More stories

  • in

    Relative effects of land conversion and land-use intensity on terrestrial vertebrate diversity

    cSAR modelWe used the numerical cSAR model16 to calculate native species loss of four taxonomic groups (mammals, amphibians, reptiles, birds) caused by 45 LU types that were mapped onto a reference 5 × 5 arcmin grid (we also call individual grid cells landscapes in the following) of the global land area excluding Greenland and Antarctica. Calculations were based on (a) gridded LU-intensity and LU-type information (see below), (b) effects of LU-intensity on species richness derived from recently published meta-analyses5,21, and (c) information on species distributions and habitat affiliations from IUCN and Birdlife International databases41,42. For presentation of results, we aggregated the calculated effects of the 45 LU-types into those of six broad LU-types (cropland (30 annual crop types); pastures (non-grassland converted to grassland); grazing land (natural/ near-natural areas with livestock grazing); builtup (sealed areas); plantations (11 permanent crop types plus timber plantations), and forests (natural/ near-natural forest under forestry); see Supplementary Data 2 for details).In the below formulae, we use the following indices: g = taxonomic group, n = grid cell, b = broad LU-type. We calculated the total number of native species losses for each taxonomic group g and grid cell n as$${{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}={{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}times left(1-{left(frac{{{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{cur}}}}}}}+{sum }_{{{{{{rm{b}}}}}}=1}^{{{{{{{rm{b}}}}}}}_{{{{{{rm{n}}}}}}}}{{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}times {{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}}{{{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}}right)}^{{{{{{{rm{z}}}}}}}_{{{{{{rm{n}}}}}}}}right)$$
    (1)
    Here, ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) is the potential species richness in pristine ecosystems, ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{cur}}}}}}}) is the pristine ecosystem area where no LU occurs (in m2), ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) is the grid cell’s terrestrial area (in m2), ({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) is the affinity parameter, ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) is the area of the LU-type, and ({{{{{{rm{z}}}}}}}_{{{{{{rm{n}}}}}}}) is the grid cell’s SAR exponent taken from ref. 43. The model’s components are described below.Potential species richness ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}})
    We defined potential species richness of a landscape as the number of species for which the area-of-habitat (AOH) under pre-human or pristine conditions overlap the landscape (here referred to as native species and native AOH). Following ref. 44, we used range maps of all mammal, reptile and amphibia species provided by the IUCN45 and bird species by Birdlife International46 databases to calculate gridded species richness via, first, overlapping each species’ range polygons with a 5 × 5 arcmin reference raster, second, constraining the resulting list of species per raster cell to those adapted to the pristine ecosystem(s) of these raster cell as defined in ref. 25, and, third, constraining the resulting list of species by each species’ elevational range, also provided by the IUCN42. Here, we are interested in the total historical range of extant species46 and hence included all parts of the range where the species were indicated as (i) Extant, Probably Extant, Possibly Extinct, Extinct and Presence Uncertain, (ii) Native and Reintroduced, and (iii) Resident or present during the Breeding Season or the Non-breeding Season, in the cited data sources.We first rasterized each species’ range polygons using the raster and fasterize packages in R47. Second, for each terrestrial grid cell in our reference raster, we created a species list by extracting each species’ gridded range using the velox package in R. Third, we ascertained that each cell’s species list contained only terrestrial species by excluding species which exclusively have aquatic habitat affiliations. The species’ habitat affiliations were directly taken from the IUCN and Birdlife databases42,46. Fourth, we removed species from this cell’s species list which, according to the IUCN, are not affiliated with that cell’s pristine ecosystem. We therefore manually assigned the habitats distinguished in the ICUN habitat affiliation scheme to one or several of the 14 broad ecosystem types distinguished and mapped in ref. 25 (Supplementary Data 4). The maps in the referenced study “approximate the original extent of natural communities prior to major land-use change”48 and, hence, represent pristine ecosystems or potential vegetation types. Fifth, we excluded species whose elevational range did not overlap the elevational range of the grid cell using the GMTED2010 dataset (www.usgs.gov). These refinement steps were taken because species’ range maps usually deliver coarse-scale extent of occurrence rather than AOH information44. Finally, we counted the species identities in each grid cell as ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}). The species lists created in this step were also used for later steps, referred to in the appropriate sections.Areas of pristine ecosystems (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{cur}}}}}}}),({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}})) and LU-types (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}))The potential pristine ecosystem area ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) is defined as the cell’s entire terrestrial area (excluding water bodies as defined by the land mask of the HYDE 3.2.1 database49). As the area of pristine ecosystems currently found in each grid cell (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{cur}}}}}}})), we used the proportion of ({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) marked as wilderness and non-productive/ snow areas as described below. The area of each of the 45 LU-types within each grid cell (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}})) was extracted from respective land cover and LU maps applying the approach outlined in ref. 50 – with 2010 as year of reference wherever possible (Supplementary Data 2).Following ref. 50, builtup land, total cropland (including annual and permanent crops/ plantations), permanent pastures (areas used as pastures for more than five years) and rangeland (available in the two sub-categories natural and converted) extents were taken from the LU database HYDE 3.2.149 which was adapted to include rural infrastructure areas by assigning 5% of each grid cell’s cropland area to builtup land. We then split the total cropland cover into areas used for 41 different annual and permanent crops by integrating data from the Spatial Production Allocation Model (SPAM) for 201051,52 and adjusting them to cropland extent in the data from ref. 50. To comply with the IUCN habitats classification scheme42, some of these crops were grouped into the plantation category (permanent crops), while the remainder was grouped into the cropland category (annual crops; see Supplementary Data 2 for details).Wilderness areas were derived from the combination of human footprint data, i.e., a spatially explicit inventory of human artefact density available for 1993 and 200953,54 and intact forest landscape data for 2000 and 201355. Core wilderness areas without human use were defined as having a value of zero human footprint and, in forests, being part of an intact forest landscape55. Within forests, the additional category of peripheral wilderness was introduced for areas where either only zero human footprint is recorded, or only an intact forest landscape exists.The area remaining in each grid cell after allocating the above land cover types represents area covered by used forests and other land with mixed land uses56. Hence, in addition to the approach in ref. 50, forests were split into deciduous and coniferous forests based on the description of the ESA CCI land cover categories57. This distinction was necessary for the differentiated allocation of wood harvest (see below). A further refinement was applied by identifying plantation forests, defined as areas in non-forest biomes converted to forests for forestry and areas in forest biomes converted to non-native forest types58, which were linked to the IUCN habitat class plantations (Supplementary Data 2).As in ref. 50, the remaining area not allocated to any of the land cover or LU types above is denoted as “other land, maybe grazed”56. These lands, typically treeless or bearing scattered tress, were allocated to converted grasslands on areas that potentially carry forests or to natural grassland on areas where the potential vegetation would not consist of forests25.To arrive at the six broad LU-type aggregates compatible with the IUCN and Birdlife habitat affiliation schemes42,46 and PREDICTS categories21 (needed for quantifying LU-intensity effects, see below in section “Affinity parameter” for details), we rearranged and aggregated the described LU layers as needed (see Supplementary Data 2 for an overview). (a) Builtup remained as described above. (b) Cropland was defined as annual crops, covering the respective 29 SPAM categories plus fodder. (c) Pastures were defined as areas where pristine ecosystems were converted to grasslands and includes permanent pastures and converted rangelands from HYDE 3.2.149, plus those parts of “other land maybe grazed” located in forest25. (d) Grazing land was defined as natural or near-natural areas where grazing occurs and includes natural rangelands from HYDE 3.2.149, plus 50% of each grid cell’s open forest area and 25% of each grid cell’s peripheral wilderness area, the latter two assumed to be only occasionally grazed and hence given low grazing intensity (see below), plus those parts of “other land maybe grazed” located in non-forest25. (e) Forests were defined as forests where forestry occurs and includes 100% of each grid cell’s closed forest area, 50% of each grid cell’s open forest area, and 25% of each grid cell’s peripheral wilderness area, the latter two assumed to be only occasionally used for forestry and given low intensity (see below). (f) Plantations were defined as areas where pristine ecosystems were converted into plantation-like LU and include the 11 SPAM categories representing permanent / plantation crops, plus used forests identified by ref. 58 as plantations (see above). As stated above, these aggregated broad LU-types were needed to align the different LU categorizations used in the different data sources with each other. The effects on biodiversity were then calculated on each of the 45 LU-types and afterwards aggregated to the six broad LU-types to give a better overview.Continuous LU-intensity indices (({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}))We constructed continuous LU-intensity indices ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) for each of the 45 LU-types based on gridded management descriptors15. For this purpose, we used two different sets of intensity indicators (called Set 1 and Set 2) to compare and combine their impact on predicted species loss. We used two indicator sets to account for the multidimensional nature of LU-intensity9,12 and to include a wide range of available data products. For an overview of which data products and assumptions went into the individual sets, please refer to Supplementary Data 2.Set 1Set 1 is taken from the human appropriation of net primary production (HANPP) framework, a socioecological indicator basically describing the LU mediated extraction of biotic resources in the context of global biogeochemical cycles23. We used the ratio of HANPPharv to NPPpot as a systemic metric to assess LU-intensity12,22, with HANPPharv being harvested or extracted biomass and NPPpot being NPP of potential natural vegetation, i.e. the vegetation existing under current climate conditions in the hypothetical absence of LU23. The ratio HANPPharv/NPPpot relates harvest to the productivity potential of the land where the harvest takes place and is, thus, robust against geographic differences in natural productivity. As it is related to energy availability in ecosystem food chains, it may be linked to the species-energy relationship, the strongest correlate of spatial biodiversity patterns at larger scales59.For calculating NPPpot, LPJ-GUESS60 version 4.0.1 was used in its standard configuration but with nitrogen limitation disabled and forced by the CRU-NCEP climate data61,62 aggregated from 6-hourly to monthly fields.HANPPharv of all LU-types except builtup was calculated based on the FAOSTAT database by principally accounting total biomass flows via conversion and expansion factors as outlined in ref. 63. As a special case, HANPPharv of built-up was assumed to be half of the actual NPP, which was defined as 1/3 of the potential vegetation in ref. 64. This results in a constant intensity on built-up land of ~17% of NPPpot.HANPPharv of permanent and non-permanent crops was spatially downscaled following 40 permanent and non-permanent crop-specific production patterns from the Spatially-Disaggregated Crop Production Statistics Database (SPAM52), merging minor SPAM categories such as “robusta coffee” and “arabica coffee” to ensure consistency with FAOSTAT reporting. Additionally, we added the LU-type fodder, which was downscaled following NPPpot patterns.Harvest of natural and plantation forest is reported by FAOSTAT in the four categories industrial roundwood, wood fuel, and coniferous and deciduous. We allocated industrial roundwood harvest to closed forests, while we split wood fuel harvest in proportion to productivity between closed and open forests, independently for deciduous and coniferous forests, respectively. For Set 1, we assumed forestry harvest to follow the patterns of forest NPPpot65. These intensity definitions were used for both natural and plantation forest.Reported harvest on grazing land and pastures was allocated following patterns of aboveground NPP accessible for grazing as reported in ref. 63. Following the assumption that systems with low natural productivity allow for a lower maximum harvest than systems with high productivity, we assigned a maximum harvest intensity of 40% at a level of accessible NPP of 20 gC/m² and increased this linearly to a maximum grazing intensity of 80% at 250 gC/m². Such, harvest was concentrated on grazing land and pastures with high productivity. In cases where the calculated national grazing land and pasture harvest demand surpassed NPP availability on grassland, we used information on fertilization rates on grassland66 to either adjust NPP or harvest data: NPP was boosted in