Ecology

Field control effect of strain MHT1134 on Fusarium wilt of pepperBefore the investigation of strain MHT1134 control effect, pepper plants with the same wilt symptoms were collected from CC9, TR1 and TR2 fields. The same wilt symptom is that the lower leaves of the plant turn yellow or fall off, and the whole seedling plant wilt and die in the later stage. The pepper root neck can be seen with obvious water-stained brown disease spots. When the root and stem are cut open, the vascular bundle turns brown and has a trend of upward stretching (Fig. 1A–C). We isolated a strain in the root, which colony color is purple (Fig. 1E,F), On the sixth day after inoculating healthy pepper with the spore suspension, the plants showed lower leaf shedding and plant wilting (Fig. 1D). And the pathogen was isolated in the root with the same colony characteristics and micromorphology. The main classification features are as follows: the conidiophores are colorless, with bottle-shaped spore-producing cells at the top (Fig. 1G). There are two kinds of conidias. The small conidia are monocytic, oval or kidney shaped, colorless and are 5–12 × 2–3.5 μm in size. Large conidia are multicellular, sickle-shaped, slightly curved, with slightly pointed cells at both ends, colorless and are 19.6–39.4 × 3.5–5.0 μm in size (Fig. 1H). The morphological characteristics of the strain were consistent with Fusarium oxysporum. The strain DNA was extracted and ITS sequence was amplified by PCR to obtain a DNA fragment with a length of about 500 bp. The sequencing results were compared with the gene sequences in Genbank, and the highest homology was found in Fusarium, and the sequence homology with Fusarium oxysporum reached 100%. The pathogen of pepper wilt was Fusarium oxysporum by means of morphological and molecular identification.Figure 1Typical symptoms and identification of pathogen strains of pepper Fusarium wilt in experimental sites. (A) At the late stage of Fusarium wilt, the whole plant withered and died; (B) the lateral root and taproot of the pepper turn brown and rot; (C) discoloration of vascular bundle in pepper stem after cutting; (D) after the isolated F. oxysporum was inoculated on the pepper, which showed the initial symptoms of wilt disease; (E) positive characteristics of F. oxysporum colony; (F) negative characteristics of colony; (G) sporulation peduncle in bottle shape; (H) large and small conidia.Full size imageCompared with CC9 treatment without biocontrol fungi MHT1134, the disease rate and disease index of pepper Fusarium wilt in TR1 and TR2 treatment were decreased. In TR1, the disease rate and disease index of pepper wilt decreased by 8.44% and 3.76%, respectively. In TR2, the disease rate and disease index of pepper wilt decreased by 57.69% and 63.02%, respectively. However, in the TR2 plots over 2018 and 2019, the disease rate and disease index decreased to 7.13% and 3.03%, which were 64.26% and 70.20%, respectively, less than in the CC9 plots. The control effect of MHT11341 on pepper wilt was 63.03% and 70.21% after one and two years of continuous cropping field, respectively (Table 1). The results indicated that the continuous application of a biocontrol strain further consolidated and improved the control effect.Table 1 Control effects of strain MHT1134 on Fusarium wilt in continuous pepper cropping fields.Full size tableEffects of strain MHT1134 on the physical and chemical properties of pepper rhizosphere soilSoil samples from different planting years showed differences in their physical and chemical properties. In particular, the contents of available phosphorus, available potassium and organic matter were significantly different between the soil planted for the first year and the soil continuously planted for 9 years (available phosphorus: F = 4.38 p = 0.03; available potassium: F = 2.94 p = 0.009; organic matter: F = 5.45 p = 0.02). With the increase in planting years, the organic matter and alkali-hydrolysable nitrogen contents in the soil showed decreasing trends. The organic matter content in the CC9 soil samples was 23.64% less than in the CC1 soil samples, and the alkali-hydrolysable nitrogen content was 45.2% less. The available phosphorus and available potassium levels did not show regular change trends, but the available potassium content in the CC9 soil was lower than in the CC1 soil.Compared with the CC9 soil samples, the alkali-hydrolysed nitrogen, organic matter, available phosphorus and available potassium contents in TR1 soil samples increased by 46.82%, 6.26%, 5.09% and 47.06%, respectively. The available potassium content increased most obviously, followed by alkali-hydrolysable nitrogen. The alkali-hydrolysable nitrogen, organic matter and available phosphorus contents decreased slightly in TR2, but were still higher than those in the CC9 soil samples. In addition, the available potassium content continued to increase by 20% after the application of biocontrol bacterium MHT1134 in the second year (Table 2).Table 2 Effects of MHT1134 on physical and chemical properties of the pepper rhizosphere soil.Full size tableEffects of strain MHT1134 on enzymatic activities in pepper rhizosphere soilBy comparing the activities of six kinds of enzymes in the five groups of soil samples, we found that all the activities, except for that of acid phosphatase, in the CC9 soil were lower than those in the CC1 soil. In TR1 and TR2, the activities of the six enzymes in the soil increased. The urease, dehydrogenase, acid phosphatase, catalase, invertase and acid protease activities increased by 9.04%, 4.42%, 29.02%, 9.35%, 17.83% and 6.83% in TR1, respectively, and by 18.60%, 20.26%, 22.86%, 18.87%, 16.59% and 14.30% in TR2, respectively (Fig. 2A–F). The results indicated that MHT1134 applications could improve the enzyme activities in the soil to different degrees. Moreover, the urease, dehydrogenase, catalase and acid protease activities in soil significantly increased after the continuous application of MHT1134.Figure 2Differences in the enzyme activities in the continuously cropped pepper rhizosphere soil after the application of strain MHT1134. Activity levels of (A) urease; (B) dehydrogenase; (C) acid phosphatase; (D) catalase; (E) invertase; and (F) acid protease. CC1, CC5 and CC9, represent the plots where pepper had been continuously planted for 1, 5 and 9 years, respectively, and TR1 and TR2 represent CC9 plots in which