Ecology

Biological damage from water deficit in foragesReports on the tolerance to water deficit damage in the forage cultivars under study are scarce, especially in relation to N and C accumulation, Si effects, and physiological attributes.Pastures grown under water restriction with and without silicon showed a decreased cumulative amount of the beneficial element. However, pastures grown with or without water restriction that had received silicon had an increase in the cumulative amount of silicon (Fig. 2a,d). Carbon content decreased in pastures that had received silicon, regardless of water availability (Fig. 2b,e). Water restriction increased N content in both treatments with and without Si for both forages. Silicon fertigation only in plants with water restriction increased N content in cultivar Massai but decreased it in cultivar BRS Zuri (Fig. 2c,f).Figure 2Silicon (Si) content (a, d), carbon (C) content (b, e) and nitrogen (N) content (c, f) in the aerial part of forage plants cultivated in soil with different soil water retention capacity (WRC) (70 and 40%) absence (− Si) and in the presence of silicon fertigation (+ Si). *Significant to 5% probability by the F test. Lowercase letters show differences in relation to Si and uppercase in relation to WRC. The bars represent the standard error of the mean, n = 6.Full size imageThe present study evidenced, especially with Si addition to the crop, that water deficit in the P. maximum pasture, regardless of cultivar, significantly impairs plant growth by changing homeostasis, i.e., decreasing the C:N ratio by reducing plant C content. This induces instability in the metabolism of the crop, especially in terms of physiological processes31,53. Thus, it was clear that water deficit aggravated physiological stress in the pastures due to an increase in electrolyte leakage, followed by a decrease in Fv/Fm. In other words, photosynthetic efficiency decreased in association with lower relative water content in the plant, which reduced the growth of both P. maximum cultivars.Water deficit in both pastures with and without silicon supply decreased the C:N ratio, except in cultivar Massai, in which the omission of silicon increased this ratio. In an adequate condition of water availability, there was no difference between the absence and presence of Si in the pastures (Fig. 3a,d). Other authors report the same results for different forages, such as sugarcane53. Water deficit in the pastures did not change the C:Si ratio, regardless of Si. In pastures with or without water deficit, silicon fertigation decreased the C:Si ratio (Fig. 3b,e).Figure 3Ratio C:N (a, d), ratio C:Si (b, e) and carbon use efficiency (c, f) in the aerial part of forage plants cultivated in soil with different soil water retention capacities (WRC) (70 and 40%) %) absence (− Si) and in the presence of silicon fertigation (+ Si). *Significant at 5% probability. ns: not significant by the test F. Lowercase letters show differences in relation to Si and capitalization in relation to WRC. The bars represent the standard error of the mean, n = 6.Full size imageAlthough this species has a high capacity for dry matter accumulation because it has a high protein content54, it is sensitive to drought55. Drought damage to plant growth, is due to the loss of stoichiometric stability of nutrients56, which balances the mass of various elements between plants and their environments57.A promising alternative to mitigate water deficit damage in the pasture is the use of Si. This element plays a vital role in the physiological, metabolic, and/or functional processes of plants58 when properly absorbed by the crop. The present study evidences the high capacity of the pastures under study to absorb Si when under water restriction. This is because P. maximum is a Si-accumulating species (leaf Si content > 10 g kg−1), which means that these plants might have specific efficient carriers in the process of Si absorption (monosilicic acid)37,59.Biological benefits of silicon in mitigating water deficit in forageThe high Si absorption by the pastures was important because it was enough to change C and N contents in the pastures under water deficit, and consequently the C:N ratio. However, Si absorption varied depending on the cultivar. In cultivar Massai, the absorption of this element decreased due to an increase in N content, while the opposite occurred in cultivar BRS Zuri. This may have occurred because cultivar Massai has higher N absorption efficiency than BRS Zuri. One cultivar or species may have greater absorption efficiency than another because it has a more efficient nitrogen transporter. In other words, it has better kinetic indexes, such as low KM and minimum concentration, which is governed by genetics31.The decrease in the C:Si ratio in plants grown under water restriction is a result of Si supply, which increased the absorption of this element and decreased C content in both pastures. Long et al.28 also reported the importance of silicon in elementary stoichiometry in a study with banana trees under water deficit.The benefit of stoichiometric homeostasis reflected the high metabolic efficiency of C, that is, Si significantly