on the standards and SEM evidence of damage, we used FIB-SEM currents for producing the sample needles, and APT laser pulses for promoting evaporation, that were similar to or smaller than those used in other investigations of organic materials61,62,63,64,65,66,67,68. We used ≤ 21 pA for the electron beam, ≤ 80 pA for ion beam “trenching”, 7.7 pA for ion beam imaging and for cutting the cantilever “liftout” piece, and ≤ 24 pA for sharpening needles. The results reported here are based on samples analyzed using 10, 20 or 100 pJ laser pulses.Identification of molecular fragments from mass-to-charge ratios is particularly difficult for organic materials because of the many possible fragments of similar mass, and several techniques have been developed to aid in this analysis61,62,63,64,68,69,70,71,72,73.Our identification of zinc-containing fragments was simplified by the pattern of the 3 or 4 main zinc isotopes (Fig. 6A). In addition, we used resources with lists of fragments as a function of mass (e.g. https://webbook.nist.gov/chemistry/mw-ser/). We also cross-checked fragment identification using a Time-of-Flight–Secondary Ion Mass Spectrometry (ToF–SIMS) system.Time of flight–secondary ion mass spectrometry (ToF–SIMS)We used a ToF–SIMS system that had a higher mass-to-charge resolution than the APT system (although it had micron- instead of nanometer-scale spatial resolution) in order to check APT fragment identification. The higher mass sensitivity of the ToF–SIMS system provided additional evidence that, for example, the fragments identified as ZnCN were not actually ZnC2H2, which is only about 0.02% lighter. We also used ToF–SIMS to study the larger-scale spatial distribution of fragments, and similarities with HEBs from other species.We used an ION-TOF ToF–SIMS IV, manufactured by ION-TOF GmbH, Muenster, Germany. The primary ion beam was Bi3+ (25 kV, 10 kHz, 0.4 pA); the static limit (2 × 1012 ions/cm2) was not exceeded. The dimensions of the analysis area varied, but were between 100 × 100 μm and 300 × 300 μm. A low energy electron beam was used for charge neutralization. The spectra were analyzed using the vendor’s software. Chemical maps of peaks of interest were created from the total spectra and used as a basis for retrospective analysis—i.e., pixel-specific extraction of spectra in order to determine the chemical makeup of features of interest. More