the MHT1134 biocontrol fermentation broth had been applied 1 and 2 years in advance, respectively.Full size imageMicrobial diversity and richnessThe sample dilution curve tended to be flat, and the fungal and bacterial diversity index table (Table 3) shows that the library coverage levels were greater than 99% and 98%, respectively. Together, they indicate that the OTU coverage of the soil samples is basically saturated; therefore, the OTUs reflect the species and structures of the fungal and bacterial communities in the samples. High-throughput sequencing results showed that 765,747 16S rRNA sequences and 1,012,237 ITS sequences were obtained from 15 samples of pepper rhizosphere soil subjected to five treatments. After data quality control, there were 35,362–72,498 bacterial 16S rRNA sequences and 54,007–74,562 fungal ITS sequences. In addition, using the 97% standard, the bacterial and fungal OTU numbers were 17,444–47,775 and 50,876–71,236, respectively.Table 3 Alpha-diversity indexes of fungi and bacteria in different continuous pepper cropping soils.Full size tableAlpha-diversity analysis of fungi and bacteriaThe changes in fungal and bacteria diversity are shown in Table 3. According to the Shannon index analysis, the species richness of fungi in CC1 was the highest (2.88). As the planting years increased, the Shannon index decreased gradually (2.71 in CC5 and 2.69 in CC9). Although ACE and Chao indexes, representing the species abundance of the community, did not show obvious increasing trends, in CC9, the values of the two indexes were significantly higher than in CC1, which indicated that as the planting years increased, the diversity of fungi in the pepper soil decreased, while the species abundance increased. As shown in Table 3, in TR1, the Simpson index, representing species dominance, and the Sobs index, representing species richness, increased significantly, and the Shannon index also increased. In TR2, the Shannon index increased significantly, while the values of other indexes decreased slightly. We hypothesised that after the first year of application, the strain MHT1134 colonised in large numbers, resulting in it being the dominant community species. After continuous application, the soil ecology had adjusted, and the diversity of soil fungi continued to increase. In general, the application of the biocontrol fungal MHT1134 increased the diversity of fungi in the pepper rhizosphere soil and decreased the dominance of some species.The changes in bacterial diversity and abundance in the pepper rhizosphere soil after different periods of continuous cropping are shown by the decreases in the Shannon and Sobs indexes decreased as the planting years increased, indicating that bacterial diversity and bacterial community richness decreased. Although ACE and Chao indexes representing the species abundance of the community did not show regular decreasing trends, in CC9, the values of the two indexes were significantly lower than in CC1, indicating that as the planting years increased, the diversity and richness of bacteria in the pepper soil decreased. Strain MHT1134 had no significant effect on the alpha-diversity index of soil bacteria in TR1, but Simpson, ACE and Chao indexes increased in TR2.Effects of MHT1134 on the microbial community structure in pepper rhizosphere soilAll the bacteria were classified into 352 genera and 23 phyla according to their 16S rRNA sequences, and all the fungi were classified into 6 phyla and 194 genera according to their ITS sequences. The top five phyla in terms of bacterial abundance were Actinobacteria, Acidobacteria, Chloroflexi, Gemmatimonadetes and Nitrospirae. The top six phyla in terms of fungal abundance were Ascomycota, Zygomycota, Basidiomycota, Glomeromycota, Chytridiomycota and Rozellomycota.Effects of MHT1134 on fungal community structure in pepper rhizosphere soilThe effects of the biocontrol treatment on fungal phyla are shown in Fig. 3A. After treatment with MHT1134, the relative abundance of Ascomycota decreased significantly from 77.9 to 70.99%. The abundance of Basidiomycota increased significantly after the treatment, whereas it decreased with the continuous cropping time before the MHT1134 application. However, Zygomycota increased in abundance with the continuous cropping time. The abundance of strain MHT1134 increased significantly and then decreased by 1 year after treatment.Figure 3Fungal clustering accumulation map in pepper rhizosphere soil at the phylum (A) and genus (B) levels. CC1, CC5 and CC9, represent the plots where pepper had been continuously planted for 1, 5 and 9 years, respectively, and TR1 and TR2 represent CC9 plots in which the MHT1134 biocontrol fermentation broth had been applied 1 and 2 years in advance, respectively.Full size imageBy analysing the relative abundance of fungi of different genera in the soil, it was found that the fungi of several genera showed similar change trends in different soil treatments. The relative abundances of Fusarium, Gibberella and the alkali-resistant fungus Pseudallescheria in the soil increased along with continuous cultivation years (CC1  TR2). In addition, the trend was found for Trichoderma, Chaetomium and Mortierella, which declined as the planting years increased, but their relative abundance levels significantly increased in TR1 and significantly increased again in TR2 (Fig. 3B).Using Fusarium as the control, we analysed the variation trends of microorganisms in CC9, TR1 and TR2 soil samples. As shown in Fig. 4, the levels of three genera were positively correlated with the Fusarium change trend, Gibellulopsis, Giberella and Pseudallescheria, while three genera, Trichoderma, Chaetomium and Mortierella, were negatively correlated with Fusarium. Thus, the abundance levels of fungi in Gibellulopsis, Gibberella and Pseudallescheria were reduced after the MHT1134 application. Some species of Gibellulopsis are the pathogenic fungi that cause Verticillium wilt, and some species of Gibberella are the pathogenic fungi that cause gibberellic diseases. The abundance levels of Trichoderma, Chaetomium and Mortierella significantly increased after the application of strain MHT1134.Figure 4The relative abundances of the first 15 genera after the MHT1134 application. *0.01  CC5  > CC9), whereas the abundance of Actinobacteria in the soil increased significantly after the application of MHT1134 fermentation broth (CC9  More

