Large scale faecal (spraint) counts indicate the population status of endangered Eurasian otters (Lutra lutra)

Ecological Indicators 109 (2020) 105844

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Large scale faecal (spraint) counts indicate the population status of endangered Eurasian otters (Lutra lutra) Sungwon Honga, Mirko Di Febbrarob, Anna Loyb, Phil Cowanc, Gea-Jae Jooa,

T

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a

Department of Biological Sciences, Pusan National University, Busan 46241, Republic of Korea Department of Biosciences and Territory, Universita del Molise, Contrada Fonte Lappone, I-86090 Pesche, Italy c Manaaki Whenua Landcare Research, Lincoln 7640, New Zealand b

A R T I C LE I N FO

A B S T R A C T

Keywords: Non-invasive monitoring Endangered species Conservation Generalized linear mixed model Hot spot analysis Lutra lutra

The validity of using spraint (otter faeces) density for population monitoring has been debated for more than 30 years. In this study, we investigated endangered Eurasian otter (Lutra lutra) spraint occurrence and densities at large scales (over 23,800 km2, a quarter of South Korea) over three years (2014–2016). To clarify the spatial heterogeneity of spraint density and count distributions, we applied the global Morans’ I test and hot spot analysis. We also constructed models with 30 environmental factors (six landscape, eight anthropogenic, 13 aquatic health indices, one prey abundance, and two meteorological factors) using generalized linear mixed models with repeated measurements. Our geographical analysis showed regional clusters of otters extending over distances of more than 80 km. The most parsimonious model, a zero-inflated negative binomial model, indicated that our otter spraint counts were significantly positively related to the benthic macro-invertebrate index and precipitation and negatively related to proportion of home range covered by water. In addition, this model showed that absence probabilities of otter spraint were significantly positively related to human populations and negatively related to the number of fish species and altitude. The best explanatory model suggests that our count data was highly related to otter population status, and also affected by anthropogenic disturbance.

1. Introduction Estimating and monitoring elusive carnivore abundance for management and conservation generally uses multiple approaches based on direct observation and quantification of field signs (Wilson and Delahay, 2001). For Mustelids, faecal densities have been used traditionally as the monitoring method to estimate population size (Hutchings and White, 2000). Estimations of otter (Lutra spp.) populations have used this approach for more than 30 years, and it has been extended to other Mustelids and carnivores (Webbon et al., 2004; Hong et al., 2018). However, others found that this method for the otters can only be used in certain conditions (Conroy and French, 1987). The semiaquatic Eurasian otters mostly nest and range along streams, resulting in home ranges that are generally linear in shape (Erlinge, 1967). The otters generally prefer dense cover in riparian zones as their resting sites (i.e. holts) and female adults are typically very sensitive to habitat characteristics suitable for raising pups (Green et al., 1984; Jefferies, 1986; Scorpio et al., 2016). Prey availability is a crucial factor in determining mortality and breeding success (Kruuk et al., 1987; Ruiz-Olmo et al., 2002, 2006).

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Conroy and French’s (1987) research reported high seasonal variation. In small survey areas (i.e. island) otter population number (r2 = 0.16) could not be predicted well whereas in large areas prediction probability was much (mainland: r2 = 0.61; Yell: r2 = 0.47). In addition, they suggested that because otters’ habitat preference can differ based on the circumstances, the basic study of otter spraint ecology should be investigated, a priori. In the survey of Shetland, there was a moderate relationship between habitat use and otter spraint densities (Kruuk et al., 1986). However, the sea coast in Shetland may have less features than those of a river bank, indicating that there may be less motivation to mark or use certain parts of the coast more frequently (Jefferies, 1986). Spraints are often used by otters for communication, and spraint density has been related to habitat use, resource availability, and mating (Kruuk, 1992; Clavero et al., 2006; Remonti et al., 2011; Kean et al., 2011). Marking behaviour using spraints may be concentrated in sites with high prey fish availabilities (Almeida et al., 2012a). Such observations suggest that otter spraint densities are largely dependent on spatial resource heterogeneity. In sites with higher food abundance, females’ home ranges decreased, and the individuals were more social

Corresponding author at: Department of Biological Sciences, Pusan National University, Jangjeon-dong, Gumjeong-gu, Busan 46241, Republic of Korea. E-mail address: [email protected] (G.-J. Joo).

https://doi.org/10.1016/j.ecolind.2019.105844 Received 21 April 2019; Received in revised form 29 August 2019; Accepted 16 October 2019 1470-160X/ © 2019 Published by Elsevier Ltd.