countries where more than 5% of overall fertilizer consumption was applied to grasslands, while countries where no relevant fertilization of grasslands occurred, the reported harvest demand was reduced accordingly, assuming it will be met from other sources. This intensity definition was applied to both (natural) gazing land and (converted) pastures.Set 2For the LU-intensity indicator Set 2, we used published data from different sources. For cropland we used the input metric nitrogen application rates (in kg N/ha of cropland)12,22, available for 17 major non-permanent crops67,68. For crops from the SPAM categories (see above) not covered by these data, we used the within-grid-cell area-weighted average of other crops in the same cell. For areas designated as cropland in our data (see above) but not in the available N application data, we assumed national average values of the respective crop.For pastures and grazing land, we used gridded livestock information69. We used information on the typical weight per animal to calculate livestock units70 and aggregated the data for all ruminant species (buffalo, horses, cattle, sheep, goats). This data on livestock numbers per grid cell was then divided by land area per grid cell to arrive at livestock densities, which were applied to the extent of grazing land and pastures. Please note that this dataset contains information on the number of livestock (per species group) per area in a grid cell and thereby differs from the grazing intensity metric applied in Set 171, as grazing animals may be fed from other sources than grassland72.For builtup, we aggregated a 1 km built-up area density map for 201473 to the target resolution of 5 arc min and used it as is as intensity indicator.For natural and plantation forest, we used the same data as described above for Set 1, but we assumed forestry harvest to follow another pattern. We calculated the difference between potential and actual biomass stocks74 and allocated forestry harvest within each country according to these patterns, i.e., the share of national forest harvest allocated to a forest cell corresponds to its share in the national difference between potential and actual biomass stocks.Scaling of LU-intensity indicesFor the purpose of applying linear functions on species richness loss caused by LU-intensity (see below, affinity parameter), we scaled each LU-intensity indicator to values between 0 and 1, with 0 being no intensity (hypothetical) and 1 being the intensity threshold above which an increase of intensity causes no further increase of species loss. This threshold is not necessarily the highest recorded value of an intensity indicator, as effects may be regionally variable. We therefore winsorized some LUI indicators to that intensity threshold before scaling them (dividing by this threshold). These thresholds were defined as follows.In Set 1, maximum intensity was assumed to be reached at harvesting 100% of NPPpot on cropland.In forests (natural and plantation), maximum intensity was derived from ref. 75, which limits sustainably harvestable aboveground biomass in forests to 30% of NPPpot. In concordance with the HANPP framework, we included the belowground biomass destroyed by forestry using biome-specific factors76.On grazing land and pastures, maximum intensity was defined as removal of all NPP accessible for grazing. This considers only the aboveground and non-woody parts of NPPpot. The maximum removable aboveground share was estimated as 50% of NPPpot, and the proportion of non-woody vegetation was estimated as 30% (in closed-canopy land cover types) or 100% (on open land cover types)71. HANPPharv/NPPpot was assumed to be at its maximum intensity level when the maximum level of grazing intensity, as described above, was reached. The resulting thresholds are in line with literature77,78, and assume that maximum intensities will be reached faster in systems with low natural productivity.In Set 2, for all crop types (permanent and non-permanent) except legumes, N application rates were capped at 150 kg N/ha, i.e., we assumed that 150 kg N/ha was the maximum LU-intensity on cropland, beyond which no further species richness loss occurs, i.e., after which an increase of N application rates causes no further increase in species loss based on ref. 79. For legumes, under the assumption that they need less N fertilizer due to their N-fixing capabilities, we assumed the following cap values, based on information provided in ref. 80: beans and lentils at 110 kg N/ha, chickpeas at 100 kg N/ha, soybean at 70 kg N/ha and cowpeas, pigeon peas and other pulses at kg N/ha 90.For pastures and grazing land, maximum intensity was defined as the per biome 80th percentile of livestock-density.The intensity of builtup area was not winsorized.Affinity parameter (({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}))The affinity parameter ({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) can be regarded as a LU-intensity dependent weighting factor for the area of each of the 45 LU-types used here. For low affinity, i.e., a small fraction of native species is left due to LU, the area of this LU-type (({{{{{{rm{A}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) in formula 1) is down-weighted, resulting in higher species loss ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}) (and vice versa). The affinity parameter consists of two terms, (a) ({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}), the fraction of species affiliated with a given LU-type, and (b) ({{{{{{rm{f}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}), the fraction of ({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) that remains when LU-intensity (({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}})) rises to a particular level.The fraction of species affiliated with a certain LU-type under minimal ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) (({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}})) is based on the habitat affiliation information taken from the IUCN Red List API45 and BirdLife data46 cross-tabulated with our mapped LU-types (Supplementary Data 2). We calculated ({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) by dividing the number of species affiliated with a certain LU-type (({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}^{{{{{{{rm{pot}}}}}}; {{{{{rm{LU}}}}}}}})) by the number of native species expected in this cell under pristine ecosystem conditions (({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}})) as$${{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}=frac{{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}^{{{{{{{rm{pot}}}}}}; {{{{{rm{LU}}}}}}}}}{{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}}$$
    (2)
    Please note that for the two unconverted broad LU-types grazing land and forests, respectively (see above), we assumed no land conversion prior to its use, leading to ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}^{{{{{{{rm{pot}}}}}}; {{{{{rm{LU}}}}}}}}={{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}). We further assumed that the whole fraction of LU-type affiliated species ({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) are present in a given LU-type as long as ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) is minimal (here 0.83, we argue that extrapolation outside the measured intensity range is uncertain, and that an increase in LUI above 0.83 (i.e., Intense) might not necessarily result in even stronger effects on SR. See Supplementary Fig. 5, which illustrates the results of these considerations and shows the continuous effect of ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) on SR used in this study.The affinity parameter ({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) was then calculated as follows and inserted into formula 1 (cSAR model).$${{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}={left({{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}right)}^{1/{{{{{{rm{z}}}}}}}_{{{{{{rm{n}}}}}}}}times {left({{{{{{rm{f}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}right)}^{1/{{{{{{rm{z}}}}}}}_{{{{{{rm{n}}}}}}}}$$
    (5)
    Species loss caused by LU-intensity (({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}{{{{{rm{int}}}}}}}))In order to calculate the relative impact of LU-intensity on species richness, we re-ran the model with ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) = 0 in all grid cells and LU types, thereby effectively setting ({{{{{{rm{f}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) = 1 and ({{{{{{rm{h}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}={{{{{{rm{r}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}). The results of this model can be considered as delivering the land conversion effect without any possible enhancement by intensification. In addition, we designed a hypothetical, back-of-the-envelope intensification scenario where ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) = 1 in all grid cells and LU-types.The contribution of intensity to the species richness loss was then calculated as$${{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}{{{{{rm{int}}}}}}}=left({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}-{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{{rm{loss}}}}}}; {{{{{rm{conv}}}}}}}}right)/{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}$$
    (6)
    With ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{{rm{loss}}}}}}; {{{{{rm{conv}}}}}}}}) being the results of the ({{{{{{rm{LUI}}}}}}}_{{{{{{rm{n}}}}}},{{{{{rm{b}}}}}}}) = 0 model and ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}) from Eq. (1).Native area-of-habitat loss of individual speciesThe cSAR model calculates by how many species the native species pool is reduced in response to LU in each 5 arcmin grid cell. However, it does not identify the individual species lost. To estimate each species’ native AOH loss, we randomly drew the predicted number of species lost from the native species pool of each cell.First, we rounded the number of species lost as calculated by the cSAR model to the next integer for losses from both conversion (({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{{rm{loss}}}}}}; {{{{{rm{conv}}}}}}}})) and intensification (here taken as ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{loss}}}}}}}-{{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{{rm{loss}}}}}}; {{{{{rm{conv}}}}}}}}), see section above: “Species loss caused by LU-intensity”). To avoid rounding all values below 0.5 to 0, and, hence, to underestimate low levels of species loss, particularly in species-poor regions, we used a two-step rounding routine. First, prior to actual rounding, we randomly decided whether a number is rounded to the next higher or lower integer, with the likelihood of either decision depending on the decimal number’s (positive or negative) distance to 0.5 (i.e., the decimal number gave the likelihood of rounding up). Second, we took the species list used to generate ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}) (see above under potential species richness) and modified it to either contain only species affiliated or unaffiliated with each LU-type, yielding two species lists for each grid cell and LU-type, respectively. The list of species affiliated with a particular LU-type was then used to select species predicted to get lost due to intensification, while the list of species not affiliated with it was used to select species lost due to conversion.From each grid cell, we then randomly drew as many species from these lists as determined by the rounding routine above, considering each LU-type and whether the number of lost species was caused by intensification or conversion. However, in each cell, each species could only be drawn once, independently of whether it was affiliated with several LU-types. As a consequence, the order in which LU-types are considered when drawing species is relevant for the outcome of the calculation. For instance, species simultaneously unaffiliated with cropland and affiliated with natural forest may never be drawn in response to intensification of natural forest if losses due to conversion into cropland are always handled first. Therefore, we randomly iterated the sequence by which LU-types were considered, i.e., the order of LU-types, in the random draw routine in each of 100 repeated runs.We repeated the random-draws 100 times to yield a representative sample and processed the resulting 100 lists of species-per-cell losses in the following way. For each of the 100 runs, we summed the areas of all cells each species was drawn from, i.e., predicted to be lost, across all LU-types and within individual LU-types, yielding 100 area sums per species (one per run). From these 100 areas, we calculated the mean and the 0.025th and 0.975th quantiles as 95% confidence intervals (CIs). The means and CIs were then divided by the species’ global AOH (sums of cell areas in native range), thereby yielding the proportional global AOH loss attributable to current LU in general, and to different LU-types or land conversion vs. LU-intensity in particular.Description/ presentation of resultsAll cSAR model calculations were based on global land use maps that distinguish 45 LU-types as described above. For the sake of simplicity, we present results aggregated to the six broad LU-types cropland, pastures, natural grazing land, built-up, plantations, and forests (natural/ near-natural forest under forestry; see Supplementary Data 2). All calculated SR decreases are expressed in percentage losses relative to ({{{{{{rm{S}}}}}}}_{{{{{{rm{g}}}}}},{{{{{rm{n}}}}}}}^{{{{{{rm{pot}}}}}}}).Summary statistics mentioned in the text and Supplementary Data 1, 5 and 6 were calculated as follows. Global, biome-wide and nation-wide average species losses due to conversion, LU-intensity or both were calculated as cell-area weighted means across all cells with native terrestrial vertebrate species either excluding or including wilderness areas (which, for this purpose, are defined as cells where the sum of all LU area equals 0). The percentual land area exceeding a certain threshold of calculated SR decline were calculated by dividing the area sum of all cells exceeding that threshold by the area sum of all cells with native species excluding wilderness.Differences among average AOH losses (across all taxonomic groups) mentioned in the text and Supplementary Data 3 were modelled using generalized linear models assuming a binomial distribution (proportional AOH loss between 0 and 1), each species’ mean AOH loss (mean of 100 random draw runs) as response, and either (a) IUCN categories, (b) land use types, or (c) taxonomic group as predictor variables. Differences between predictor variable levels were then alculated by multiple comparisons via p-values adjusted with the Tukey method. A p-value of  More

