Philippa Kaur

In this picture, I’m face to face with an anaesthetized 250-kilogram male grizzly bear (Ursus arctos horribilis), which was caught near Sparwood and Elkford in Canada. With help from conservation inspector Joe Caravetta, who is sitting next to me, and my field technician Laura Smit, I’m putting a GPS-enabled collar on the bear so that we can track his movements.The first time I worked with a bear this size, it was absolutely exhilarating, a real adrenaline rush. I thought, “My whole head could fit inside this animal’s jaws.” Over time, it has become fairly routine. I learnt to trust the anaesthetic — a mix of drugs given using an air-powered dart gun — and we constantly monitor the bears’ vital signs.While I’m attaching the collar, Laura collects hair samples for genetic studies. We measure the bear’s temperature and oxygen levels, and take hair samples to get an idea of his diet. We weigh him, which is quite a challenge: we use a custom-made tarpaulin with handles to wrap him up like a bear taco. We attach the handles to a hanging scale and, with a rope over a tree branch, winch him up. This particular bear is eight years old and has 29% body fat, which is very healthy for spring.Ultimately, the collars will help us to reduce conflict between bears and the people who live in the area — I’ve seen bears rip shed doors off to get to livestock, and peel open an outdoor freezer like a can of sardines.At times, it’s chaos for both humans and bears, and people react by shooting the bear — the most common cause of death for younger ones. Tracking bears with collars will help us to find solutions.From tracking the bears, we’ve learnt that they are adapting their habits to avoid people, and they become more nocturnal as they get older. We’ve helped local communities to adapt, too: we’ve launched cost-share initiatives for electrical fencing, which is a really effective bear deterrent. More

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Effects of grid spacing on habitat hydraulic complexity metricsThe sensitivity of the habitat hydraulic complexity metrics to Δs was examined by calculating the metrics for Δs = 0.06, 0.12, 0.18, and 0.24 m (for M4, Δs = Δx = Δy). Figure 3 shows the variation of the metrics with grid spacing for scenarios with boulders. A preliminary assessment of no-boulder scenarios (S1-L and S1-H) showed that all the metrics decreased by increasing the grid spacing. However, because the metrics are mostly used in complex rather than non-obstructed and 1-D flows, the plots only include scenarios with boulder placement to highlight the effects of grid spacing on the metrics in complex flows. All the metrics generally decreased as Δs increased. At the low flow rate, by changing the Δs from the smallest to largest, i.e., 0.06 m to 0.024, the mean decreases in the M1, M2, and M4 metrics (averaged over all the scenarios with boulders) were 45.1, 9.9, and 74.7%, respectively. At the high flow rate, these reductions were 34.8, 14.7, and 82.5% for M1, M2, and M4, respectively. Table 2 shows the p-values associated with the changes in the metrics due to increasing Δs from 0.06 to 0.24 m for all scenarios. The table indicates that changes in M1 and M4 were statistically significant while for M2 they were not (p-values  > 0.05 for all scenarios except for S2-H). This result indicated the considerable influence of grid spacing on M1 and M4 metrics in the reaches with boulder placement. Additionally, the differences in the reported average reductions due to changing the flow rate were less than 10%, indicating an insubstantial effect of flow rate on the habitat hydraulic complexity metrics’ sensitivity to the grid spacing. The significant sensitivity of the metrics M1 and M4 to the grid spacing in this study is contrary to the findings of a previous study in which an insignificant correlation was found between the habitat hydraulic complexity metrics and Δs29. This difference can be attributed to different topographic features in the studied reaches. In the previous findings, measurements were mainly taken around the bends and reaches with no significant obstruction29, in which a more uniform flow with smaller velocity gradients is expected. However, in this study, the systematic boulder placement generated more complex flow patterns with noticeable velocity gradients. Therefore, due to the variations of flow velocities in the zone studied, substantially different values for the metrics are anticipated by computing the metrics over different spatial scales.Figure 3Variation of the habitat hydraulic complexity metrics with grid spacing (Δs) for scenarios with boulder placement. (a) kinetic energy gradient metric, M1, (b) normalized kinetic energy gradient metric, M2, (c) modified recirculation metric M4.Full size imageTable 2 p-values associated with changing the grid spacing from 0.06 to 0.24 m.Full size tableStatistical distribution of habitat hydraulic complexity metricsTable 3 lists the mean, minimum, maximum, and standard deviations of the habitat hydraulic complexity metrics (Δs = 0.06 m) for all the scenarios. To complement the results from Table 3 and assess whether the influences of solely changing the boulder concentration or flow rate on the metrics were statistically significant, Table 4 shows p-values associated with changing flow rate from low to high for a given boulder concentration, and Table 5 shows p-values associated with gradually increasing the boulder concentration for a given flow rate.Table 3 The statistical parameters of the habitat