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Data collection and preparation—Dutch watersStudy specimensBetween 2006 and 2019, 1457 deceased harbour porpoises in The Netherlands were collected for post-mortem investigations and diet analysis, with the necropsies conducted following internationally standardized guidelines69. For this study focussing on female life history, we selected all females  > 115 cm, as smaller animals may be maternally dependent and can be considered young of the year23. Cases in DCC5, which represent carcass remains, and of which reproductive organs were not assessable or present due to scavenging or incompleteness of the carcasses were excluded. The reproductive organs of the female porpoises  > 115 cm (n = 328/1457) were macroscopically inspected to differentiate between immature and mature animals, with the presence of ovarian corporal scars used as indication of maturity70. Exact age was determined by assessing tooth growth layer groups (GLG) for a subsample of cases (n = 154), according to previously described methods71. Data can be found in STab. 13.Health and nutritional statusPorpoises collected for post-mortem investigation were necropsied with the primary aim to determine the animals’ causes of death and their health status, with the quantity and quality of data and results strongly depending on carcass freshness and completeness as well as other logistical and financial factors69. For this study, we established three proxies based on the findings and metrics taken and assessed at necropsy: a proxy for health status and two proxies for nutritional status. For the proxy for health status, we assessed the cause of death among the mature females (n = 199/328) and divided all cases in two categories. In the first category, we placed all mature females which most likely died as a direct result of incidental bycatch (diagnosed based on the presence of encircling imprints or external incisions, the recent ingestion of prey, and the exclusion of other causes of death, for more details see IJsseldijk et al.63), as a direct result of a predatory attack (diagnosed based on the presence of large, sharp-edged mutilations with associated ante-mortem bite lesions bilaterally on the tailstock, extremities or on the head, for more details see Leopold et al.58) or as a direct result of another acute cause, such as sharp forced trauma or dystocia (obstructed labour, full-term foetus) which did not present signs of significant disease or debilitation. All other animals were placed in the second category, with these mature females displaying evidence of general and significant debilitation, including infectious disease (such as significant parasitism, bacterial, viral or mycotic infections) and/or emaciation. Cases that could not be grouped, mostly as a result of decomposition, were excluded from analyses that included this as a parameter.The first proxy of nutritional status was based on the mean blubber thickness, measured during necropsies in a dorsoventral line on the left body flank just cranial to the dorsal fin, at three locations: dorsal, lateral, and ventral. Blubber thickness in small cetaceans has previously been shown to decrease during periods of fasting72,73 and this metric has been used as proxy of nutrition by others45,74,75,76. However, it should be noted that blubber thickness is not always a good reflection of individual health nor cause of death (e.g., animals dying of acute causes could also be debilitated62,63). There is uncertainty to what extent factors such as age, sex and season naturally influence blubber thickness, and this should be accounted for. Since we focus our analyses on mature females, no further correction for age and sex was done. However, to correct for season, we modelled the mean blubber thickness as a function of Julian date using a generalized additive model (GAM) to allow a smooth effect of the predictor variable (Julian date). This captures the sinus-shaped seasonal variation in blubber thickness which naturally occurs as a result of changing water- and air temperature25,72 (SFig. 5). The residuals of that model were thereby indicative of an adult females’ nutritional status independent of season, and hence they were used as the proxy for nutrition (referred to in the main text as: nutritional status using corBT, Model 1 in STab. 1).The second proxy of nutritional status used the categorical variable “nutritional condition” (NCC), which is assigned during necropsies as good, moderate, or poor. Animals in good NCC generally presented a convex outline on a cranial perspective, no signs of muscle atrophy (abundant skeletal musculature) and presented signs of visceral fat. Animals in moderate NCC generally did not have a fully round outline on a cranial perspective, showed possible signs of muscle atrophy and did not present visceral fat. Animals in poor NCC generally had a concave outline on cranial perspective, with visible aspects of vertebrae and/or scapula externally, an hollow appearance caudal to the skull and signs of muscle atrophy (based on IJsseldijk et al.69). Since this categorial differentiation is collinear with the first established proxy of nutritional status (SFig. 5), it was not used in the same modelling procedures. Therefore, models were run twice, first with corBT and secondly with NCC (for an overview see STab. 1).Pregnancy rate and foetus sizeThe pregnancy rate (PR) was calculated as the proportion of pregnant females in the total sample of mature females (following e.g.,70,77). Pregnancy rates were also calculated separately for the animals in the two different health status categories (see above). To avoid missing the presence of very small, early embryos, samples from the period of conception (June–August23) as well as samples from the period of calving (May–June23) were excluded in the PR calculations. All foetuses were measured during necropsy (of the dam) and a proportion of these were also weighed.Mean energetic density of dietsAs a measure of the quality of prey species constituting the diet of harbour porpoises necropsied in The Netherlands, we calculated the mean energy density of their diet (MEDD). Prey were identified from stomach contents, mostly from otoliths; for each individual prey that could be identified, the fresh mass was estimated (using78 and following29) The energy density (ED) is defined as the energy per kilogram of wet weight of prey8,79. ED values for all prey species encountered were taken from the literature (STab. 7). If for a given prey species no value for ED could be found, the ED of a comparable species (mostly same genus), or the mean value of its family, was used. For species for which multiple ED values were available, values were averaged. ED values reported in kcal were multiplied by 4.184 to convert to kJ (following e.g.80). To calculate the mean ED of the diet for a group of porpoises (MEDD, kJ·g−1, see Table 1) we used:$$MEDD=frac{1}{sum_{i=1}^{n}{M}_{i}}sum_{i=1}^{n}({M}_{i}*{ED}_{i})$$