increased C use efficiency in P. maximum pastures under water restriction (Fig. 3b,e). Other authors report this effect in Brachiaria spp. pastures under drought25 and in sugarcane plants without water stress60.Carbon use efficiency (CUE) decreased in pastures with water restriction without silicon application. However, this variable increased in pastures where this element had been applied. In pastures under adequate water availability, silicon fertigation also increased CUE (Fig. 3c,f). Sugarcane plants under water deficit also showed decreased carbon use efficiency53. This increase in C use efficiency (Fig. 3c,f) by Si may have occurred in both pastures because there was a clear decrease in C content in plants grown under water restriction (Fig. 2b,e).Hao et al.29 reported similar results in native grass species, in which high Si content correlated with low levels of C. This decrease in C content may have occurred because when absorbing the beneficial element, the plant applies an “exchange strategy” to C, particularly in cell wall components such as cellulose. This is because the energy cost of including Si in the carbon chain is lower than that of including C itself61. This strategy thus improves the homeostasis of resistance to water deficiency in pastures. Reports indicate that the increase in Si in plant tissues may decrease lignin synthesis in the cell wall, which has a high energy cost62; The plant uses a “low cost strategy” when occupying binding sites between cell wall components, providing similar structural resistance to that of lignin63.These findings may support the promising role of Si in pasture management. This was evidenced from the effect of Si on elemental stoichiometry homeostasis in both forages grown under water restriction, which favored vital physiological processes by increasing the relative water content of the plant by approximately 14% (Fig. 4a,d). However, the effect of Si on the stoichiometric homeostasis of C might have induced energy savings in the plant, which is critical under water deficit conditions. Plants under water deficit have a limitation in the CO2 assimilation rate accompanied by an increase in the activity of another sink of absorbed energy, for example, photorespiration30. Studies on other crops confirm this finding, indicating a benefit of Si on stoichiometric homeostasis in plants under water deficit. Some examples are the studies of Rocha et al.25 on pasture, and Oliveira Filho et al.26 and Teixeira et al.64 on sugarcane.Figure 4Relative water content (a, d), electrolyte leakage index (b, e) and Total phenolic content (c, f) of forage plants cultivated in soil with different soil water retention capacities (WRC) (70 and 40%) absence (− Si) and in the presence of silicon fertigation (+ Si). *Significant at 5% probability. ns: not significant by the test F. Lowercase letters show differences with respect to Si and uppercase in relation to WRC. The bars represent the standard error of the mean, n = 6.Full size imagePastures under water deficit without silicon fertigation showed decreased relative water content in the plants. On the other hand, silicon fertigation increased the relative water content of forages under water deficit (Fig. 4a,d). Wang et al.65 performed a review to elucidate the effect of silicon on plant water transport processes. The authors indicated that silica deposition on leaf cuticle and stomata decreases water loss from transpiration under water deficit stress. However, accumulating evidence suggest that silicon maintains leaf water content not by reducing water loss, but rather through osmotic adjustments, enhancing water transport and uptake. According to the same authors, enhancement of stem water transport efficiency by silicon is due to silica depositing in the cell wall of vessel tubes, avoiding collapse and embolism.The physiological improvement promoted by Si in attenuating water deficit in pastures probably correlates with the reduction of oxidative stress. In this sense, cell electrolyte leakage decreased (Fig. 4b,e), from the increase of the non-enzymatic antioxidant compound in both forages (Fig. 4c,f) or from the activity of antioxidant enzymes66. This reduces reactive oxygen species, which are common in plants under water deficit67.Water deficiency affected the production of phenolic compounds depending on the cultivar. In Massai, this variable only increased with Si supply; in BRS Zuri, however, it decreased regardless of Si. Plants with silicon fertigation had increased phenolic compound content in pastures under both water availability conditions (Fig. 4c,f). Other authors have reported this effect of Si in increasing phenolic compounds in crops such as faba bean68 and sugar beet69. This supports the hypothesis that Si can attenuate the oxidative stress caused by water deficit by increasing the non-enzymatic antioxidant compound.Exogenous application of Si protects the photosynthetic pigments from oxidative damage by reducing membrane lipid peroxidation. In peanut, this type of application either maintained or reduced H2O268. Another effect of Si that demonstrates the attenuation of oxidative stress in pastures under water deficit was