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still recovering in South Korea, potentially resulting in yearly variation of the hot and cold spots (Hong et al., 2017). Using generalized linear mixed models (GLMMs) applied with different distributions, we aimed to identify the environmental conditions related to spatial clusters (hot and cold spots). GLMMs with repeated measurements are useful for explaining statistically relevant influential relationships (McCullagh and Nelder, 1989). Random effects can explicitly describe spatial and temporal variations (Meredith and Stehman, 1991). We applied such models to the relationships between otter spraint occurrence and density along with a set of environmental parameters. In this study, we aimed to (i) define any national and regional hot spots of otter spraint densities, and (ii) explore the quantitative statistical relationships among otter spraint density, count, occurrence, and environmental factors.

(Jenkins, 1980; Kruuk, 2006). Therefore, otter spraint density may vary depending on the seasonal habitat use of each individual based on habitat conditions (Hung et al., 2004). Since the 1980s, many approaches to estimate abundance or reveal the relationship between spraint and otter numbers have been conducted. Kruuk et al. (1989) tried to relate otter numbers and habitat preference in Shetland with the number of holts, and found a significantly high correlation (r2 = 0.93). The number of holts are normally highly correlated with the number of otter spraints (Jefferies, 1986), highlighting the indirect relationship of spraint numbers and the number of otters. A quantitative comparison between otter presence using radio telemetry and traditional surveys (i.e. footprint, spraints, visual inspection) found high correlations between spraint presence and otter presence, but otters were not detected by spraint presence at nearly 30% of sites (Ruiz-Olmo et al., 2001). More recently, genetic approaches were used to reveal the number of populations (Prigioni et al., 2006). Applying genetic approaches showed a high correlation (r2 = 0.72) between number of genotyped individuals and spraint densities (Lanszki et al., 2008). However, these approaches did not consider population changes from immigration and emigration since they did not apply the capture–recapture approach (Mowry et al., 2011). In South Korea, forest devastation from the Korean war (1950–1954), water pollution through industrialization (1960–1990s), and illegal hunting have negatively affected otter distribution ****(Hong, 2018). More recently, with improved environments, conservation awareness, and strengthened protection laws, Eurasian otter populations have undergone a remarkable recovery and are now found in 84.5% of 10 × 10 km grid cells (Hong et al., 2017; Hong, 2018; Jo et al., 2019). Otter populations have been observed in most cities located in the middle and lower reaches of large rivers, where the density of the slow-moving and/or large fish prey that otters prefer is higher (Erlinge, 1968a; Kruuk and Moorhouse, 1990; Kloskowski, 2005; Hong et al., 2019a,b). Therefore, if otters preferentially mark sites with high prey availabilities, then higher spraint densities would be expected along the middle and lower river reaches (Green et al., 1984). Alternatively, otters show opportunistic generalist behaviour when densities of preferred prey species are rare (Lanszki and Molnar, 2003). When preferred prey densities were lower, their diet consisted of frogs, small fishes, birds, etc (Brzezinski et al., 2006; Sittenthaler et al., 2019). If spraint densities indicate population status and are negatively affected by anthropogenic factors, higher spraint densities would be expected in the remote upper river systems with developed forests, away from human disturbance. If otters prefer to scent-mark in the sites with higher food availability, and otter spraint densities are highly related to population densities, otter spraint presence could be more related to food abundance than spraint density. In previous research on patterns of otter occurrence, Jo et al. (2017) found that otters dispersed and settled in peri-urban areas that were normally located in middle and lower stream reaches; which had higher fish densities than other habitats. Based on these relationships, we hypothesized that environmental factors might affect otter spraint occurrence and density in different ways. Geographical analysis such as Moran’s I and Getis-Ord Gi* (hot spot analysis), can identify regional clusters of populations and communities (Fortin et al., 1990; Yurkowski et al., 2019). If otters are denser or they spend more time in preferred areas, otter spraint densities would be spatially clustered at the landscape scale. Moran’s I can be used to define the highest spatially auto-correlated distance (Krivoruchko, 2011). If the distance sustains more than a few individuals’ territories (i.e. 100 km2), the geographical clusters can determine the preferred areas of the populations. Within the auto-correlated distance, Getis-Ord Gi* can statistically designate the hot and cold spot areas, compared to the random chance of the spraint data in the sites (Getis and Ork, 1992). We hypothesized that spraint densities are highly geographically clustered in remote upper river systems. However, otter populations are