(1)
where i is the prey species and M the reconstructed prey mass in grams (following8). The reconstructed prey mass per species is multiplied by the species-specific ED and the energy sum is divided by the total mass of all prey, resulting in the MEDD.Data analyses and statistical models—Dutch watersData were explored prior to analyses following Zuur et al.81,82. Data exploration and analyses were performed using R version 3.6.383, with packages ggplot2, grid, gridExtra, rsq, glmTMB, mgcv and ggpubr. Several statistical models were developed (for referencing in the text see overview in: STab. 1).Influences on foetus sizeTo identify which variables influence foetus size, we firstly identified the best measure for foetus size. A Generalized Linear Model (GLM) for foetus length and weight was fitted (Model 2), with weight only available for a subset of all foetuses (n = 34). This model indicated a close relationship between length and weight (R2 of 0.8 for foetus length as a function of mass, SFig. 5), and foetus length was therefore used as representative for foetus size in the subsequent analysis, to increase sample size. GLMs with a Gaussian distribution were used (Model 3). The model selection tested for covariates and their influence on foetus length, with the predictor variables: Julian date to account for foetus length which increases throughout gestation, total length of the mother, health status of the mother, nutritional status of the mother. Interactions between length of the mother and her nutritional status were included following data exploration. Only cases with complete observation of all parameters were included (n = 43). A backwards model selection approach was applied with the drop1 function from the R language used to assess which model terms could be excluded83. The best fitting model was selected using Akaike’s Information Criterion (AIC), which provides a relative measure of the goodness of fit of statistical models. Model validation was done to identify potential violations of model assumptions by inspection of normalized residuals and assessment of residual probability plots. Likelihood profile confidence intervals (95%) and odds ratios of the most optimal model were calculated. Models were run twice, first using the first proxy of nutritional status based on blubber thickness corrected for season (corBT) and secondly using the nutritional condition category (NCC), taken into account blubber thickness, visceral fat and muscle mass (for full descriptions, see above).Influences on pregnancyTo identify which variables influence pregnancy, we firstly coded all mature, pregnant females as 1 and all mature, non-pregnant females as 0. Next, GLMs with a binomial error distribution and logit link were used (Model 4) to test the influence of included covariates on the likelihood of pregnancy. Only cases with complete observations of all parameters were included (n = 65). The predictor variables included in the saturated model were age, year to assess temporal variance, month to assess seasonal variance, health status (proxy, categorical), and nutritional status. Interactions were added following data exploration: between health and nutritional status, between the health status and year and health status and month. Model selection, validation and interpretation was conducted following the protocol previously described above. Models were run twice, first using the first proxy of nutritional status based on for season corrected blubber thickness (corBT, numerical) and secondly using the nutritional condition category, taking into account blubber thickness, visceral fat and muscle mass (NCC, categorical) (for full descriptions, see above).Age at sexual maturityThe age at sexual maturity (ASM), or age at 50% maturity, was determined using binomial logistic regression models. Maturity, coded as 1 for mature females and 0 for immature females, was modelled as a function of age (in years) to assess ASM (n = 154, Model 5). The model was fitted using a binomial error distribution and logit link, as is appropriate for binary data and the ASM was estimated by calculating the negative of the slope over the intercept.Assessment of porpoise life history and environmental condition globallyThe life history response variables assessed were PR and ASM, which were obtained from 17 different studies. The earliest study was conducted between 1941 and 1943, but the majority of the studies were performed between 1980 and 2019 (including the present study). The environmental predictor variables used were quality of diet, expressed as mean energy density of diet (MEDD), cumulative human impact (CHI) with data on climate change, fishing, land-based pressures, and other human activities, and lastly chemical pollution expressed as polychlorinated biphenyls (PCBs). Fifteen diet studies were used, ranging from 1985 to 2019 (including this study). One comprehensive study was used to obtain the CHI information for the year 2008. A total of 21 studies reporting PCB levels in harbour porpoises, conducted in the period 1971–2019 (including the present study) were collated. Details below and in STab. 1.Life historyFor PR the following were tabulated: (1) the number of pregnant females out of the total number of mature females in each study, (2) the determined conception period and whether this was accounted for in the calculation of the PR, (3) the method to assess pregnancy, which was either based on the presence of a foetus or presence of a corpora lutea (CL), and (4) the source of the specimens: either directly from fisheries, strandings including trauma cases, or a combination thereof (STab. 3). For ASM we provide: (1) how ASM was assessed in each study, and (2) the standard error (SE) or confidence interval (CI), if reported (STab. 4).Energy density of preyA literature search was performed for diet studies from stomach contents of porpoises from or near the study areas where PR and ASM were determined. When multiple diet studies were available the study was selected that best corresponded to the time frame at which PR and ASM were calculated. For the diet studies which reported the reconstructed prey mass in grams we used formula (1) (STab. 8). When the prey mass was reported as a percentage of relative abundance in terms of estimated biomass of prey (%M), we multiplied %M by the ED of the prey species and divided the total %M (STab. 9), using:$$MEDD=frac{1}{sum_{i=1}^{n}{mathrm{%}M}_{i}}sum_{i=1}^{n}({mathrm{%}M}_{i}*{ED}_{i})$$