the increase in Fv/Fm; in other words, it favored photosynthetic efficiency. In both pastures, the condition of water restriction without silicon supply decreased the quantum efficiency of PSII (Fv/Fm). However, the supply of silicon in pastures, regardless of water condition, increased the photochemical efficiency of PSII (Fig. 5a,c).Figure 5Quantum efficiency of photosystem II (Fv/Fm) (a, c) and total chlorophyll index (Chl a + b) (b, d) of forage plants grown in soil with different soil water retention capacities (WRC) (70 and 40%) absence (− Si) and in the presence of silicon fertigation (+ Si). *Significant at 5% probability. ns: not significant by the test F. Lowercase letters show differences in relation to Si and capitalization in relation to WRC. The bars represent the standard error of the mean, n = 6.Full size imageThe protection of photosynthetic pigments by Si is also indicative of decreased oxidative stress58. The present study evidenced this situation, as the beneficial element increased the total chlorophyll index in both forages under water deficit (Fig. 5b,d). Wang et al.69 reported that Si delays the degradation of chlorophyll–protein complexes, as the element alters the protein components of the thylakoid, thus optimizing the light collection and stability of PSI. Another benefit of Si would be an increase in osmoprotection as a result of the greater accumulation of metabolites, mainly sugars and sugar alcohols (talose, mannose, fructose, sucrose, cellobiose, trehalose, pinitol, and myo-inositol) and amino acids (glutamic acid, serine, histidine, threonine, tyrosine, valine, isoleucine, and leucine), as seen in peanut plants68.Si benefit on forage productivity under water deficitWater restriction with or without silicon supply decreased the height of both pastures, and silicon application in both water regimes increased plant height (Fig. 6a,d). Water restriction with or without silicon supply decreased the number of tillers in both pastures, except for the cultivar BRS Zuri that had received Si. Silicon application increased the number of tillers in both pastures in both water regimes, except for the cultivar Massai without water restriction (Fig. 6b,e). The dry weight of both pastures decreased under water deficit, regardless of silicon. However, the dry matter of the pastures increased after Si application, with or without water restriction (Fig. 6c,f).Figure 6Plant height (a, d), number of tillers (b, e) and dry matter mass (c, f) of forage plants grown in soil with different soil water retention capacity (WRC) (70 and 40%) absence (− Si) and in the presence of silicon fertigation (+ Si). ns: not significant by the test F. Lowercase letters show differences in relation to Si and capitalization in relation to WRC. The bars represent the standard error of the mean, n = 6.Full size imageThus, the mitigating effects of Si on the physiological processes of both pastures grown under water deficit were responsible for increasing forage growth by promoting an increase of 12% in plant height and 31% in the number of tillers, which is one of the main components of pasture production. This resulted in a 25% increase in dry matter accumulation in relation to the pasture without Si (Fig. 7). Other authors have also reported the mitigating effect of Si on water deficit with a view to increasing plant growth in forage crops70 and other crops like wheat71 and rice72.Figure 7Figure of a forage plant in the condition of water deficit in the absence (− Si) and in the presence of silicon fertigation (+ Si) and a summary of its beneficial in the effects of the plant growth.Full size imageThe present study showed that the effect of Si on the attenuation of drought is not restricted only to physiological aspects involving increased plant water content and photosynthetic or biochemical efficiency. It also regulates elemental stoichiometric homeostasis as discussed above, confirming the biological strategy reported by Hao et al.29 in other forage grasses. Our study indicates that the line of research on the relationship between water deficit and Si in elementary stoichiometry is promising and should advance towards a better understanding of the multiple effects of this beneficial element on the plant.Animal production depends on the amount of biomass produced for grazing. The report of Habermann et al.73 has indicated that climate changes, such as droughts, are threatening pasture production and have a negative impact on animal and protein production. To solve this, the present research serves as a reference for Si fertigation management during the growth of P. maximum. This management consists of a sustainable alternative to improve production with greater nutritional balance even under soil water restriction, favoring water use efficiency in cultivation (Fig. 8). Moreover, Si has long-term potential to reduce the occurrence of droughts, favoring the sustainability of ecosystems. This is because the use of the beneficial element in the soil does not produce greenhouse gases, without negative impacts on the production environment74,75.Figure 8Benefits of Si in elementary stoichiometry and its relationship with physiological