  • in

    Generalist herbivore response to volatile chemical induction varies along a gradient in soil salinization

    1.Assadi, A., Pirnalouti, A. G., Malekpoor, F., Teimori, N. & Assadi, L. Impact of air pollution on physiological and morphological characteristics of Eucalyptus camaldulensis Den. J. Food Agric. Environ. 9, 676–679 (2011).
    Google Scholar 
    2.Rai, R., Rajput, M., Agrawal, M. & Agrawal, S. B. Gaseous air pollutants: A review on current and future trends of emissions and impact on agriculture. J. Sci. Res. 55, 77–102 (2011).
    Google Scholar 
    3.Brooker, R. W. Plant-plant interactions and environmental change. New Phytol. 171, 271–284. https://doi.org/10.1111/j.1469-8137.2006.01752.x (2006).Article 
    PubMed 

    Google Scholar 
    4.Jefferies, R. L. & Maron, J. L. The embarrassment of riches: Atmospheric deposition of nitrogen and community and ecosystem processes. Trends Ecol. Evol. 12, 74–78 (1997).CAS 
    Article 

    Google Scholar 
    5.Egerton-Warburton, L. M. & Allen, E. B. Shifts in arbuscular mycorrhizal communities along an anthropogenic nitrogen deposition gradient. Ecol. Appl. 10, 484–496 (2000).Article 

    Google Scholar 
    6.Stiling, P. & Cornelissen, T. How does elevated carbon dioxide (CO2) affect plant herbivore interactions? A field experiment and meta-analysis of CO2-mediated changes on plant chemistry and herbivore performance. Glob. Change Biol. 13, 1823–1842. https://doi.org/10.1111/j.1365-2486.2007.01392.x (2007).ADS 
    Article 

    Google Scholar 
    7.Zvereva, E. L. & Kozlov, M. V. Consequences of simultaneous elevation of carbon dioxide and temperature for plant-herbivore interactions: A metaanalysis. Glob. Change Biol. 12, 27–41. https://doi.org/10.1111/j.1365-2486.2005.01086.x (2006).ADS 
    Article 

    Google Scholar 
    8.Kopper, B. J. & Lindroth, R. L. Effects of elevated carbon dioxide and ozone on the phytochemistry of aspen and performance of an herbivore. Oecologia 134, 95–103. https://doi.org/10.1007/s00442-002-1090-6 (2003).ADS 
    Article 
    PubMed 

    Google Scholar 
    9.Maron, J. L. & Crone, E. Herbivory: Effects on plant abundance, distribution and population growth. Proc. Biol. Sci. 273, 2575–2584. https://doi.org/10.1098/rspb.2006.3587 (2006).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Karban, R. & Baldwin, I. T. Induced Responses to Herbivory (University of Chicago Press, 2007).
    Google Scholar 
    11.Lambers, H. Rising CO2, secondary plant metabolism, plant-herbivore interactions and litter decomposition. Vegetation 104(105), 263–271 (1993).Article 

    Google Scholar 
    12.Poorter, H. et al. The effect of elevated CO2 on the chemical composition and construction costs of leaves of 27 C3 species. Plant Cell Environ. 20, 472–482 (1997).CAS 
    Article 

    Google Scholar 
    13.Thaler, J. S., Stout, M. J., Karban, R. & Duffey, S. S. Exogenous jasmonates simulate insect wounding in tomato plants (Lycopersicon esculentum) in the laboratory and field. J. Chem. Ecol. 22, 1767–1781 (1996).CAS 
    Article 

    Google Scholar 
    14.De Moraes, C. M. et al. Herbivore-infested plants selectively attract parasitoids. Nature 393(6685), 570–573 (1998).ADS 
    Article 

    Google Scholar 
    15.Thaler, J. S. Jasmonate-inducible plant defenses cause increased parasitism of herbivores. Nature 399, 686–688 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    16.Kessler, A. & Baldwin, I. T. Defensive function of herbivore-induced plant volatile emissions in nature. Science 291(5511), 2141–2144 (2001).ADS 
    CAS 
    Article 

    Google Scholar 
    17.Orrock, J., Connolly, B. & Kitchen, A. Induced defences in plants reduce herbivory by increasing cannibalism. Nat. Ecol. Evol. 1, 1205–1207. https://doi.org/10.1038/s41559-017-0231-6 (2017).Article 
    PubMed 

    Google Scholar 
    18.Blande, J. D., Holopainen, J. K. & Niinemets, Ü. Plant volatiles in polluted atmospheres: Stress responses and signal degradation. Plant Cell Environ. 37, 1892–1904. https://doi.org/10.1111/pce.12352 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    19.Bidart-Bouzat, M. G. & Imeh-Nathaniel, A. Global change effects on plant chemical defenses against insect herbivores. J. Integr. Plant Biol. 50, 1339–1354. https://doi.org/10.1111/j.1744-7909.2008.00751.x (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    20.Tao, L., Berns, A. R., Hunter, M. D. & Johnson, M. Why does a good thing become too much? Interactions between foliar nutrients and toxins determine performance of an insect herbivore. Funct. Ecol. 28, 190–196. https://doi.org/10.1111/1365-2435.12163 (2014).Article 

    Google Scholar 
    21.Forieri, I., Hildebrandt, U. & Rostás, M. Salinity stress effects on direct and indirect defence in maize. Environ. Exp. Bot. 122, 68–77. https://doi.org/10.1016/j.envexpbot.2015.09.007 (2016).CAS 
    Article 

    Google Scholar 
    22.Maathuis, F. J. Sodium in plants: Perception, signaling, and regulation of sodium fluxes. J. Exp. Bot. 65, 849–858. https://doi.org/10.1093/jxb/ert326 (2014).CAS 
    Article 
    PubMed 

    Google Scholar 
    23.Rengasamy, P. World salinization with emphasis on Australia. J. Exp. Bot. 57(5), 1017–1023 (2006).CAS 
    Article 

    Google Scholar 
    24.Harmon, J. P. & Daigh, A. L. M. Attempting to predict the plant-mediated trophic effects of soil salinity: A mechanistic approach to supplementing insufficient information. Food Webs 13, 67–77. https://doi.org/10.1016/j.fooweb.2017.02.002 (2017).Article 

    Google Scholar 
    25.Zribi, L. et al. Application of chlorophyll fluorescence for the diagnosis of salt stress in tomato “Solanum lycopersicum (variety Rio Grande)”. Sci. Hortic. 120, 367–372. https://doi.org/10.1016/j.scienta.2008.11.025 (2009).CAS 
    Article 

    Google Scholar 
    26.Farooq, M. et al. Effects, tolerance mechanisms and management of salt stress in grain legumes. Plant Physiol. Biochem. PPB 118, 199–217. https://doi.org/10.1016/j.plaphy.2017.06.020 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    27.Zhang, C. et al. Uptake and translocation of organic pollutants in plants: A review. J. Integr. Agric. 16, 1659–1668. https://doi.org/10.1016/s2095-3119(16)61590-3 (2017).CAS 
    Article 

    Google Scholar 
    28.Dumbroff, E. B. & Cooper, A. W. Effects of salt stress applied in balanced nutrient solution at several stages during growth of tomato. Bot. Gazette 135, 219–224 (1974).Article 

    Google Scholar 
    29.Aucejo-Romero, S., Gómez-Cadenas, A. & Jacas-Miret, J.-A. Effects of NaCl-stressed citrus plants on life-history parameters of Tetranychus urticae (Acari: Tetranychidae). Exp. Appl. Acarol. 33, 55–67 (2004).Article 

    Google Scholar 
    30.Polack, L. A., Pereyra, P. C. & Sarandón, S. J. Effects of plant stress and habitat manipulation on aphid control in greenhouse sweet peppers. J. Sustain. Agric. 35, 699–725. https://doi.org/10.1080/10440046.2011.606489 (2011).Article 

    Google Scholar 
    31.Dombrowski, J. E. Salt stress activation of wound-related genes in tomato plants. Plant Physiol. 132, 2098–2107. https://doi.org/10.1104/pp.102.019927 (2003).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Younginger, B., Barnouti, J. & Moon, D. Interactive effects of mycorrhizal fungi, salt stress, and competition on the herbivores of Baccharis halimifolia. Ecol. Entomol. 34(5), 580–587 (2009).Article 

    Google Scholar 
    33.Orrock, J. L. et al. Plants eavesdrop on cues produced by snails and induce costly defenses that affect insect herbivores. Oecologia 186, 703–710. https://doi.org/10.1007/s00442-018-4070-1 (2018).ADS 
    Article 
    PubMed 

    Google Scholar 
    34.Rodriguez-Saona, C., Chalmers, J. A., Raj, S. & Thaler, J. S. Induced plant responses to multiple damagers: Differential effects on an herbivore and its parasitoid. Oecologia 143, 566–577. https://doi.org/10.1007/s00442-005-0006-7 (2005).ADS 
    Article 
    PubMed 

    Google Scholar 
    35.Connolly, B. M., Guiden, P. W. & Orrock, J. L. Past freeze–thaw events on Pinus seeds increase seedling herbivory. Ecosphere 8, e01748. https://doi.org/10.1002/ecs2.1748 (2017).Article 

    Google Scholar 
    36.Ainsworth, E. A. & Gillespie, K. M. Estimation of total phenolic content and other oxidation substrates in plant tissues using Folin–Ciocalteu reagent. Nat. Protoc. 2, 875–877. https://doi.org/10.1038/nprot.2007.102 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    37.Connolly, B., Ripka, M., & Ebersole, W. Microclimate measurements (15-minute intervals) at Fish Lake Environmental Education Center (Eastern Michigan University; Lapeer County, Michigan, USA), Dryad, Dataset. https://doi.org/10.5061/dryad.3n5tb2rh4 (2021).38.Edwards, P. J. & Wratten, S. D. Wound induced defenses in plants and their consequences for patterns of insect grazing. Oecologia 59, 88–93 (1983).ADS 
    CAS 
    Article 

    Google Scholar 
    39.R Core Team. R Foundation for Statistical Computing. R: A language and environment for statistical computing. Vienna, Austria. https://www.R-project.org/ (2019)40.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67(1), 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

    Google Scholar 
    41.Therneau, T. A package for survival analysis in R. R package version 3.2-11. https://CRAN.R-project.org/package=survival (2021)42.Kassambara, A., Kosinski, M., & Biecek, P. survminer: Drawing survival curves using ‘ggplot2’. R package version 0.4.9. https://CRAN.R-project.org/package=survminer (2021)43.Wickham, H., François, R., Henry, L., & Müller, K. dplyr: A grammar of data manipulation. R package version 1.0.7. https://CRAN.R-project.org/package=dplyr (2021)44.Lenth, R. V. emmeans: Estimated marginal means, aka least-squares means. R package version 1.6.2-1. https://CRAN.R-project.org/package=emmeans (2021)45.Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2016).Book 

    Google Scholar 
    46.Katerji, N., van Hoorn, J. W., Hamdy, A. & Mastrorilli, M. Salinity effect on crop development and yield, analysis of salt tolerance according to several classification methods. Agric. Water Manag. 62, 37–66. https://doi.org/10.1016/s0378-3774(03)00005-2 (2003).Article 

    Google Scholar 
    47.Snell-Rood, E. C., Espeset, A., Boser, C. J., White, W. A. & Smykalski, R. Anthropogenic changes in sodium affect neural and muscle development in butterflies. Proc. Natl. Acad. Sci. U.S.A. 111, 10221–10226. https://doi.org/10.1073/pnas.1323607111 (2014).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.Negrão, S., Schmöckel, S. M. & Tester, M. Evaluating physiological responses of plants to salinity stress. Ann. Bot. 119, 1–11. https://doi.org/10.1093/aob/mcw191 (2017).Article 
    PubMed 

    Google Scholar 
    49.Mogren, C. L. & Trumble, J. T. The impacts of metals and metalloids on insect behavior. Entomol. Exp. Appl. 135, 1–17. https://doi.org/10.1111/j.1570-7458.2010.00967.x (2010).CAS 
    Article 

    Google Scholar 
    50.Schultz, J. C. Habitat selection and foraging tactics of caterpillars in heterogeneous trees. In Variable Plants and Herbivores in Natural and Managed Systems (eds Denno, R. F. & McClure, M. S.) 61–90 (Academic Press Inc, 1983).Chapter 

    Google Scholar 
    51.Zalucki, M. P., Clarke, A. R. & Malcolm, S. B. Ecology and behavior of first instar larval lepidoptera. Annu. Rev. Entomol. 47, 361–393 (2002).CAS 
    Article 

    Google Scholar 
    52.Elvira, S., Williams, T. & Caballero, P. Juvenile hormone analog technology: Effects on larval cannibalism and the production of Spodoptera exigua (Lepidoptera: Noctuidae) nucleopolyhedrovirus. J. Econ. Entomol. 103, 577–582. https://doi.org/10.1603/ec09325 (2010).Article 
    PubMed 