2. Materials and methods 2.1. Study area and sample collection The Nakdong River (35–37° N and 124–131° E) is the principal river in South Korea, flowing for approximately 520 km with a catchment of about 23,800 km2 (a quarter of South Korea’s land area). A quarter of the human population of South Korea (13,248,677 individuals in 2015) resides in its catchment (WaMIS, http://www.wamis.go.kr). Annual average precipitation of the Nakdong River watershed from 2000 to 2010 was 1,326.2 mm, similar to the annual average precipitation of South Korea (1971–2000; Jung et al., 2016), and more than 60% of total rainfall occurs during the rainy season (late June-mid September; Jeong et al., 2007). In the upper reaches of the river there are four dams, a low density human population, and mostly undisturbed streams (Jeong et al., 2010; Hong et al., 2018). For one month and a half (May-June) before the monsoon period, we monitored spraint density at 250 sites for three years (2014–2016) (Fig. 1). We considered both anal jelly (jelly-like secretion) and faeces as spraints. Jelly is more useful for DNA extraction (Hajkova et al., 2006), but any difference in behavioural meaning is unknown (Kruuk, 2006). Jelly might be more involved in mating, but deposition of single spraints also has a similar meaning (Gorman et al., 1978). Thus, we did not distinguish between the two materials. In 2014, we searched for otter spraints during a standardized time period (30 min). In 2015, we searched for spraints along transects of up to 600 m, occasionally truncated when we encountered extreme landscape features, such as cliffs or deep water. In 2016, we walked more consistent 600 m transects, even climbing cliffs or following different sides of streams using GPS tracking devices (Table 1; GPSMAP 64 s, GARMIN). This resulted in three otter spraint indices (spraint densities (no. spraint/meter), spraint count (n), and spraint occurrence). The walking distance was different between years; among 2014 and 2015 (90.8% of sites), and among 2015 and 2016 (30.8% of sites). Along a 600 m transect, we recorded the starting point of each sample site and searched for spraints underneath bridges, rocks, and artificial concrete structures (Kruuk, 2006). One experienced surveyor conducted the counts. There were no carnivore species in the area whose droppings could be confused with those of the otter. In South Korea, there are no carnivores that have a similar niche to the Eurasian otter (Choi and Choi, 2006). In the field survey, if the shape of some faeces were vague, we smelled them. In South Korea, there are no carnivores whose droppings smell similar to those of the otter. 2.2. Spatial analysis for determining otter presence and clustering Previous research indicates that there is still a probability of otter presence at sites where no spraints were found (Kruuk and Conroy, 1987; Ruiz-Olmo et al., 2001). To deal with these cases, we used geographical analysis to examine regional clusters of high densities or 2

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Fig. 1. (a) location of study areas (Nakdong River basin: green polygon) in South Korea, (b) land cover map (blue: water, red: urban, yellow: agriculture, green: mountain), and (c) 250 study sites (pink circles) during survey periods (2014–2016) and light blue lines represent the main river and tributaries in the Nakdong River basin.

2.3. Environmental variables as predictors of otter spraint densities, counts and presence/absence (not otter presence/absence)

Table 1 Transect distance (m) per year (2014–2016).

Average (m) ± S.E. Min-Max (m) Percentage we walked 600 m transect (%)

2014

2015

2016

333 ± 18 5–1600 9.6

571 ± 146 50–1110 69.2

600 600–600 100

We selected 30 environmental variables (six landscape, eight anthropogenic, 13 aquatic health, one resource abundance, and two meteorological factors) based on our literature review (Table 2). We carried out stream health assessments at the spraint sampling sites and used the aquatic organisms’ index values [benthic diatoms (TDI), benthic macro-invertebrates (BMI), and fish (FAI)] as aquatic health factors. The data of stream health factors originated from the national project “Stream Health Assessment” (MOE/NIER, 2008). We calculated four biodiversity indices (Dominance, Shannon, Evenness, and Margalef indices) based on fish and benthic macro-invertebrates density data (Odum and Barrett, 2005). In addition, we used the number of fish species as a variable. We extracted the eight anthropogenic factors and six landscape factors from the land cover map (1 m resolution) and DEM (Digital Elevation Model; 30 m resolution) provided from EGIS (Environmental Geographic Information Service; http://egis.me.go.kr) and BizGIS (http://www.biz-gis.com). We calculated distances (m) from environmental factors using ‘near’ tool, and analysed environmental areas within adult otter home ranges (20 km2, circle radius: 2.5 km; Min, 2007) using ‘join’ tool of ArcMap10.5 (ESRI, USA). Lastly, we used the average temperature and precipitation during May and June as meteorological variables. We obtained these variables (at 100 m’ resolution) via regression-kriging models using altitude as a covariate with the ‘GSIF (Global Soil Information Facilities)’ R package based on official data from 65 meteorological stations (Hengl, 2019). We standardised all survey data to have zero mean and unit variance.