and biochemical aspects.Full size imageFuture perspectivesPeatlands and other terrestrial ecosystems represent large reservoirs and filters for Si, controlling Si transfer to the oceans. Land use change during the last 250 years has decreased soil Si availability by increasing export and decreasing Si storage due to higher erosion and a decrease in potentially Si-accumulating plants. Moreover, it has led to a twofold to threefold decrease of the base flow delivery of Si76. This raises concern over forage crops, reinforcing the need for silicate fertilization to explain the response of these species to the application of this element. Future perspectives would focuse on the benefits of Si in elementary stoichiometry and its relationship with physiological and biochemical aspects.Studies should use, other forage species, especially dicotyledons sensitive to water deficit, which have different mechanisms for Si absorption. This will allow a better understanding of whether the Si mechanisms that attenuate drought in monocotyledons also occur in dicotyledons. More

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Changepoint detection methodAlthough most of the reflectance time series used in the BinSeg–Normal–Mean and BinSeg–Normal–MeanVar algorithms had a normal distribution, several lagoons had distributions that were skewed or did not follow a normal distribution (Fig. S1). However, results suggested that the accuracy of the detected changepoints were not sensitive to the normality assumption or distributional characteristics.The BinSeg-Normal-Mean algorithm had the highest performance (81% of the 340 validation sites) in detecting the correct year of swine waste lagoon construction, followed by BinSeg-Normal-MeanVar (77%). The two algorithms did not detect the same year of construction for 19 waste lagoons; of these 19, the BinSeg-Normal-Mean detected the correct year for 84% of them, while the BinSeg-Normal-MeanVar detected the correct year for only 16%. Therefore, the BinSeg-Normal-MeanVar algorithm was abandoned given it did not provide additional useful information relative to the BinSeg-Normal-Mean algorithm.Despite good performance, the BinSeg-Normal-Mean algorithm consistently detected a changepoint during the period of record for all sites included in the 10% validation set (n = 340 swine waste lagoons). However, 58 of the 340 swine waste lagoons were constructed prior to 1986, before the period of record suitable for detecting an accurate changepoint. Changepoints before 1986 either (1) detected the correct construction year, or (2) incorrectly detected a changepoint due to artifact signals identified on the images taken in 1984, probably associated with the initial satellite commissioning. In the latter circumstance, if the algorithm detected a changepoint due to this signal, it meant that no land-use change was detected after 1986. Therefore, these waste lagoons were estimated as having been constructed before 1986. In some conditions, when a large number of images was available for the year 1985 and 1986, the algorithm was able to detect the changepoint occurring for the years 1985 or 1986. Further, the BinSeg-Normal-Mean algorithm detected a false year of construction for swine waste lagoons for which the mean of the segment after the changepoint (S2) had a greater average than the segment before the changepoint (S1).To increase algorithm performance, we developed a workflow to address some of the aforementioned caveats (Fig. 4). In this workflow, the BinSeg-Normal-Mean algorithm is applied to a B4 reflectance time series at location j. If the BinSeg-Normal-Mean changepoint is identified for a time in or prior to 1986 (Fig. 4a,i,b,i) we assume that the lagoon was constructed in or prior to 1986. Similarly, a lagoon is assumed to be constructed in or prior to 1986 if a BinSeg-Normal-Mean changepoint is identified after 1986 and the mean of S2 is greater than the mean of S1 (Fig. 4a,ii,b,ii). If a changepoint occurred after 1986 and the mean of S1 was greater than S2, then the changepoint was estimated as having occurred between 1987 and 2010 (Fig. 4a,iii,b,iii).Figure 4Changepoint detection algorithm for determining the year of construction of swine waste lagoons. Panel (a) summarizes the algorithm workflow, while panel (b) illustrates specific examples corresponding to each step (i–iii) in the workflow.Full size imageThe performance of the workflow was evaluated using the validation set composed of 10% of the total number of swine waste lagoons (n = 340). With the new approach, 94% of the swine waste lagoon construction years (+ /- one year) were accurately retrieved. A tolerance of + /− 1 year was chosen to account for a lack of images in some years due to issues with image quality (e.g. high cloud cover) (e.g., Fig. 5a), or because construction spanned at least a year (e.g., Fig. 5b). The changepoint detection workflow incorrectly estimated the construction years for 19 of the 340 swine waste lagoons in the validation set; the differences between the observed and predicted years of construction of these lagoons ranged from 2 to 26 years with a median of 8 years.Figure 5Examples of limitations to the changepoint