hydraulic complexity metrics in the detailed measurement zone.Full size tableTable 4 p-values from a t-test associated with changes in flow rate for a given boulder concentration.Full size tableTable 5 p-values from a t-test associated with changes in boulder concertation for a given flow rate.Full size tableFor metric M1, the mean M1 values for scenarios incorporating boulders showed the same order of magnitude as values from previous studies for reaches with single and multiple boulders27 but they were about one order of magnitude larger than calculated values in the confluence of two rivers11. Using a larger grid spacing in the study in the confluence of two rivers11 can be the reason for this difference. For a scenario at the higher flow rate, the mean M1 was on average (averaged for all the scenarios) 36% greater than its counterpart at the lower flow rate and this change in M1 values was statistically significant with p  More

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Analysis of changes in ecosystem services value in the YRBThe results showed that from 1990 to 2020, the total ecosystem services value in the YRB showed a dynamic trend of decrease-increase–decrease, with overall increasing trend, and a total increase of 31.85 × 1010 USD, with an average annual increase of 1.14 × 1010 USD (Table 2). This changing trend is consistent with land use cover change in the area. In 30a, YRB cultivated land decreased by 8663 km2, due to rapid urbanization. In addition, after year 2000, China began to implement the policy of returning farmland to forest and grassland on a large scale, which accelerated the reduction of cultivated land. Results again showed that the forest area increased by 30,933,093 km2, indicating that the implementation of “returning farmland to forest and grassland”policy achieved great results, thus increased the value of ecosystem services generated by forest land by 167.66 × 1010 USD. Grassland increased by 738 km2, as corresponding ESV increased by 28.73 × 1010 USD, while unused land decreased by 8131 km2, with 9.52 × 1010 USD ESV decrease. In general, the ecological protection and management measures in the YRB have achieved remarkable results, and ecosystem service values has been significantly improved due to forest and grassland increase.Table 2 The value of ecosystem services in the YRB from 1990 to 2020.Full size tableIn terms of ecosystem service structure in the YRB (Table 3), the relative proportions of various ESVs did not change significantly, resulting in relatively stable ESV structure. Soil conservation and waste disposal are the most important among them, accounting for about 37% of ESV’s total value. The YRB ecosystem, as can be seen, emphasises the importance of soil conservation and waste disposal in the basin, with Climate regulation, Biodiversity conservation, and Entertainment accounting for only 11.99 percent of the total. Various services have changed to varying degrees during the study period. Waste disposal and climate regulation, for example, have suffered losses of 22.23 × 1010 USD and 20.29 × 1010 USD, respectively.The rest of the services showed an upward trend, among which the value of the Food production service increased the most, which was 19.03 × 1010 USD, owing to the obvious increase of the forest land and grassland area in the YRB.Table 3 The value of individual ecosystem functions in the YRB from 1990 to 2020.Full size tableSpatial distribution and variation characteristics of ecosystem services in the YRBThe total ESV value of the study area and changes in the value of each service could not reflect their spatial differences. To describe the temporal and spatial distribution pattern of ESV in the study area, the natural breakpoint method was used. This method was further used to classify ESV with reference to existing studies, and divided the area into four levels: low-value, lower-value, higher-value, and high-value areas. Takin the three-level watershed of the YRB as the statistical unit for analysis, the result showed that the higher the level, the higher the ESV. As shown in Fig. 2, from 1990 to 2020, the spatial characteristics of ESV were relatively stable. The YRB’s upper reaches, from Shizuishan to the north bank of Hekou Town, the Fenhe River Basin, from Hekou Town to Longmen, and the Jinghe River Basin are all rich in high-values. The forest and grassland are relatively concentrated in the above-mentioned areas, the ESV coefficient is high, and the watershed area is large, resulting in a high total ESV. The higher value areas are mainly distributed in the areas from Longyang Gorge to Lanzhou main stream basin, the Daxia River and Tao River basin, and the Wei River basin. The area above Baoji Gorge and the inflow area fall in the transition zone between the high-value area and the lower-value area. For example, the transition area between the Loess and Qinghai-Tibet Plateaus is a higher-value area. The lower-value area mainly includes the Huangshui River Basin, the Datong River Basin, the basin below Lanzhou, and the Guanzhong Plain area. Thus, the unused land in this area is widely distributed. Due to the large area of construction land in the Guanzhong Plain, the ecosystem service value has shrunk. The low-value area is found in the YRB’s lower reaches, which contains the most extensive and large area of construction land in the basin, has a poor ecosystem service function, and is also the YRB’s most economically developed area. In terms of changes in the value of watershed ecosystem services, the number