    Google Scholar 
    53.Elderd, B. D. Bottom-up trait-mediated indirect effects decrease pathogen transmission in a tritrophic system. Ecology 100, e02551 (2019).Article 

    Google Scholar 
    54.Mitchell, T. S., Shephard, A. M., Kalinowski, C. R., Kobiela, M. E. & Snell-Rood, E. C. Butterflies do not alter oviposition or larval foraging in response to anthropogenic increases in sodium. Anim. Behav. 154, 121–129. https://doi.org/10.1016/j.anbehav.2019.06.015 (2019).Article 

    Google Scholar 
    55.Beaton, L. L. & Dudley, S. A. Tolerance to salinity and manganese in three common roadside species. Int. J. Plant Sci. 165, 37–51 (2004).CAS 
    Article 

    Google Scholar 
    56.Kim, H. et al. Effect of methyl jasmonate on phenolics, isothiocyanate, and metabolic enzymes in radish sprout (Raphanus sativus L.). J. Agric. Food Chem. 54, 7263–7269 (2006).CAS 
    Article 

    Google Scholar 
    57.Inbar, M., Doostdar, H. & Mayer, R. T. Suitability of stressed and vigorous plants to various insect herbivores. Oikos 94, 228–235 (2001).Article 

    Google Scholar 
    58.English-Loeb, G., Stout, M. J. & Duffey, S. S. Drought stress in tomatoes: Changes in plant chemistry and potential nonlinear consequences for insect herbivores. Oikos 79, 456–468 (1997).Article 

    Google Scholar 
    59.Welti, E. A. R. & Kaspari, M. Sodium addition increases leaf herbivory and fungal damage across four grasslands. Funct. Ecol. https://doi.org/10.1111/1365-2435.13796 (2021).Article 

    Google Scholar 
    60.Caparrotta, S. et al. Induction of priming by salt stress in neighboring plants. Environ. Exp. Bot. 147, 261–270. https://doi.org/10.1016/j.envexpbot.2017.12.017 (2018).CAS 
    Article 

    Google Scholar 
    61.Nedjimi, B. & Daoud, Y. Cadmium accumulation in Atriplex halimus subsp. Schweinfurthii and its influence on growth, proline, root hydraulic conductivity and nutrient uptake. Flora Morphol. Distrib. Funct. Ecol. Plants 204, 316–324. https://doi.org/10.1016/j.flora.2008.03.004 (2009).Article 

    Google Scholar 
    62.Methenni, K. et al. Salicylic acid and calcium pretreatments alleviate the toxic effect of salinity in the Oueslati olive variety. Sci. Hortic. 233, 349–358. https://doi.org/10.1016/j.scienta.2018.01.060 (2018).CAS 
    Article 

    Google Scholar 
    63.Song, Y. Y. et al. Hijacking common mycorrhizal networks for herbivore-induced defence signal transfer between tomato plants. Sci. Rep. 4, 3915. https://doi.org/10.1038/srep03915 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    64.Evelin, H., Kapoor, R. & Giri, B. Arbuscular mycorrhizal fungi in alleviation of salt stress: A review. Ann. Bot. 104, 1263–1280. https://doi.org/10.1093/aob/mcp251 (2009).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Mangrove diversity is more than fringe deep

    1.Tomlinson, P. B. The Botany of Mangroves. (Cambridge University Press, 1994).2.Carrasquilla-Henao, M. & Juanes, F. Mangroves enhance local fisheries catches: A global meta-analysis. Fish Fish. 18, 79–93 (2017).
    Google Scholar 
    3.del Valle, A., Eriksson, M., Ishizawa, O. A. & Miranda, J. J. Mangroves protect coastal economic activity from hurricanes. Proc. Natl. Acad. Sci. U.S.A. 117, 265–270 (2020).PubMed 

    Google Scholar 
    4.Zhang, K. et al. The role of mangroves in attenuating storm surges. Estuar. Coast. Shelf Sci. 102–103, 11–23 (2012).ADS 

    Google Scholar 
    5.Menendez, P., Losada, I. J., Torres-Ortega, S., Narayan, S. & Beck, M. W. The global flood protection benefits of mangroves. Sci. Rep. 10, 1–11 (2020).
    Google Scholar 
    6.Macreadie, P. I. et al. The future of Blue Carbon science. Nat. Commun. 10, 1–13 (2019).
    Google Scholar 
    7.Valiela, I., Bowen, J. L. & York, J. K. Mangrove forests: One of the world’s threatened major tropical environments. Bioscience 51, 807–815 (2001).
    Google Scholar 
    8.Bryan-Brown, D. N. et al. Global trends in mangrove forest fragmentation. Sci. Rep. https://doi.org/10.1038/s41598-020-63880-1 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    9.Duke, N. C. et al. A world without mangroves ?. Science 317, 41–43 (2007).CAS 

    Google Scholar 
    10.Friess, D. A. et al. The state of the world’s mangrove forests: Past, present, and future. Annu. Rev. Environ. Resour. 44, 89–115 (2019).
    Google Scholar 
    11.Friess, D. A. et al. Mangroves give cause for conservation optimism, for now. Curr. Biol. 30, R153–R154 (2020).CAS 
    PubMed 

    Google Scholar 
    12.Reynolds, L. K., McGlathery, K. J. & Waycott, M. Genetic diversity enhances restoration success by augmenting ecosystem services. PLoS ONE 7, 1–7 (2012).
    Google Scholar 
    13.Lowenfeld, R. & Klekowski, E. J. Mangrove genetics. I. Mating system and mutation rates of rhizophora mangle in Florida and San Salvador Island, Bahamas. Int. J. Plant Sci. 153, 394–399 (1992).14.Kennedy, J. P., Sammy, J. M., Rowntree, J. K. & Preziosi, R. F. Mating system variation in neotropical black mangrove, Avicennia germinans, at three spatial scales towards an expanding northern distributional limit. Estuarine Coastal Shelf Sci. https://doi.org/10.1016/j.ecss.2020.106754 (2020).Article 

    Google Scholar 
    15.Van Der Stocken, T. et al. Impact of landscape structure on propagule dispersal in mangrove forests. Mar. Ecol. Prog. Ser. 524, 95–106 (2015).ADS 

    Google Scholar 
    16.Hamilton, J. F., Osman, R. W. & Feller, I. C. Modeling local effects on propagule movement and the potential expansion of mangroves and associated fauna: Testing in a sub-tropical lagoon. Hydrobiologia 803, 173–187 (2017).
    Google Scholar 
    17.Binks, R. M. et al. Habitat discontinuities form strong barriers to gene flow among mangrove populations, despite the capacity for long-distance dispersal. Divers. Distrib. 25, 298–309 (2019).
    Google Scholar 
    18.Ngeve, M. N., Van der Stocken, T., Sierens, T., Koedam, N. & Triest, L. Bidirectional gene flow on a mangrove river landscape and between-catchment dispersal of Rhizophora racemosa (Rhizophoraceae). Hydrobiologia 790, 93–108 (2017).
    Google Scholar 
    19.Cisneros-de la Cruz, D. J. et al. Short-distance barriers affect genetic variability of Rhizophora mangle in the Yucatan Peninsula. Ecol. Evolut. https://doi.org/10.1002/ece3.4575 (2018).Article 

    Google Scholar 
    20.Kennedy, J. P. et al. Postglacial expansion pathways of red mangrove Rhizophora mangle, in the Caribbean Basin and Florida. Am. J. Bot. 103, 260–276 (2016).PubMed 

    Google Scholar 
    21.Wee, A. K. S. et al. Vicariance and oceanic barriers drive contemporary genetic structure of widespread mangrove species Sonneratia alba. J. Sm Indo-West Pac. For. 8, 1–21 (2017).
    Google Scholar 
    22.Iuit, L. R. C. et al. Genetic structure and connectivity of the red mangrove at different geographic scales through a complex transverse hydrological system from freshwater to marine ecosystems. Diversity 12, 113 (2020).
    Google Scholar 
    23.Ngeve, M. N., Van der Stocken, T., Menemenlis, D., Koedam, N. & Triest, L. Hidden founders? Strong bottlenecks and fine-scale genetic structure in mangrove populations of the Cameroon Estuary complex. Hydrobiologia 803, 189–207 (2017).
    Google Scholar 
    24.Triest, L. et al. Channel network structure determines genetic connectivity of landward–seaward Avicennia marina populations in a tropical bay. Ecol. Evol. 10, 12059–12075 (2020).PubMed 
    PubMed Central 

    Google Scholar 
    25.Canty, S. W. J., Fox, G., Rowntree, J. K. & Preziosi, R. F. Genetic structure of a remnant Acropora cervicornis population. Sci. Rep. 11, 1–9 (2021).
    Google Scholar 
    26.Kettenring, K. M., Mossman, B. N., Downard, R. & Mock, K. E. Fine-scale genetic diversity and landscape-scale genetic structuring in three foundational bulrush species: Implications for wetland revegetation. Restor. Ecol. 27, 408–420 (2019).
    Google Scholar 
    27.Mijangos, J. L., Pacioni, C., Spencer, P. B. S. & Craig, M. D. Contribution of genetics to ecological restoration. Mol. Ecol. 24, 22–37 (2015).PubMed 

    Google Scholar 
    28.Ross, M. S. et al. Early post-hurricane stand development in Fringe mangrove forests of contrasting productivity. Plant Ecol. 185, 283–297 (2006).
    Google Scholar 
    29.Kennedy, J. P. et al. Hurricanes overcome migration lag and shape intraspecific genetic variation beyond a poleward mangrove range limit. Mol. Ecol. https://doi.org/10.1111/mec.15513 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.NOAA. Historical Hurricane Tracks. https://coast.noaa.gov/hurricanes/ (National Hurricane Center | National Oceanic and Atmospheric Administration).31.Cahoon, D. R. et al. Mass tree mortality leads to mangrove peat collapse at Bay Islands, Honduras after Hurricane Mitch. J. Ecol. 91, 1093–1105 (2003).
    Google Scholar 
    32.Cannicci, S. et al. Faunal impact on vegetation structure and ecosystem function in mangrove forests: A review. Aquat. Bot. 89, 186–200 (2008).
    Google Scholar 
    33.Krauss, K. W. et al. Environmental drivers in mangrove establishment and early development: A review. Aquat. Bot. 89, 105–127 (2008).
    Google Scholar 
    34.Clarke, P. J. Effects of experimental canopy gaps on mangrove recruitment: Lack of habitat partitioning may explain stand dominance. J. Ecol. 92, 203–213 (2004).
    Google Scholar 
    35.Sandoval-Castro, E. et al. Post-glacial expansion and population genetic divergence of Mangrove species Avicennia germinans (L.) stearn and Rhizophora mangle L. along the Mexican coast. PLoS ONE 9, 113 (2014).
    Google Scholar 
    36.Rabinowitz, D. Dispersal properties of Mangrove propagules. Biotropica 10, 47–57 (1978).
    Google Scholar 
    37.Chollett, I. et al. A case for redefining the boundaries of the Mesoamerican reef ecoregion. Coral Reefs https://doi.org/10.1007/s00338-017-1595-4 (2017).Article 

    Google Scholar 
    38.Haddad, N. M. et al. Habitat fragmentation and its lasting impact on Earth’s ecosystems. Sci. Adv. 1, 1–10 (2015).
    Google Scholar 
    39.Jump, A. S. & Peñuelas, J. Genetic effects of chronic habitat fragmentation in a wind-pollinated tree. Proc. Natl. Acad. Sci. U.S.A. 103, 8096–8100 (2006).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Jalonen, R., Hong, L. T., Lee, S. L., Loo, J. & Snook, L. Integrating genetic factors into management of tropical Asian production forests: A review of current knowledge. For. Ecol. Manag. 315, 191–201 (2014).
    Google Scholar 
    41.Pacioni, C., Trocini, S., Wayne, A. F., Rafferty, C. & Page, M. Integrating population genetics in an adaptive management framework to inform management strategies. Biodivers. Conserv. 29, 947–966 (2020).
    Google Scholar 
    42.Van der Stocken, T. et al. A general framework for propagule dispersal in mangroves. Biol. Rev. 94, 1547–1575 (2019).PubMed 