(2)
For the studies where the %M was presented in a bar chart, we measured the %M using digital callipers.Cumulative human impactAn ecosystem-specific, multiscale spatial model containing high resolution data on the intensity of human stressors and their impact on marine ecosystems was developed by Halpern et al.12,14 as part of their Ocean Health Index project. CHI values derived by this model are based on fourteen stressors related to human activities from four primary categories: (1) land-based drivers, including nutrient pollution runoff, organic chemical pollution runoff (pesticides), direct impact of humans (density of coastal human populations), and light; (2) five types of (commercial) fishing, including commercial demersal destructive, commercial demersal non-destructive high bycatch, commercial demersal non-destructive low bycatch, pelagic high bycatch, pelagic low bycatch, and artisanal; (3) climate change, including sea surface temperature, ocean acidification and sea level rise; and (4) shipping. Extensive descriptions of these drivers are published in the methods and supplementary material of Halpern et al.12,14, including information on the origin and validation of the data.For this study we used the global CHI dataset that is publicly available via the Knowledge Network for Biocomplexity14. Data on CHI was based on the year 2008. We extracted the CHI scores for each of our study areas at ~ 1 km2 resolution and calculated the min, max, mean and median values. To do so, we defined our study areas using the standard georeferenced marine regions as published under the Flanders Marine Institute84. In most cases we combined two or more regions from the database to get full coverage of the study area, but for the study areas where the marine regions did not provide full coverage, we used a manually created polygon. The list of regions is given in STab. 15. For areas with more than one life history study (Denmark and The Netherlands) we used the newest studies since these provided the better match to the time of the CHI score calculation (STab. 2).Chemical pollutionPolychlorinated biphenyls were not included in the list of organic polluters by Halpern et al.14. However, PCBs have been specifically associated with reproductive impairment in many marine mammal species16,17,18,30,50, therefore the correlation with life history parameters for this industrial organic pollutant was assessed separately. Data was retrieved from the International Whaling Commission’s (IWC) ‘POP Contaminants Trend Explorer’ tool, hosted on the portal of the Sea Mammal Research Unit (SMRU, University of St. Andrews, Scotland). This tool is established under the IWC Scientific Sub-Committee on Environmental Concerns (IWC SC/68A 2019) as part of the IWC Pollution 2020 Initiative and includes data from scientific publications from the 1970s–2000s34,43. The database was provided by the tool manager and included data restricted to adult males, to reduce the bias of biotransfer of chemicals, which occurs during gestation and lactation in females35. The tool reports PCB concentrations in blubber, which is the most commonly assessed tissue in marine mammals for studying the burden of the highly lipophilic and stable PCB compounds47. PCB concentrations that were measured in porpoises in the same areas from which life history parameters were verified with the literature and included. In addition, the literature was searched for PCB analyses of harbour porpoises published in the 2010s, as well as own institutional databases, and data added to align time frame, where possible, with time frame of conducted life history studies (STab. 2).The presentation of concentrations of pollutants was based on either wet weight (ww) or lipid weight (lw). To allow comparison, the datapoints need to be converted to one common unit, with lw most frequently reported. Studies reporting only ww or dry weight were not included. Studies reporting ww and percentage of lipids (%lipids) were converted to lw, using:$$lw=frac{ww}{%lipids}*100$$
(3)
The datapoints were converted to mg/kg lw for all studies and the mean ∑TotalPCB is reported per area.The variance of the sum of congeners reported ranged from ∑6PCBs up to ∑99PCBs, with several older studies reported Aroclor mixtures. Data per congener was however largely not available in literature. We therefore present two mean ∑PCB datapoints: firstly including all studies regardless of the sum of congeners or mixtures (referred to as PCB1), and secondly limited to studies reporting ∑17-99PCBs (referred to as PCB2).Statistical models for global assessmentFor the analyses we restricted to study areas with complete observations of the environmental conditions to compare models. A GLM fitted with a binomial distribution and logit link was used to determine the effect of environmental conditions on pregnancy rates (Model 6). The response variable was the number of pregnant females (Npreg) in the total number of females (Ntotal) (grouped binomial data, STab. 3). Since the differences between study areas can be large because of unknown effects, an individual normal random effect for area was added on the logit scale. Another GLM was conducted to determine the effect of the three environmental conditions on age at sexual maturity (Model 7) fitted with a Gaussian distribution and weighed by sample size (Ntotal) (STab. 4). This model was applied twice using two individual predictor functions: first, with the predictor variables MEDD, CHI and PCB1 and secondly with the predictor variables MEDD, CHI and PCB2. The latter restricted the analyses to a smaller number of study areas due to missing data, but it reduced some of the bias because of very small ( More