detection algorithm. In some cases, an insufficient number of high-quality Landsat 5 images were available to capture the year of construction of an individual swine waste lagoon (a), resulting in errors of + /− 1 year. In other cases, the changepoint algorithms detected the start of the construction of the swine waste lagoon but the swine waste lagoon was not fully operational until later years due to prolonged construction timelines (b).Full size imageBy visually inspecting historical Google Earth images for each of the lagoon sites for which the model incorrectly estimated construction year, we identified that model errors were associated with swine waste lagoon expansion, pixel transitions to land-use classes other than swine waste lagoons, or issues with pixels being partly covered by clouds or incompletely covered by the lagoon (i.e., narrow and small waste lagoons that do not entirely cover a pixel).Estimating swine waste lagoon construction yearsUsing the newly developed algorithm (Fig. 4), construction years were estimated for each swine waste lagoon in the NC Coastal Plain (Fig. 6); the years of construction for each swine waste lagoon are included in the supplementary material. Most swine waste lagoons were built in the early 90s and prior to the moratorium of 1997. More specifically, 80% of the swine waste lagoons (n = 2,736) were built between 1987 and 1997. Sixteen percent of the swine waste lagoons were constructed in or prior to 1986. A large decrease in the construction of swine waste lagoons occurred after the moratorium of 1997, with only 3.7% of swine waste lagoons being constructed after the moratorium. These results suggest that the 1997 moratorium did not completely halt the construction of lagoons, but dramatically slowed the rate of expansion.Figure 6Spatiotemporal distribution of swine waste lagoon construction (+/- 1 year) across the HUC6 watersheds. This figure was produced using QGIS version QGIS 3.18.3 (https://www.qgis.org/).Full size imageWith regards to hydrological boundaries (Fig. 7a–h), the Cape Fear River watershed had the highest number of swine waste lagoons (i.e., 56%; Fig. 7b), followed by the Neuse River (i.e., 23%; Fig. 7d), the Lower Pee Dee River (i.e., 9%; Fig. 7c) watersheds. The Albemarle-Chowan (Fig. 7a), Onslow Bay (Fig. 7e), Pamlico (Fig. 7f), Roanoke (Fig. 7g), and Upper Pee Dee (Fig. 7h) watersheds all had less than 9% of the total lagoons within the study area.Figure 7Year of construction of the swine waste lagoons (+ /− 1 year) for the HUC6 watersheds. The y-axis scales are unequal between the plots to improve readability. The dashed red lines correspond to the establishment of the moratorium in 1997.Full size imageResults suggested that the Cape Fear River watershed was the center of the historical growth of the swine industry, where over 300 swine waste lagoons were built prior to 1987. The Cape Fear River watershed experienced a steady increase in the number of swine waste lagoons from 1987 to 1990, with an average of 46 swine waste lagoons being built annually. However, after 1991, the pace of swine waste lagoon construction increased dramatically with an average of 192 swine waste lagoons built annually between 1991 and 1997. The highest construction rate occurred in 1994, with 242 swine waste lagoons built. However, after the 1997 moratorium, the construction rate decreased dramatically; in 1997, 153 swine waste lagoons were constructed, and this number dropped to 23 in 1998. After 1998, the annual average number of swine waste lagoons constructed plunged to 5. Although the swine waste lagoon construction rate fell considerably after the 1997 moratorium, the decrease had already started in 1995. The same pattern was observed for the Neuse, Pamlico, Albemarle-Pamlico, and Onslow Bay watersheds.The spatiotemporal distribution of swine waste lagoons at the HUC12 watershed scale emphasized the historical clustering of the swine industry in the NC Coastal Plain. After the moratorium, swine waste lagoons were present within 436 HUC12 watersheds. However, before 1986, they were spread across only 197 HUC12 watersheds (Fig. 8). Before 1986, the density of waste lagoons was relatively low with an average of 3.38 swine waste lagoons per 100 km2 and a maximum of 15.13 swine waste lagoons per 100 km2 (i.e., Clayroot Swamp-Swift Creek watershed) (Fig. 8). In the 90s, swine waste lagoon construction expanded and continued to intensify in the region. After the moratorium of 1997, the average density of waste lagoons per HUC12 watersheds was 10 per 100 km2 with a maximum of 78 waste lagoons per 100 km2 identified in the Maxwell Creek-Stocking Head Creek basin. After 1997, 16 of 436 HUC12 watersheds had a swine waste lagoon density greater than 40 per 100 km2 (Fig. 8).Figure 8Cumulative swine waste lagoon density per 100 km2 reported at the HUC12 watershed scale; HUC6 watersheds shown in gray for reference. This figure was produced using QGIS version QGIS 3.18.3 (https://www.qgis.org/).Full size imageSpatiotemporal distribution of swine waste lagoons in relation to water resourcesDistance of swine waste lagoon sites to the nearest water feature (i.e., reservoir, canal/ditch, lake/pond, stream/river, estuary) were assessed using the NHD. The analysis revealed that over 150 swine waste lagoons were misclassified by the NHD and were documented in the NHD as lake/pond (n = 102) or swamp/marsh (n = 46). Further, we observed that some NHD water features were misclassified as other non-water features (e.g., forest, pasture), and most of these misclassifications were for polygons with an area less than 0.05 km2. Therefore, NHD water features with areas less than 0.05 km2 were removed from subsequent analyses. Distances between swine waste lagoons and waterways were computed from the NHD without features with areas less than 0.05 km2. The new analysis revealed that 3 swine waste lagoons remained misclassified as lake/pond (n = 1) and swamp/marsh (n = 2). Canal/Ditch, lake/pond, stream/river, and swamp/marsh were identified as the NHD features that were most commonly near swine waste lagoons (Fig. 9). Two swine waste lagoons were near a reservoir in which one was identified as a treatment-sewage pond by the NHD.Figure 9Nearest water features distance to swine waste lagoons.Full size imageThe average and median distance of all swine waste lagoons (including those built early and late in the period of record) to the nearest water features were 234 and 177 m, respectively. Further, 92% of the swine waste lagoons were less than 500 m from the nearest waterways. The Mann–Kendall results revealed a significant upward trend over time of swine waste lagoon distances to the nearest water features (alpha = 0.05, p-value = 0.01). A slight increase over time of swine waste lagoon distances to the nearest water feature is also documented in Table 1.Table 1 Temporal average and median of nearest distance (m) of swine waste lagoons to water features. NA indicated that the water feature was not the closest waterway to any of the studied swine waste lagoons for the time period.Full size table More

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Time-to-event models for wild animals generally model exposure of individuals to natural conditions that may affect the risk of mortality and disappearance. Most models neglect to consider seasons of high human activity that may affect such risks, or interactions between endpoint hazards (reflected in incidences) that may illuminate ecology. For many large carnivores, which suffer from low natural mortality yet are also subject to high risk of anthropogenic mortality and poaching, seasons of anthropogenic activity may be as important as natural ones in mediating cause-specific mortality and disappearance.Importantly, such anthropogenic seasons of higher mortality need not be specific to the animals being studied, especially if the species is controversial and much mortality illegal: our anthropogenic seasons consist of state hunting and hounding seasons for species other than wolves (i.e., deer or bear hunting, and hounding; not wolf hunting), but that mediate human activity on the landscape during those seasons. Our results support the hypothesis that increases in poaching risk during hunting seasons may be attributable to the surge of individuals with inclination to poach on the landscape14,18,29. Alternatively, it could also suggest enhanced criminal activity of a few poachers during the same periods. We temper this increase in poaching risk by establishing snow cover as a major environmental factor strongly associated with poaching. Moreover, our time-to-event analyses illuminate how to evaluate the effects that such anthropogenic seasons may have on risk of mortality and disappearance of monitored animals throughout their lifetime, and how considering such seasons may elucidate the mechanisms behind anthropogenic mortality and disappearance.Additionally, our analysis period precedes and completely excludes any established public wolf hunting seasons. Hence, our modeled anthropogenic seasons represent the periods of most relevant anthropogenic activity for wolves, as hypothesized by other studies14,29,33 and suggested by social science studies on inclinations to poach self-reported by both deer hunters and bear hunters, as well as acceptance of poaching by hunters and farmers30,31,32.Our analyses show increases in the hazard of disappearances of collared wolves (LTF) relative to the baseline period (which excludes environmental and anthropogenic risks) for all seasons. The highest hazard of LTF occurs during the snow season, whereas increases in hazard are lower (and similar) for the two seasons that included hounding and hunting. LTF may experience changes in hazard due to changes in the hazard of any/all of its components: migration, collar failure, or cryptic poaching.Constant and steep increases in LTF hazard throughout a wolf’s lifetime suggests mechanisms other than migration regulating LTF hazard, given migration for adults is most frequent by yearlings and younger adults, around 1.5 to 2.2 years34,35,36. Moreover, only migration out of state would end monitoring, not routine extraterritorial movements of radio-collared wolves. That our seasonal LTF curves depict the cumulative hazards more than doubling beyond those t generally associated with dispersal (~ t  More