    Google Scholar 
    43.Bologna, P. A. X. et al. Lingering impacts of Hurricane Hugo on Rhizophora mangle (Red Mangrove) population genetics on St. John, USVI. Diversity 11, 1–14 (2019).
    Google Scholar 
    44.Cerón-Souza, I., Bermingham, E., McMillan, W. O. & Jones, F. A. Comparative genetic structure of two mangrove species in Caribbean and Pacific estuaries of Panama. BMC Evol. Biol. 12, 205 (2012).PubMed 
    PubMed Central 

    Google Scholar 
    45.Núñez-Farfán, J. et al. Genetic divergence among Mexican populations of red mangrove (Rhizophora mangle): Geographic and historic effects. Evol. Ecol. Res. 4, 1049–1064 (2002).
    Google Scholar 
    46.Coleman, M. A. et al. Restore or redefine: Future trajectories for restoration. Front. Mar. Sci. 7, 1–12 (2020).
    Google Scholar 
    47.Breed, M. F. et al. Priority actions to improve provenance decision-making. Bioscience 68, 510–516 (2018).
    Google Scholar 
    48.Breed, M. F. et al. The potential of genomics for restoring ecosystems and biodiversity. Nat. Rev. Genet. 20, 615–628 (2019).CAS 
    PubMed 

    Google Scholar 
    49.Kandil, F. E., Grace, M. H., Seigler, D. S. & Cheeseman, J. M. Polyphenolics in Rhizophora mangle L. leaves and their changes during leaf development and senescence. Trees 18, 518–528 (2004).CAS 

    Google Scholar 
    50.Sahu, S. K., Thangaraj, M. & Kathiresan, K. DNA extraction protocol for plants with high levels of secondary metabolites and polysaccharides without using liquid nitrogen and phenol. ISRN Mol. Biol. 2012, 1–6 (2012).
    Google Scholar 
    51.Wang, S., Meyer, E., Mckay, J. K. & Matz, M. V. 2b-RAD: A simple and flexible method for genome-wide genotyping. Nat. Methods 9, 808–810 (2012).CAS 
    PubMed 

    Google Scholar 
    52.Guo, Y. et al. An improved 2b-RAD approach (I2b-RAD) offering genotyping tested by a rice (Oryza sativa L.) F2 population. BMC Genomics 15, 1–13 (2014).CAS 

    Google Scholar 
    53.Eaton, D. A. R. & Overcast, I. ipyrad: Interactive assembly and analysis of RADseq datasets. Bioinformatics 36, 2592–2594 (2020).CAS 
    PubMed 

    Google Scholar 
    54.Xu, S. et al. The origin, diversification and adaptation of a major mangrove clade (Rhizophoreae) revealed by whole-genome sequencing. Natl. Sci. Rev. 4, 721–734 (2017).CAS 
    PubMed 

    Google Scholar 
    55.Marandel, F. et al. Estimating effective population size using RADseq: Effects of SNP selection and sample size. Ecol. Evol. 10, 1929–1937 (2019).
    Google Scholar 
    56.Team, R. C. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2021).57.Jombart, T. & Ahmed, I. adegenet 1.3–1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).58.Kamvar, Z. N., Tabima, J. F. & Grünwald, N. J. Poppr : An R package for genetic analysis of populations with clonal, partially clonal, and/or sexual reproduction. PeerJ 2, e281 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    59.Rousset, F. GENEPOP’007: A complete re-implementation of the GENEPOP software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106 (2008).PubMed 

    Google Scholar 
    60.Garnier-Géré, P. & Chikhi, L. Population subdivision, Hardy-Weinberg equilibrium and the Wahlund effect. Els. https://doi.org/10.1002/9780470015902.a0005446.pub3 (2013).Article 

    Google Scholar 
    61.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332 (2009).PubMed 
    PubMed Central 

    Google Scholar 
    63.Earl, D. A. & vonHoldt, B. M. STRUCTURE HARVESTER: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).64.Pritchard, J. K. & Wen, W. Documentation for Structure Software: Version 2.2. http://pritch.bsd.uchicago.edu (2002).65.Vähä, J. P., Erkinaro, J., Niemelä, E. & Primmer, C. R. Life-history and habitat features influence the within-river genetic structure of Atlantic salmon. Mol. Ecol. 16, 2638–2654 (2007).PubMed 

    Google Scholar 
    66.Caliński, T. & Harabasz, J. A dendrite method for cluster analysis. Commun. Stat. 3, 1–27 (1974).MathSciNet 
    MATH 

    Google Scholar 
    67.Meirmans, P. G. genodive version 3.0: Easy-to-use software for the analysis of genetic data of diploids and polyploids. Mol. Ecol. Resour. 20, 1126–1131 (2020).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    68.Peakall, R. & Smouse, P. E. GenAlEx 6.5: Genetic analysis in Excel. Population genetic software for teaching and research–An update. Bioinformatics 28, 2537–2539 (2012).69.Smouse, P. E. & Peakall, R. Spatial autocorrelation analysis of individual multiallele and multilocus genetic structure. Heredity 82, 561–573 (1999).PubMed 

    Google Scholar 
    70.Peakall, R., Ruibal, M. & Lindenmayer, D. B. Spatial autocorrelation analysis offers new insights into gene flow in the Australian bush rat, Rattus fuscipes. Evolution 57, 1182–1195 (2003).PubMed 

    Google Scholar  More

  • in

    Arctic warming-induced cold damage to East Asian terrestrial ecosystems

    1.Post, E. et al. The polar regions in a 2 °C warmer world. Sci. Adv. 5, eaaw9883 (2019).CAS 

    Google Scholar 
    2.Arrhenius, S. On the influence of carbonic acid in the air upon the temperature of the ground. London, Edinburgh, Dublin Phil. Mag. J. Sci 41, 237–276 (1896).CAS 

    Google Scholar 
    3.Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G. & Nemani, R. R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).CAS 

    Google Scholar 
    4.Bhatt, U. S. et al. Circumpolar Arctic tundra vegetation change is linked to sea ice decline. Earth Intract 14, 1–20 (2010).
    Google Scholar 
    5.Francis, J. A. & Vavrus, S. J. Evidence linking Arctic amplification to extreme weather in mid-latitudes. Geophys. Res. Lett. 39, L06801 (2012).
    Google Scholar 
    6.Cohen, J. et al. Recent Arctic amplification and extreme mid-latitude weather. Nat. Geosci. 7, 627–637 (2014).CAS 

    Google Scholar 
    7.Kim, J.-S. et al. Reduced North American terrestrial primary productivity linked to anomalous Arctic warming. Nat. Geosci. 10, 572–576 (2017).CAS 

    Google Scholar 
    8.Kug, J. S. et al. Two distinct influences of Arctic warming on cold winters over North America and East Asia. Nat. Geosci. 8, 759–762 (2015).CAS 

    Google Scholar 
    9.Jeong, S. J., Medvigy, D., Shevliakova, E. & Malyshev, S. Uncertainties in terrestrial carbon budgets related to spring phenology. J. Geophys. Res. 117, G01030 (2012).
    Google Scholar 
    10.Friedlingstein, P. et al. Global carbon budget 2020. Earth Syst. Sci. Data 12, 3269–3340 (2020).
    Google Scholar 
    11.Piao, S. L. et al. The carbon budget of terrestrial ecosystems in East Asia over the last two decades. Biogeosciences 9, 3571–3586 (2012).CAS 

    Google Scholar 
    12.Mori, M., Watanabe, M., Shiogama, H., Inoue, J. & Kimoto, M. Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades. Nat. Geosci. 7, 869–873 (2014).CAS 

    Google Scholar 
    13.Takaya, K. & Nakamura, H. Mechanisms of intraseasonal amplification of the cold Siberian high. J. Atmos. Sci 62, 4423–4440 (2005).
    Google Scholar 
    14.Honda, M., Inoue, J. & Yamane, S. Influence of low Arctic sea-ice minima on anomalously cold Eurasian winters. Geophys. Res. Lett. 36, L08707 (2009).
    Google Scholar 
    15.Piao, S. L. et al. Interannual variations of monthly and seasonal normalized difference vegetation index (NDVI) in China from 1982 to 1999. J. Geophys. Res. 108, D144401 (2003).
    Google Scholar 
    16.Hua, W. et al. Observational quantification of climatic and human influences on vegetation greening in China. Remote Sens 9, 425 (2017).
    Google Scholar 
    17.Zhou, B., Gu, L., Ding, Y. & Shao, L. The great 2008 Chinese ice storm: its socioeconomic–ecological impact and sustainability lessons learned. Bull. Am. Meteorol. Soc. 92, 47–60 (2011).
    Google Scholar 
    18.Shao, Q., Huang, L., Liu, J., Kuang, W. & Li, J. Analysis of forest damage caused by the snow and ice chaos along a transect across southern China in spring 2008. J. Geogr. Sci. 21, 219–234 (2011).
    Google Scholar 
    19.Wang, X., Huang, S., Li, J., Zhou, G. & Shi, L. Sprouting response of an evergreen broad‐leaved forest to a 2008 winter storm in Nanling Mountains, southern China. Ecosphere 7, e01395 (2016).
    Google Scholar 
    20.Woodward, F. I. & Williams, B. G. Climate and plant distribution at global and local scales. Vegetatio 69, 189–197 (1987).
    Google Scholar 
    21.Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol 12, 1969–1976 (2006).
    Google Scholar 
    22.Piao, S. L., Fang, J. Y., Zhou, L. M., Ciais, P. & Zhu, B. Variations in satellite-derived phenology in China’s temperate vegetation. Glob. Change Biol 12, 672–685 (2006).
    Google Scholar 
    23.Cook, B. I., Wolkovich, E. M. & Parmesan, C. Divergent responses to spring and winter warming drive community level flowering trends. Proc. Natl Acad. Sci. USA 109, 9000–9005 (2012).CAS 

    Google Scholar 
    24.Smith, B., Prentice, I. C. & Sykes, M. T. Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space. Glob. Ecol. Biogeogr 10, 621–637 (2001).
    Google Scholar 
    25.Zhu, D. et al. Improving the dynamics of Northern Hemisphere high-latitude vegetation in the ORCHIDEE ecosystem model. Geosci. Model Dev. 8, 2263–2283 (2015).
    Google Scholar 
    26.Peano, D. et al. Plant phenology evaluation of CRESCENDO land surface models – Part 1: Start and end of the growing season. Biogeosciences 18, 2405–2428 (2021).
    Google Scholar 
    27.Zeng, N., Mariotti, A. & Wetzel, P. Terrestrial mechanisms of interannual CO2 variability. Glob. Biogeochem. Cycles 19, GB1016 (2005).
    Google Scholar 
    28.Lawrence, D. M. et al. The community land model version 5: description of new features, benchmarking, and impact of forcing uncertainty. J. Adv. Model. Earth Syst 11, 4245–4287 (2019).
    Google Scholar 
    29.White, M. A., Thornton, P. E. & Running, S. W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob. Biogeochem. Cycles 11, 217–234 (1997).CAS 