CORRESPONDENCE
02 November 2021

For NGOs, article-processing charges sap conservation funds

Kevin A. Wood

 ORCID: http://orcid.org/0000-0001-9170-6129

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Julia L. Newth

 ORCID: http://orcid.org/0000-0003-3744-1443

1
&

Geoff M. Hilton

 ORCID: http://orcid.org/0000-0001-9062-3030

2

Kevin A. Wood

Wildfowl & Wetlands Trust, Slimbridge, UK.

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Julia L. Newth

Wildfowl & Wetlands Trust, Slimbridge, UK.

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Geoff M. Hilton

Wildfowl & Wetlands Trust, Slimbridge, UK.

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Nature 599, 32 (2021)
doi: https://doi.org/10.1038/d41586-021-02979-5

Competing Interests
All three authors are current employees of the Wildfowl & Wetlands Trust, an environmental non-governmental organization that is actively involved in undertaking and publishing research.

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Data setsMonthly climate data of maximum and minimum temperature, dewpoint temperature, and precipitation at a 1/24th degree horizontal resolution from 1950 to 2020 was acquired from the Parameterized Regression on Independent Slopes Model (PRISM)44. Monthly surface downward shortwave radiation and 10-m wind speeds at a 0.25-degree horizontal resolution were acquired from ERA-545 for the same period and bilinearly interpolated to the PRISM grid. Monthly data for the same variables from a single ensemble member from each of 30 climate models participating in the Sixth Coupled Model Intercomparison Project (CMIP6) were acquired from the historical climate experiment for 1950–2014 and from the SSP2-45 experiment for 2015–2050 and interpolated to a common 1.0-degree horizontal resolution grid (Supplementary Table 4).Following Abatzoglou and Williams, we calculated three proxies of aridity using monthly climate data: mean vapor pressure deficit (VPD), Penman-Monteith reference evapotranspiration (ETo), and climatic water deficit (CWD46, defined as ETo minus actual evapotranspiration3). We modified ETo to account for potential reduced stomatal conductance due to increasing atmospheric carbon dioxide, which reduces surface resistance to evapotranspiration. We made this modification following the method of Yang et al.47. Importantly, the effect of CO2 on surface resistance at the scale of the western US is highly uncertain and this method derives the strength of this effect from earth system models. Each index was calculated as follows. At each grid cell, we calculated mean Mar–Sep VPD, the sum of Mar–Sep ETo, and Jan-Dec CWD; each of these time series was standardized to the 1991–2020 baseline using z-score transformations to create a fuel aridity index f for each grid cell. The regionally averaged fuel aridity index F was calculated by first taking the average of f over grid cells that have a majority of land classified as forest or woodland in the LANDFIRE environmental site potential product48. We then re-standardized F relative to the 1991–2020 reference period and applied equidistant quantile mapping49 to each model. The latter ensures that the distributions of modeled Z match those of observed Z for the 1991–2020 period while preserving changes in Z from this reference period. Herein we used CWD for F because it presents a more balanced view of precipitation and atmospheric demand than VPD or ETo alone, exhibits strong links to the forest-fire area over the observational record, and has more conservative increases in fire under future climate (Supplementary Fig. 2). The variance explained in forest-fire area when defining F as VPD, ETo, and detrended CWD is presented in Supplementary Table 1. We note that our approach does not explicitly incorporate daily meteorology such as the number of dry days or critical fire-weather patterns10 beyond that already included in F.Burned area data from wildland fires were acquired from Monitoring Trends in Burn Severity (MTBS) during 1984–201850 and from the version 6 MODIS burned area dataset during 2001–202051. The forested burned area was aggregated by lands classified as forest or woodland48. MTBS includes primarily fires ≥404 ha that comprises >95% of burned area in the region52. We further excluded areas in the unburned-to-low burn severity class53 as well as fires classified as prescribed burns in MTBS. Further, we did not include forested area treated by prescribed fire as a contemporary area for prescribed fire is more than an order of magnitude less than that of forest-fire area41. Forest-fire area estimates for 2019–2020 were obtained using adjusted burned areas from MODIS based on a linear model that relates MODIS and to the MTBS forest-fire area time series during the overlapping 2001–2018 period26.Experimental designWe focus on macroscale climate–fire models operating at the scale of the entire western US forested area. While there is value in spatially refined models, efforts to parameterize empirical relationships at