of watersheds at the ESV level did not change significantly between 1990 and 2020. The average ESV of the watershed is 40.52 × 1010 USD. There were 7 high-value, 5 higher-value, 12 lower-value, and 5 low-value watersheds respectively. Figure 2Spatial distribution of ESV changes in YRB from 1990 to 2020. (a) 1990, (b) 2000, (c) 2010, (d) 2020.Full size imageThe hotspot analysis revealed the spatial agglomeration characteristics and ESV evolution law in the YRB from 1990 to 2020 (Fig. 3). In most of the YRB, the ESV accumulation characteristics were not significant in space, and significant areas were dominated with high and low ESV accumulation. The Maqu-Longyangxia River Basin, Daxia River and Tao River Basin, the Datong River Basin, and Fen River Basin were the five core areas where ESV had the highest value. The Inner River, YRB’s northern and eastern margins, and the lower reaches are primarily low-value agglomeration areas. The high-value agglomeration area and low-value agglomeration area did not change significantly in space from 1990 to 2020, but the number of grids in each decreased from 647 to 627 and 699 to 681, respectively. In general, the YRB’s high-value agglomeration areas are strewn about, whereas the low-value agglomeration areas are scattered.Figure 3Spatial agglomeration characteristics of ESV in the YRB from 1990 to 2020. (a) 1990, (b) 2000, (c) 2010, (d) 2020.Full size imageFrom 1990 to 2020, the barycenter coordinates of the ESV in the YRB remained stable between 106.78°–106.94° E and 36.40°–36.65° N (Fig. 4). During the study period, the ESV barycenter coordinates showed a transfer trajectory of first to southwest, then to northeast, and then to southwest. From the perspective of overall transfer direction, ESV barycenter shifted from northeast of Huanxian County to southwest from 1990 to 2020. The ESV in the northeast decreased, while that in southeast increased. From 1995 to 2000 and from 2000 to 2005, the migration distance of ESV barycenter in the YRB was longer by 16.33 km and 15.75 km, respectively, while the barycenter migration distance of ESV from 2005 to 2020 was shorter.Figure 4Barycenter coordinates of ecosystem services in the YRB from 1990 to 2020.Full size imageResponse of ecosystem services to land-use changeThe area of land use type change in the YRB increased by 64,356 km2 between 1990 and 2020. Each land type’s area has changed to varying degrees. Cultivated land and construction land are the two land types that have seen the most changes. The area of cultivated land has shrunk by 8663 km2, while the area of construction land has grown by 13,109 km2. In comparison to water, forest, and grassland, unused land has undergone significant transformations. However, in comparison to 1990, it shrunk by 8131 km2 in 2020. The forest increased by 3093 km2 while grassland increased by 738 km2. Ecosystem services are significantly impacted by changes in land use types. Using the spatial analysis method, the researcher introduces a resilience index to reflect ESV’s response to land-use change in this paper. During 1990–2000 and 2000–2010, average elasticity of ESV change in the YRB relative to land use change was 0.27 and 0.44, respectively, but dropped to 0.04 during 2010–2020. This indicates that the disturbance capacity of land-use change on ecosystem services was low between 1990 and 2000, but increased between 2000 and 2010. Land-use change has had less of an impact on ecosystem services since 2010. The range of changes in land land-use types was wide during this time, but the average elasticity index was low because there were so many different types of land land-use changes, such as the conversion of forest land and cultivated land to construction land, and the conversion of forest land and water area to cultivated land. The decrease in ESV caused by the change in land use per unit area was minor. Furthermore, the forest land and grassland in the river basin have been effectively increased, as ESV has increased. Overall, the value of ecosystem services has remained relatively constant.Accurate spatial statistics on the elasticity index from 1990 to 2020 was carried out (Fig. 5). The elastic index of the upper YRB and Loess Plateau is higher, and the impact of land use change on ecosystem services is more apparent in this region, according to the findings. This is mainly due to the implementation of large-scale ecological engineering measures in response to vegetation degradation in the upper reaches of the YRB and soil erosion in the middle reaches (Loess Plateau), by the Chinese government. In addition, Lanzhou New District, Guanzhong Plain, and the lower Yellow River region also showed higher elasticity index. The above-mentioned regional development and construction, as well as human activities, have resulted in a rapid increase in construction land, resulting in a significant decline in ecosystem services and a higher resilience index as a result of rapid urbanisation.Figure 5Spatial distribution of elastic coefficients in the YRB from 1990 to 2020.Full size imageIn general, the land use types in the YRB have changed dramatically, and land type conversion is very common. The conversion of ecological land to urban construction land, as well as the conversion of unused and cultivated land to ecological land, has resulted in significant changes in ecosystem service value. This demonstrates that the basin’s ecological construction projects have yielded positive environmental results. More

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