    Google Scholar 
    30.Chen, X. Q., Wang, L. X. & Inouye, D. Delayed response of spring phenology to global warming in subtropics and tropics. Agric. For. Meteorol. 234–235, 222–235 (2017).
    Google Scholar 
    31.Aono, Y. & Kazui, K. Phenological data series of cherry tree flowering in Kyoto, Japan, and its application to reconstruction of springtime temperatures since the 9th century. Int. J. Climatol. 914, 905–914 (2008).
    Google Scholar 
    32.Pearse, W. D., Davis, C. C., Inouye, D. W., Primack, R. B. & Davies, T. J. A statistical estimator for determining the limits of contemporary and historic phenology. Nat. Ecol. Evol 1, 1876–1882 (2017).
    Google Scholar 
    33.Jang, Y. S., Kug, J. S. & Kim, B. M. How well do current climate models simulate the linkage between Arctic warming and extratropical cold winters? Clim. Dyn. 53, 4005–4018 (2019).
    Google Scholar 
    34.Park, H. & Jeong, S. J. Leaf area index in Earth system models: how the key variable of vegetation seasonality works in climate projections. Environ. Res. Lett. 16, 034027 (2021).
    Google Scholar 
    35.Alexeev, V. A., Esau, I. N., Polyakov, I. V., Byam, S. J. & Sorokina, S. Vertical structure of recent Arctic warming from observed data and reanalysis products. Climatic Change 111, 215–239 (2011).
    Google Scholar 
    36.Hänninen, H. Climate warming and the risk of frost damage to boreal forest trees: identification of critical ecophysiological traits. Tree Physiol 26, 889–898 (2006).
    Google Scholar 
    37.Augspurger, C. K. Spring 2007 warmth and frost: phenology, damage and refoliation in a temperate deciduous forest. Funct. Ecol. 23, 1031–1039 (2009).
    Google Scholar 
    38.Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. 9, 426 (2018).
    Google Scholar 
    39.Ichii, K. et al. New data-driven estimation of terrestrial CO2 fluxes in Asia using a standardized database of eddy covariance measurements, remote sensing data, and support vector regression. J. Geophys. Res. Biogeosci. 122, 767–795 (2017).CAS 

    Google Scholar 
    40.Li, X. & Xiao, J. A global, 0.05‐degree product of solar‐induced chlorophyll fluorescence derived from OCO‐2, MODIS, and reanalysis data. Remote Sens 11, 517 (2019).
    Google Scholar 
    41.Cowtan, K. & Way, R. G. Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. Q. J. R. Meteorol. Soc. 140, 1935–1944 (2014).
    Google Scholar 
    42.Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).
    Google Scholar 
    43.Harris, I., Osborn, T. J., Jones, P. & Lister, D. Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset. Sci. Data 7, 109 (2020).
    Google Scholar 
    44.Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI) 3g and fraction of photosynthetically active radiation (FPAR) 3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5, 927–948 (2013).
    Google Scholar 
    45.Bontemps, S. et al. Consistent global land cover maps for climate modeling communities: current achievements of the ESA’s land cover CCI. In ESA Living Planet Symp. 2013 CCI-4 (ESA, 2013).46.Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).
    Google Scholar 
    47.O’Neill, B. C. et al. The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).
    Google Scholar 
    48.Kim, H. Global Soil Wetness Project Phase 3 Atmospheric Boundary Conditions (Experiment 1) [Data set]. Data Integration and Analysis System (DIAS), https://doi.org/10.20783/DIAS.501 (2017).49.Zheng, F., Li, J., Ding, R. & Feng, J. Cross-Seasonal Influence of the SAM on Southern Hemisphere Extratropical SST and its Relationship with Meridional Circulation in CMIP5 models. Int. J. Climatol. 38, 1499–1519 (2018).
    Google Scholar 
    50.Livezey, R. E. & Chen, W. Y. Statistical field significance and its determination by Monte Carlo techniques. Month. Weath. Rev 111, 46–59 (1983).
    Google Scholar 
    51.Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science 300, 1560–1563 (2003).CAS 

    Google Scholar 
    52.Kim, J. S., Kug, J. S. & Jeong, S. J. Intensification of terrestrial carbon cycle related to El Nino-Southern Oscillation under greenhouse warming. Nat. Commun. 8, 1674 (2017).
    Google Scholar  More

  • in

    Grape expectations: making Australian wine more sustainable

    Download PDF

    This photograph was taken at the Angullong estate in New South Wales, Australia, which hosts some of my field trials. The aim is to study sustainable agriculture in vineyards. You have to dodge the odd brown snake, but, as offices go, this one — among the grapevines of such a picturesque part of the world — makes my job quite a privilege.It’s a November evening, which is springtime here in the Southern Hemisphere, and this time of year is when pests such as the light brown apple moth (Epiphyas postvittana) start to emerge. That means that ecologists such as myself, as well as the commercial winemakers we collaborate with, move into data-capture mode to track the presence of the insects. These moths produce multiple generations every year, so they can be quite numerous by harvest time, and can cause real damage by getting into the grapes.We’re conducting experiments to see whether positioning various plant species between and under grapevines can help to reduce the population of pests by encouraging their predators. Parasitoid wasps, for example, target the eggs of light brown apple moths, injecting them with their own eggs. When the wasp larvae hatch, they eat the moth larvae from the inside out. Although quite gruesome, parasitoid wasps could provide an environmentally friendly way to control moth populations.In my laboratory at Charles Sturt University in Orange, we’re incubating moth eggs that we then put on special cards in the vineyard. Because parasitoids love nectar, we expect to see more attacks on the moth eggs in areas where we’ve planted flowering shrubs than in the control areas, where grass predominates. We collect the cards after about 48 hours in the field, and incubate the moth eggs to measure the level of parasitism. In the next couple of years, with more data, we hope to identify the optimum mix of plant species to manage pests without resorting to chemicals.

    Nature 602, 176 (2022)
    doi: https://doi.org/10.1038/d41586-022-00218-z

    Related Articles

    ‘For a brown invertebrate’: rescuing native UK oysters

    Breeding the sweetest biofuels in the business

    An IPCC reviewer shares his thoughts on the climate debate

    Collection: Fieldwork

    Subjects

    Careers

    Ecology

    Agriculture

    Latest on:

    Careers

    A nutritionist in Kenya shares advice for prospective students
    Career Q&A 31 JAN 22

    Why early-career researchers should step up to the peer-review plate
    Career Feature 31 JAN 22

    I’m a lip-reading scientist: here’s how I can discuss science with you
    Career Column 28 JAN 22

    Ecology

    Richard Leakey (1944–2022)
    Obituary 28 JAN 22

    Emphasizing declining populations in the Living Planet Report
    Matters Arising 26 JAN 22

    Reply to: Do not downplay biodiversity loss
    Matters Arising 26 JAN 22

    Agriculture

    A hard graft problem solved for key global food crops
    News & Views 25 JAN 22

    Countries should boycott Brazil over export-driven deforestation
    Correspondence 18 JAN 22

    From the archive
    News & Views 18 JAN 22

    Jobs

    Postdoctoral Research Assistant

    Queen Mary University of London (QMUL)
    London, United Kingdom

    Faculty Position in Environmental Sensing Technologies Joint Appointement between the Swiss Federal Laboratories for Materials Science and Technology (Empa) and the Ecole polytechnique fédérale de Lausanne (EPFL)

    Swiss Federal Institute of Technology in Lausanne (EPFL)
    Dübendorf and Lausanne, Switzerland

    Private Secretary, Chief Scientist

    Alan Turing Institute
    London, United Kingdom

    Research Fellow/Senior Research Fellow – Mathematical Modeller/Bioinformatician

    University College London (UCL)
    London, United Kingdom More

  • in

    Ozone damage costs billions

    Tropospheric ozone is formed through the oxidation of precursor pollutants (nitrogen oxide gases and volatile organic compounds) in the presence of sunlight. This surface ozone — a major pollutant — contributes negatively to air quality, posing risks to human health. Many plant species are also sensitive to ozone, exhibiting reduced growth and seed production, and accelerated ageing. Such impacts translate to high crop yield losses, threatening food security, especially in light of rising ozone concentrations and exposure observed across Asia. More

  • in

    Egg-laying increases body temperature to an annual maximum in a wild bird

    1.Perrins, C. M. Eggs, egg formation and the timing of breeding. Ibis (Lond. 1859). 138, 2–15. https://doi.org/10.1111/j.1474-919X.1996.tb04308.x (1996).Article 

    Google Scholar 
    2.Monaghan, P. & Nager, R. G. Why don’t birds lay more eggs? Trends Ecol. Evol. 12, 270–274. https://doi.org/10.1016/S0169-5347(97)01094-X (1997).CAS 
    Article 

    Google Scholar 
    3.Alisauskas, R., DeVink, J.-M. Breeding costs, nutrient reserves, and cross-seasonal effects: dealing with deficits in sea ducks. pp. 125–168 (2015).4.Ebeid, T., Tumova Prague (Czech Republic). Katedra Chovu Prasat a Drubeze) E (Ceska ZU. In press. Physiological aspects of oviposition and its role in egg quality. A review. Sci. Agric. Bohem. (Czech Republic). v. 35.5.Johnson, A. The avian ovary and follicle development: some comparative and practical insights. Turkish J. Vet. Anim. Sci. 38, 660–669 (2014).CAS 
    Article 

    Google Scholar 
    6.Bédécarrats, G. Y., Baxter, M. & Sparling, B. An updated model to describe the neuroendocrine control of reproduction in chickens. Gen. Comp. Endocrinol. 227, 58–63. https://doi.org/10.1016/j.ygcen.2015.09.023 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    7.Brommer, J. E., Rattiste, K. & Wilson, A. J. Exploring plasticity in the wild: laying date-temperature reaction norms in the common gull Larus canus. Proc. R. Soc. B Biol. Sci. 275, 687–693. https://doi.org/10.1098/rspb.2007.0951 (2008).Article 

    Google Scholar 
    8.Schaper, S. V. et al. Increasing Temperature, Not Mean Temperature, Is a Cue for Avian Timing of Reproduction. Am. Nat. 179, E55–E69. https://doi.org/10.1086/663675 (2012).Article 
    PubMed 

    Google Scholar 
    9.Shave, A., Garroway, C. J., Siegrist, J. & Fraser, K. C. Timing to temperature: Egg-laying dates respond to temperature and are under stronger selection at northern latitudes. Ecosphere 10, e02974. https://doi.org/10.1002/ecs2.2974 (2019).Article 

    Google Scholar 
    10.Verhagen, I., Tomotani, B. M., Gienapp, P. & Visser, M. E. Temperature has a causal and plastic effect on timing of breeding in a small songbird. J. Exp. Biol. https://doi.org/10.1242/jeb.218784 (2020).Article 
    PubMed 

    Google Scholar 
    11.Caro, S. P., Schaper, S. V., Hut, R. A., Ball, G. F. & Visser, M. E. The case of the missing mechanism: How does temperature influence seasonal timing in endotherms?. PLoS Biol. 11, e1001517–e1001517. https://doi.org/10.1371/journal.pbio.1001517 (2013).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    12.Bobr, L. W. & Sheldon, B. L. Analysis of ovulation-oviposition patterns in the domestic fowl by telemetry measurement of deep body temperature. Aust. J. Biol. Sci. 30, 243–257. https://doi.org/10.1071/bi9770243 (1977).CAS 
    Article 
    PubMed 

    Google Scholar 
    13.Kadono, H., Besch, E. L. & Usami, E. Body temperature, oviposition, and food intake in the hen during continuous light. J. Appl. Physiol. 51, 1145–1149. https://doi.org/10.1152/jappl.1981.51.5.1145 (1981).CAS 
    Article 
    PubMed 

    Google Scholar 
    14.Yang, J., Morgan, J. L., Kirby, J. D., Long, D. W. & Bacon a W.,. Circadian rhythm of the preovulatory surge of luteinizing hormone and its relationships to rhythms of body temperature and locomotor activity in turkey hens. Biol. Reprod. 62, 1452–1458. https://doi.org/10.1095/biolreprod62.5.1452 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    15.Zivkovic, B. D., Underwood, H. & Siopes, T. Circadian ovulatory rhythms in Japanese quail: role of ocular and extraocular pacemakers. J. Biol. Rhythms 15, 172–183. https://doi.org/10.1177/074873040001500211 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    16.Ward, S. Energy expenditure of female barn swallows Hirundo rustica during egg formation. Physiol. Zool. 69, 930–951. https://doi.org/10.1086/physzool.69.4.30164236 (1996).Article 