localized scales can be limited by the stochastic nature of ignitions and fire weather—particularly in locations with long fire return intervals with zero-inflated distributions of annual burned area. Strong interannual relationships between fuel aridity and strain on national fire suppression resources shared across the region highlight the implicit value in considering larger spatial scales54. The macroscale approach is further justified because the leading mode of variability in fuel aridity across forested land is a commonly signed regionwide pattern that is strongly correlated (r2 = 0.79) to the logarithm of forest-fire area (Supplementary Fig. 3).Static modelFollowing previous empirical models of annual forest-fire area3,25, we first consider a static model of western US annual forest-fire area (FFA) based on F (fuel aridity) of the form:$${{{{{rm{log }}}}}}left({{{{{{mathrm{FFA}}}}}}}(t)right)={alpha }_{{{{{{mathrm{s}}}}}}}+{beta }_{{{{{{mathrm{s}}}}}}}Fleft(tright)+{{{{{rm{varepsilon }}}}}},$$
(1)
where t is the year, αs and βs, are regression coefficients, and ε represents an error term. We use annual CWD for F as it accounts for precipitation and atmospheric demand, exhibits strong interannual relationships with FFA, and provide more conservative estimates of projected changes in aridity and thus area burned than other aridity metrics such as VPD3,7,12. The error term ε is drawn from the population of the log-residual of observed minus modeled FFA. This error term represents variability not captured in the FFA–F relationship (e.g., extreme fire-weather conditions, human ignitions) that is important for the full distribution of FFA.Dynamic modelsThe contemporary climate–fire relationship in Eq. 1 should persist with increased F until increased burned area and severity cause fuel limitations15. Fire-fuel feedbacks that alter the climate–fire relationship primarily occur through temporary reduction of fine fuels; such feedbacks can reduce the burning potential for approximately three decades post-fire38,55. Further, longer-lived reductions in the forest-fire area can occur when forests do not recover from fire and instead transition to non-forest vegetation that can still carry fire. However, constraints on the area burned imposed by fire-fuel feedbacks are weakened by concurrent drought, which allows the fire to propagate across sparser fuels, and can markedly shorten the window of reduced burning18.We incorporate these effects through a term L, which represents the fraction of contemporary forested land that is incapable of carrying fire in a predominately forested environment in a given year, in a dynamic model of the form:$${log }left(frac{{{{{{{mathrm{FFA}}}}}}}}{1-Lleft(tright)}right)={alpha }_{{{{{{mathrm{d}}}}}}}+{beta }_{{{{{{mathrm{d}}}}}}}Fleft(tright)+{{{{{rm{varepsilon }}}}}},$$
(2)
where the response of log(FFA) to fuel aridity reduces as a function of L. We present various potential forms and strengths of fire-fuel feedbacks in L that are guided by the ecological literature and account for post-fire tree regeneration failure, fuel limitations imposed by recent fire history, and waning of fuel limitations during drought18,22,23,24. L is influenced by semi-permanent limitations due to failure of post-fire forest regeneration (Lrf), and temporary limitations due to recent fire history (Lf):$$Lleft(tright)={L}_{{{{{{{mathrm{rf}}}}}}}}left(tright)+{L}_{{{{{{mathrm{f}}}}}}}(t).$$
(3)
Importantly, L is poorly constrained and likely varies in geographically and temporally complex ways18,34. For example, L can differ for a fixed fraction of recently burned forest. A relatively small L implies weak feedbacks allowing forests to more easily reburn. A relatively large L implies strong feedbacks, for example, where heterogeneous fire effects create patch mosaics that constrain fire spread even though there is ample fuel. Finally, the age threshold for L may decrease with continued climate change, with some indications that recent fires burned through forests , 2end{array}right.,$$
(4)
where μ is set at 0.1 (Eq. 4 is plotted in Supplementary Fig. 4a). Hence, the fraction of forested land that is semi-permanently ineligible to carry forest fire because previously burned forest did not regenerate as forest (Lrf) is the cumulative sum of the product of annual FFA and ρ since 1984:$${L}_{{{{{{{mathrm{rf}}}}}}}}left(tright)=mathop{sum }limits_{i=1984}^{t}frac{rho left(tright){{{{{{mathrm{FFA}}}}}}}(t)}{T},$$
(5)
where T refers to the contemporary area of forested land48. Note that Eq. 4 and μ can be modified to account for the diversity of species-specific responses at local-to-regional scales given the acknowledgement that some species are more resilient than others and local plant water stress alters regeneration probabilities58,59. Overall, Lrf as parameterized here resulted in values approaching Lrf ~0.01 by 2050, suggesting that the inability of trees to regenerate post-fire is a minor contributor to fire-fuel feedbacks through mid-century. Modifications to the parameters in Eq. 4 resulted in only minor differences in projected FFA (Supplementary Table 3).Temporary fire-fuel feedbacks L