    Google Scholar 
    17.Nilsson, J. -Å. & Råberg, L. The resting metabolic cost of egg laying and nestling feeding in great tits. Oecologia 128, 187–192. https://doi.org/10.1007/s004420100653 (2001).ADS 
    Article 
    PubMed 

    Google Scholar 
    18.Vézina, F. & Williams, T. D. Metabolic costs of egg production in the European starling (Sturnus vulgaris). Physiol. Biochem. Zool. 75, 377–385. https://doi.org/10.1086/343137 (2002).Article 
    PubMed 

    Google Scholar 
    19.Götmark, F. The Effects of Investigator Disturbance on Nesting Birds BT – Current Ornithology. In ed. D.M. Power, pp. 63–104. Springer. https://doi.org/10.1007/978-1-4757-9921-7_3 (1992).20.Lyngs, P. Status of the Danish Breeding population of Eiders Somateria mollissima 1988–93. Dansk Ornitol. Foren. Tidsskr. 94, 12–18 (2000).
    Google Scholar 
    21.Bolduc, F. & Guillemette, M. Human disturbance and nesting success of Common Eiders: interaction between visitors and gulls. Biol. Conserv. 110, 77–83. https://doi.org/10.1016/S0006-3207(02)00178-7 (2003).Article 

    Google Scholar 
    22.Christensen, T. K. Female pre-nesting foraging and male vigilance in Common Eider Somateria mollissima. Bird Study 47, 311–319. https://doi.org/10.1080/00063650009461191 (2000).Article 

    Google Scholar 
    23.Guillemette, M. Foraging before spring migration and before breeding in common eiders: Does hyperphagia occur?. Condor 103, 633–638 (2001).Article 

    Google Scholar 
    24.Guillemette, M. & Ouellet, J. Temporary flightlessness as a potential cost of reproduction in pre-laying Common Eiders Somateria mollissima. Ibis (Lond. 1859). 147, 301–306. https://doi.org/10.1111/j.1474-919x.2005.00402.x (2005).Article 

    Google Scholar 
    25.Rigou, Y. & Guillemette, M. Foraging effort and pre-laying strategy in breeding common eiders. Waterbirds Int. J. Waterbird Biol. 33, 314–322 (2010).
    Google Scholar 
    26.Watson, M. D., Robertson, G. J. & Cooke, F. Egg-laying time and laying interval in the common eider. Condor 95, 869–878. https://doi.org/10.2307/1369424 (1993).Article 

    Google Scholar 
    27.Guillemette, M., Woakes, A. J., Flagstad, A. & Butler, P. J. Effects of data-loggers implanted for a full year in female common eiders. Condor 104, 448–452 (2002).Article 

    Google Scholar 
    28.Franzmann, N. E. Ederfuglens (Somateria m. mollissima) ynglebiologi of populationsdynamik pa° Christiansø 1973–1977. Ph.D. Diss. Copenhagen 1980.29.Pelletier, D., Guillemette, M., Grandbois, J.-M. & Butler, P. J. It is time to move: linking flight and foraging behaviour in a diving bird. Biol. Lett. 3, 357–359. https://doi.org/10.1098/rsbl.2007.0088 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    30.Coulson, J. C. The population dynamics of the Eider Duck Somateria mollissima and evidence of extensive non-breeding by adult ducks. Ibis (Lond. 1859). 126, 525–543. https://doi.org/10.1111/j.1474-919X.1984.tb02078.x (2008).Article 

    Google Scholar 
    31.Sabourin, M. Comportement d’incubation de l’Eider à duvet (Somateria mollissima) et effet du dérangement humain dans deux colonies de l’Estuaire du Saint-Laurentle. Mémoire de maîtrise Université (2003).32.Waltho, C., Coulson, J. Egg laying, parasitism, ‘jumbo clutches’ and egg stealing. In The Common Eider, pp. 7–10. POYSER (2015).33.Guillemette, M., Ydenberg, R. C. & Himmelman, J. H. The role of energy intake rate in prey and habitat selection of common eiders Somateria mollissima in winter: a risk-sensitive interpretation. J. Anim. Ecol. 61, 599. https://doi.org/10.2307/5615 (1992).Article 

    Google Scholar 
    34.Canty, A. & Ripley, B. boot: Bootstrap R (S-Plus) functions. R Packag Vers 1, 3–20 (2017).
    Google Scholar 
    35.Carpenter J, Bithell J. Bootstrap confidence intervals: when, which, what? A practical guide for medical statisticians. Stat. Med. 19, 1141–1164 2000. https://doi.org/10.1002/(SICI)1097-0258(20000515)19:93.0.CO;2-F36.Jenssen, B., Ekker, M. & Bech, C. Thermoregulation in winter-acclimatized Common Eiders (Somateria mollissima) in air and water. Can. J. Zool. 67, 669–673. https://doi.org/10.1139/z89-096 (1989).Article 

    Google Scholar 
    37.Winget, C. M., Averkin, E. G. & Fryer, T. B. Quantitative measurement by telemetry of ovulation and oviposition in the fowl. Am. J. Physiol. 209, 853–858. https://doi.org/10.1152/ajplegacy.1965.209.4.853 (1965).CAS 
    Article 
    PubMed 

    Google Scholar 
    38.Cain, J. R. & Wilson, W. O. Multichannel telemetry system for measuring body temperature: circadian rhythms of body temperature, locomotor activity and oviposition in chickens. Poult. Sci. 50, 1437–1443. https://doi.org/10.3382/ps.0501437 (1971).CAS 
    Article 
    PubMed 

    Google Scholar 
    39.Khalil, A., Matsui, K. & Takeda, K. Responses to abrupt changes in feeding and illumination in laying hens. Turkish J. Vet. Anim. Sci. https://doi.org/10.3906/vet-0901-25 (2010).Article 

    Google Scholar 
    40.Kadono, H. & Yamade, T. Changes of body temperature related to oviposition and ovulation induced by LH in the domestic hen. Nihon Juigaku Zasshi. 47, 55–61. https://doi.org/10.1292/jvms1939.47.55 (1985).CAS 
    Article 
    PubMed 

    Google Scholar 
    41.Piccione, G. & Refinetti, R. Thermal chronobiology of domestic animals. Front. Biosci. 8, s258–s264. https://doi.org/10.2741/1040 (2003).Article 
    PubMed 

    Google Scholar 
    42.Peters DG, Rose RW. The oestrous cycle and basal body temperature in the common wombat (Vombatus ursinus). Reproduction 57, 453–460 (in press). doi:https://doi.org/10.1530/jrf.0.057045343.Rose, R. W. & Jones, S. M. The association between basal body temperature, plasma progesterone and the oestrous cycle in a marsupial, the Tasmanian bettong (Bettongia gaimardi). J. Reprod. Fertil. 106, 67–71. https://doi.org/10.1530/jrf.0.1060067 (1996).CAS 
    Article 
    PubMed 

    Google Scholar 
    44.Graham, C. E., Warner, H., Misener, J., Collins, D. C. & Preedy, J. R. The association between basal body temperature, sexual swelling and urinary gonadal hormone levels in the menstrual cycle of the chimpanzee. J. Reprod. Fertil. 50, 23–28. https://doi.org/10.1530/jrf.0.0500023 (1977).CAS 
    Article 
    PubMed 

    Google Scholar 
    45.Nyakudya, T. T., Fuller, A., Meyer, L. C. R., Maloney, S. K. & Mitchell, D. Body temperature and physical activity correlates of the menstrual cycle in Chacma Baboons (Papio hamadryas ursinus). Am. J. Primatol. 74, 1143–1153. https://doi.org/10.1002/ajp.22073 (2012).Article 
    PubMed 

    Google Scholar 
    46.Suthar, V. S., Burfeind, O., Bonk, S., Dhami, A. J. & Heuwieser, W. Endogenous and exogenous progesterone influence body temperature in dairy cows. J. Dairy Sci. 95, 2381–2389. https://doi.org/10.3168/jds.2011-4450 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    47.Giersch, G. E. W. et al. Menstrual cycle and thermoregulation during exercise in the heat: A systematic review and meta-analysis. J. Sci. Med. Sport 23, 1134–1140. https://doi.org/10.1016/j.jsams.2020.05.014 (2020).Article 
    PubMed 

    Google Scholar 
    48.Farmer, C. G. Parental care: The key to understanding endothermy and other convergent features in birds and mammals. Am. Nat. 155, 326–334. https://doi.org/10.1086/303323 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    49.Koteja, P. Energy assimilation, parental care and the evolution of endothermy. Proc. R. Soc. Lond. Ser. B Biol. Sci. 267, 479–484. https://doi.org/10.1098/rspb.2000.1025 (2000).CAS 
    Article 

    Google Scholar 
    50.Portugal, S. J. et al. Associations between resting, activity, and daily metabolic rate in free-living endotherms: No universal rule in birds and mammals. Physiol. Biochem. Zool. 89, 251–261. https://doi.org/10.1086/686322 (2016).Article 
    PubMed 

    Google Scholar 
    51.Guillemette, M., Pelletier, D., Grandbois, J.-M. & Butler, P. J. Flightlessnessand the energetic cost of wing molt in a large sea duck. Ecology 88, 2936–2945. https://doi.org/10.1890/06-1751.1 (2007).Article 
    PubMed 

    Google Scholar 
    52.Parker, H. & Holm, H. Patterns of nutrient and energy expenditure in female common eiders nesting in the high arctic. Auk 107, 660–668. https://doi.org/10.2307/4087996 (1990).Article 

    Google Scholar 
    53.Guillemette, M. & Ouellet, J.-F. Temporary flightlessness in pre-laying Common Eiders Somateria mollissima: Are females constrained by excessive wing-loading or by minimal flight muscle ratio?. Ibis (Lond. 1859). 147, 293–300. https://doi.org/10.1111/j.1474-919x.2005.00401.x (2005).Article 

    Google Scholar 
    54.Vézina, F., Speakman, J. R. & Williams, T. D. Individually variable energy management strategies in relation to energetic costs of egg production. Ecology 87, 2447–2458. https://doi.org/10.1890/0012-9658(2006)87[2447:ivemsi]2.0.co;2 (2006).Article 
    PubMed 

    Google Scholar 
    55.Bevan, R. M., Butler, P. J., Woakes, A. J. & Prince, P. A. The energy expenditure of free-ranging black-browed albatross. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 350, 119–131. https://doi.org/10.1098/rstb.1995.0146 (1995).ADS 
    Article 

    Google Scholar 
    56.Bevan, R. et al. Heart rates and abdominal temperatures of free-ranging South Georgian shags, Phalacrocorax georgianus. J. Exp. Biol. 200, 661–675 (1997).CAS 
    Article 

    Google Scholar 
    57.Woakes, A. J., Butler, P. J. & Bevan, R. M. Implantable data logging system for heart rate and body temperature: Its application to the estimation of field metabolic rates in Antarctic predators. Med. Biol. Eng. Comput. 33, 145–151. https://doi.org/10.1007/BF02523032 (1995).CAS 
    Article 
    PubMed 

    Google Scholar 
    58.Lewden, A. et al. Body surface rewarming in fully and partially hypothermic king penguins. J. Comp. Physiol. B 190, 597–609. https://doi.org/10.1007/s00360-020-01294-1 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Wilson, R. P. & Grémillet, D. Body temperatures of free-living African penguins (Spheniscus demersus) and bank cormorants (Phalacrocorax neglectus). J. Exp. Biol. 199, 2215–2223 (1996).CAS 
    Article 