f
Most studies in forested environments show strong fire-fuel feedbacks in the first 5–10 years post-fire55,60. This temporary fire-fuel feedback, which we refer to here as Lf, tends to wane after 10 years60, with the longevity τ of the fire-fuel feedbacks varying geographically, from as short as ~15 years in warmer sites in the southwest to over ~30 years in cold mesic systems in the northern Rockies18. Herein, we use a baseline τ = 30 years, which results in a conservative estimate of future area burned.We consider two forms for how Lf incorporates information on annual fire histories over the previous τ years: a constant feedback and a fading feedback. These forms of Lf are defined below in Eqs. 6 and 7 and plotted in Supplementary Fig. 4c.In the case of the constant feedback, the effect of burned area on Lf remains constant over the τ years following fire. At the scale of the whole western US forested area, the constant form, therefore, assumes that the transient limitation is simply proportional to the total FFA over the preceding τ years:$${L}_{{{{{{mathrm{f}}}}}}}left(tright)=gamma mathop{sum }limits_{i=-tau }^{-1}frac{{{{{{{mathrm{FFA}}}}}}}(i)}{T}.$$
(6)
In Eq. 6, parameter γ represents the strength of the feedback, described in more depth below.The fading feedback form of Lf more heavily weights the contribution from recent FFA compared to older FFA. At the scale of the whole western US forested area, this form applies constant weight to FFA in the five most recent years given strong fire-fuel feedbacks of recent fires, and increasingly reduces the contributions from prior years based on a sinusoid function:$${L}_{{{{{{mathrm{f}}}}}}}left(tright)=gamma frac{mathop{sum }nolimits_{i=-5}^{-1}{{{{{{mathrm{FFA}}}}}}}left(iright)+mathop{sum }nolimits_{i=-tau }^{-6}{{{{{{mathrm{FFA}}}}}}}left(iright)ast left[1-{cos }frac{pi left(-i-5right)}{tau -5}right]/2}{T}.$$
(7)
Given the uncertainty in the efficacy of the fire-fuel feedback, we present results using both the constant and fading formulations for the temporary fire-fuel feedbacks.We additionally considered three different fuel-limitation strengths γ in Eqs. 6 and 7 to account for direct and indirect potential effects of past fires: γ = 0.5, referred to as weak; γ = 1, referred to as moderate; and γ = 1.5, referred to as strong. For the weak (γ = 0.5) fuel-limitation case using the constant feedback model, the fractional forested area ineligible to burn is only half of the total area burned in the past 30 years, indicating that half of recent burned areas can reburn. For the strong-constant fuel-limitation case, the forested area ineligible to burn post-fire exceeds the total recent burned area by 50%. An example of a strong fuel limitation is a burn mosaic with reduced connectivity that constrains the ability of subsequent fire spread into the adjacent forest that did not burn in the previous τ years. We considered higher values of γ, but these yielded degraded cross-validation skills when modeling the historical period (Supplementary Table 2).Longevity of fire-fuel feedbacks during droughtFinally, some temporary fuel limitations can be overcome during extreme fire-weather conditions and during periods of drought. For example, while reduced fuel loads in a post-fire landscape serve as an effective barrier for fire propagation under moderate fuel aridity, the fire spread probability increases with increasing F34. Studies have found that the longevity of fire-fuel feedbacks was a third shorter during periods of extreme drought than in periods without drought stress18,34. For example, there is evidence of short-interval (95% of the iterations had bias CE  > 0, >95% of the iterations had r  > 0, and the inner 95% of the simulations included a bias of 0.Supplementary Table 2 shows that the static model and many of the dynamic models have significant cross-validated skills. However, skill decreased in the dynamic models as the feedback strength increases. While the weak dynamic feedback models had similar cross-validation skill as the static model, dynamic models with very strong feedbacks (γ ≥ 2) had sizeable underpredictions in FFA by up to 46% for the validation period. Hence, we excluded such parameters from the further analysis given that such results were incongruent with the observational record.Three statistical metrics of annual variability of FFA were calculated for both static and dynamic models. First, we used generalized extreme value theory to estimate recurrence intervals for FFA greater than equal to that of the 2020 fire season. Second, we calculated the interquartile range (IQR) in modeled FFA to examine changing interannual variability. Lastly, we examined the percent of years with modeled FFA below the 1991–2020 observed median as a measure of quiescent fire years. Calculations were performed separately for each climate model for 1991–2020 and 2021–2050. More