    Google Scholar 
    60.Schmidt, A., Alard, F. & Handrich, Y. Changes in body temperature in king penguins at sea: The result of fine adjustments in peripheral heat loss?. Am. J. Physiol. Regul. Integr. Comp. Physiol. 291, R608–R618. https://doi.org/10.1152/ajpregu.00826.2005 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    61.Sherer, J., Wunder, B. A. Thermoregulation of a semi-aquatic mammal, the muskrat, in air and water. 24, 249–256 (1979).62.Dyck, A. P. & MacArthur, R. A. Seasonal patterns of body temperature and activity in free-ranging beaver (Castor canadensis). Can. J. Zool. 70, 1668–1672. https://doi.org/10.1139/z92-232 (1992).Article 

    Google Scholar 
    63.Kolka, M. A. & Stephenson, L. A. Resetting the thermoregulatory set-point by endogenous estradiol or progesterone in women. Ann. N. Y. Acad. Sci. 813, 204–206. https://doi.org/10.1111/j.1749-6632.1997.tb51694.x (1997).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    64.Ubuka, T. & Bentley, G. E. Neuroendocrine control of reproduction in birds. In Hormones and reproduction of Vertebrates (ed Norris DO & Lopez KH), pp. 1–25 (2011).65.van der Klein, S. A. S., Zuidhof, M. J. & Bédécarrats, G. Y. Diurnal and seasonal dynamics affecting egg production in meat chickens: A review of mechanisms associated with reproductive dysregulation. Anim. Reprod. Sci. 213, 106257. https://doi.org/10.1016/j.anireprosci.2019.106257 (2020).Article 
    PubMed 

    Google Scholar 
    66.Tanabe, Y. Production, evolution and reproductive endocrinology of ducks. Asian-Australas. J. Anim. Sci. 5, 173–181. https://doi.org/10.5713/ajas.1992.173 (1992).Article 

    Google Scholar 
    67.Johnson, A. L. Chapter 3 – Organization and Functional Dynamics of the Avian Ovary. In (eds DO Norris, KHBT-H and R of V Lopez), pp. 71–90. Academic Press (2011). https://doi.org/10.1016/B978-0-12-374929-1.10003-468.Bluhm, C. K., Phillips, R. E. & Burke, W. H. Serum levels of luteinizing hormone (LH), prolactin, estradiol, and progesterone in laying and nonlaying canvasback ducks (Aythya valisineria). Gen. Comp. Endocrinol. 52, 1–16. https://doi.org/10.1016/0016-6480(83)90152-1 (1983).CAS 
    Article 
    PubMed 

    Google Scholar 
    69.Bluhm, C. K., Phillips, R. E. & Burke, W. H. Serum levels of luteinizing hormone, prolactin, estradiol and progesterone in laying and nonlaying mallards (Anas platyrhynchos). Biol. Reprod. 28, 295–305. https://doi.org/10.1095/biolreprod28.2.295 (1983).CAS 
    Article 
    PubMed 

    Google Scholar 
    70.Sockman, K. W. & Schwabl, H. Daily estradiol and progesterone levels relative to laying and onset of incubation in canaries. Gen. Comp. Endocrinol. 114, 257–268. https://doi.org/10.1006/gcen.1999.7252 (1999).CAS 
    Article 
    PubMed 

    Google Scholar 
    71.Proszkowiec, M. & Rzasa, J. Variation in the ovarian and plasma progesterone and estradiol levels of the domestic hen during a pause in laying. Folia Biol. (Praha) 49, 285–289 (2001).CAS 

    Google Scholar 
    72.Proszkowiec-Weglarz, M., Rzasa, J., Słomczyńska, M. & Paczoska-Eliasiewicz, H. Steroidogenic activity of chicken ovary during pause in egg laying. Reprod. Biol. 5, 205–225 (2005).PubMed 

    Google Scholar 
    73.Nakayma, T., Suzuki, M. & Ishizuka, N. Action of progesterone on preoptic thermosensitive neurones. Nature 258, 80. https://doi.org/10.1038/258080a0 (1975).ADS 
    Article 

    Google Scholar 
    74.Hampl, R., Stárka, L. & Janský, L. Steroids and thermogenesis. Physiol. Res. 55, 123–131 (2006).CAS 
    PubMed 

    Google Scholar 
    75.Splawinski, J. A., Górka, Z., Zacny, E. & Wojtaszek, B. Hyperthermic effects of arachidonic acid, prostaglandins E2 and F2α in rats. Pflügers Arch. 374, 15–21. https://doi.org/10.1007/BF00585692 (1978).CAS 
    Article 
    PubMed 

    Google Scholar 
    76.Gray, D. A., Marais, M. & Maloney, S. K. A review of the physiology of fever in birds. J. Comp. Physiol. B 183, 297–312. https://doi.org/10.1007/s00360-012-0718-z (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    77.Hertelendy, F. & Biellier, H. V. Evidence for a physiological role of prostaglandins in oviposition by the hen. J. Reprod. Fertil. 53, 71–74. https://doi.org/10.1530/jrf.0.0530071 (1978).CAS 
    Article 
    PubMed 

    Google Scholar 
    78.Etches, R. J., Kelly, J. D., Anderson-Langmuir, C. E. & Olson, D. M. Prostaglandin production by the largest preovulatory follicles in the domestic hen (Gallus domesticus). Biol. Reprod. 43, 378–384. https://doi.org/10.1095/biolreprod43.3.378 (1990).CAS 
    Article 
    PubMed 

    Google Scholar 
    79.Takahashi, T., Tajima, H., Nakagawa-Mizuyachi, K., Nakayama, H. & Kawashima, M. Changes in prostaglandin F2α receptor bindings in the hen oviduct uterus before and after oviposition. Poult. Sci. 90, 1767–1773. https://doi.org/10.3382/ps.2010-01329 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    80.McNabb, F. M. A. The hypothalamic-pituitary-thyroid (HPT) axis in birds and its role in bird development and reproduction. Crit. Rev. Toxicol. 37, 163–193. https://doi.org/10.1080/10408440601123552 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    81.Nakao, N., Ono, H. & Yoshimura, T. Thyroid hormones and seasonal reproductive neuroendocrine interactions. Reproduction 136, 1–8. https://doi.org/10.1530/REP-08-0041 (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    82.Sechman, A. The role of thyroid hormones in regulation of chicken ovarian steroidogenesis. Gen. Comp. Endocrinol. 190, 68–75. https://doi.org/10.1016/J.YGCEN.2013.04.012 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    83.Gabrielsen, G., Mehlum, F., Karlsen, H., Andresen & Parker, H. Energy cost during incubation and thermoregulation in female Common Eider (Somateria mollissima). Nor. Polarinstitutt Skr. 195 (1991).84.Ardia, D. R., Pérez, J. H. & Clotfelter, E. D. Experimental cooling during incubation leads to reduced innate immunity and body condition in nestling tree swallows. Proc. R. Soc. B Biol. Sci. 277, 1881–1888. https://doi.org/10.1098/rspb.2009.2138 (2010).Article 

    Google Scholar 
    85.Hepp, G. R. & Kennamer, R. A. Warm is better: Incubation temperature influences apparent survival and recruitment of wood ducks (Aix sponsa). PLoS ONE 7, e47777 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    86.Ipek, A., Sahan, U. & Sozcu, A. The effects of different eggshell temperatures between embryonic day 10 and 18 on broiler performance and susceptibility to ascites. Rev. Bras. Ciência Avícola 17, 387–394. https://doi.org/10.1590/1516-635X1703387-394 (2015).Article 

    Google Scholar 
    87.Haftorn, S. & Reinertsen, R. E. Regulation of body temperature and heat transfer to eggs during incubation. Ornis Scand. Scandinavian J. Ornithol. 13, 1–10. https://doi.org/10.2307/3675966 (1982).Article 

    Google Scholar 
    88.Vehrencamp, S. Body temperatures of incubating versus non-incubating roadrunners. Condor 84, 203 (1982).Article 

    Google Scholar 
    89.Evans, S. S., Repasky, E. A. & Fisher, D. T. Fever and the thermal regulation of immunity: The immune system feels the heat. Nat. Rev. Immunol. 15, 335–349. https://doi.org/10.1038/nri3843 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    90.Hupton, G., Portocarrero, S., Newman, M. & Westneat, D. F. Bacteria in the reproductive tracts of red-wingedblackbirds. Condor 105, 453–464. https://doi.org/10.1650/7246 (2003).Article 

    Google Scholar 
    91.White, J. et al. Sexually transmitted bacteria affect female cloacal assemblages in a wild bird. Ecol. Lett. 13, 1515–1524. https://doi.org/10.1111/j.1461-0248.2010.01542.x (2010).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    92.Hansen, C. M., Meixell, B. W., Van Hemert, C., Hare, R. F. & Hueffer, K. Microbial infections are associated with embryo mortality in arctic-nesting geese. Appl. Environ. Microbiol. 81, 5583–5592. https://doi.org/10.1128/AEM.00706-15 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    93.Barrow, P. A. & Lovell, M. A. Experimental infection of egg-laying hens with Salmonella enteritidis phage type 4. Avian Pathol. 20, 335–348. https://doi.org/10.1080/03079459108418769 (1991).CAS 
    Article 
    PubMed 

    Google Scholar 
    94.Mitchell, D. et al. Revisiting concepts of thermal physiology: Predicting responses of mammals to climate change. J. Anim. Ecol. 87, 956–973. https://doi.org/10.1111/1365-2656.12818 (2018).Article 
    PubMed 

    Google Scholar 
    95.van Heerwaarden, B. & Sgrò, C. M. Male fertility thermal limits predict vulnerability to climate warming. Nat. Commun. 12, 2214. https://doi.org/10.1038/s41467-021-22546-w (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    96.Guillemette, M., Polymeropoulos, E. T., Portugal, S. J. & Pelletier, D. It takes time to be cool: On the relationship between hyperthermia and body cooling in a migrating seaduck. Front. Physiol. 8, 532. https://doi.org/10.3389/fphys.2017.00532 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    97.Stillman, J. H. Heat waves, the new normal: Summertime temperature extremes will impact animals, ecosystems, and human communities. Physiology 34, 86–100. https://doi.org/10.1152/physiol.00040.2018 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    98.Schou, M. F. et al. Extreme temperatures compromise male and female fertility in a large desert bird. Nat. Commun. 12, 666. https://doi.org/10.1038/s41467-021-20937-7 (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    99.Stevenson, I. R. & Bryant, D. M. Climate change and constraints on breeding. Nature 406, 366–367. https://doi.org/10.1038/35019151 (2000).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar  More

  • in

    Competition between the tadpoles of Japanese toads versus frogs

    The average water temperature and pH in tanks was 19.29 ± 0.10 °C (SE, range: 17.0–22.5) and 8.59 ± 0.01 (SE, range 8.2–8.9) respectively. There was no significant difference among treatments (water temperature: F = 0.0086, df = 5, p = 1.0000, pH: F = 0.0063, df = 5, p = 1.0000).Intraspecific competition (density = 5, 15, 50 tadpoles per tank)The density of conspecifics did not have any significant effect on survival to metamorphosis of B. j. formosus (treatment: Wald chi-square = 3.468, df = 2, p = 0.1766; block: Wald chi-square = 7.770, df = 4, p = 0.1004; Fig. 1a). However, conspecific density had a significant effect on the combined responses of variables (larval period, metamorph SUL, metamorph mass) of B. j. formosus (MANOVA treatment: Wilks’ Lambda = 0.0181, F = 10.7224, df = 6, 10, p = 0.0007; block: Wilks’ Lambda = 0.2028, F = 0.9326, df = 12, 13.52, p = 0.5441). Higher densities of conspecifics increased the duration of the larval period (treatment: F = 6.678, df = 2, 9.30, p = 0.0159; block: F = 0.817, df = 4, 0.40, p = 0.7574; Fig. 1b), and decreased size at metamorphosis (SUL—treatment: F = 49.729, df = 2, 6.94, p  More