counts of otter spraints. If the densities at the nearest sites were lower, the areas have a lower probability of otter presence. Contrarily, if those were higher, the areas could be included as hot spots. This analysis assumed spraint densities or counts could be highly spatially autocorrelated and geographically clustered. We used Moran’s I to assess the spatial autocorrelation of spraint densities at the national scale, and used hot spot analysis (Getis-Ord Gi*) to identify local spatial clustering (Lee & Jun, 2016). We checked for spatial autocorrelation by applying different distance bands using Moran’s I tool in Arc GIS 10.5; ESRI, USA (http://desktop.arcgis.com/ en/arcmap/10.3/tools/spatial-statistics-toolbox/how-incrementalspatial-autocorrelation-works.htm). Because the maximum distance from neighbouring populations was 20.4 km, we examined distances from 30 km (at least one neighbour was included for the statistical analysis) to 300 km (extent of study areas), increasing in 10 km increments (Fig. 2; Krivoruchko, 2011; Lee & Jun, 2016). We obtained the largest z-score indicating the highest spatial autocorrelation distance, then applied localised spatial clustering (hot spot) analysis where spraint densities were regionally clustered (Getis and Ork, 1992). To assess the differences of data distribution between otter spraint densities and counts, we plotted the hot spot analysis results to examine yearly variation, separately (Fig. 3).

3

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Fig. 2. The line contour plots of z scores of otter spraint densities (a) and counts (b) data with distance (km) between survey years (2014–2016). Different coloured lines indicate different years (red: 2014, green: 2015, blue: 2016). Larger red points indicate the highest spatially auto-correlated distances.

2.4. Generalized linear mixed models using five different distributions

not find spraints on a survey transect. Second, we modelled count data using two different distributions (Poisson and negative binomial). In particular, we applied a variable selection procedure to compare models with all possible combinations of starting covariates and random effect terms (Bates et al., 2015). A single dredge run on all variables requires a considerable amount of time, so we divided the variables into separate steps. First, we used 12

In this study, we calibrated the GLMM by testing five different distributions (beta, Poisson, negative binomial, zero-inflated negative binomial, and binomial) according to the response variable considered. Using the beta distribution, the value of the dependent variable (spraint densities) must be > 0; therefore, 1.0*10−10 was added when we did

Fig. 3. The maps of hot and cold spots of otter spraint densities (a to c) and count (d to f) data per metre. The upper numbers (2014–2016) indicate the years. The right legend shows the different statistical significance for the different colours. 4

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Table 2 The description, abbreviation, and related references of the 30 selected variables. Classification

Abbreviation

Description and hypothesis from literatures

Reference

Landscape

Altitude (m)

Remonti et al., 2009

Order

Altitude of sites. When otter populations are disturbed by humans, populations may be restricted to higher altitudes. Distance to the nearest forest area. Riparian forests can provide refuge. Percentage of forested area within 20 km2 (Adult otter home range). Forests can enhance fish fauna diversity as well as provide otter habitat. Percentage of water area (stream, river, lake, dam, and sea) within 20 km2 (Adult home range of otter). Availability of water is essential for otters’ life. River sub-basin. In South Korea, water quality has been managed at the scale of the river sub-basin. Historical spatially heterogeneous otter occurrences can affect spraint densities. Stream order. Stream orders provide different fish communities.

D_Fact (m) D_Resi (m) Pop (n/km2) (Indi per km2) Z_Agri (%) Z_Fact (%) Z_Resi (%) Z_Road (%) D_PA (m)

Distance to the nearest factory. Pollution can impact otter populations. Distance to the nearest human residence. Human activity can impact otter activity. Human population within 20 km2 (Adult otter home range). Human populations negatively impact otter populations. Percentage of agriculture area within 20 km2. Pesticide can negatively impact otter populations. Percentage of factory area within 20 km2. Pollution can negatively impact otter populations. Percentage of residence area within 20 km2. Conflict between humans and otters may be higher. Percentage of roads within 20 km2. Road kill can impact otter populations. Distance to protected areas. Protected areas can be source sites for otter populations.

BMI

Benthic Macro-Invertebrate Index calculated by Appendix S6. The value of index is calculated by sensitivity of species to pollution and saprobic weights. The higher BMI scores suggest higher habitat condition. Fish Assessment Index calculated by Appendix S7. Otters are sensitive to fish availability. Caught fish species number. Broad diversity of fish can enhance otter foraging selection. Dominance index of fish fauna at the sites. Specific species dominance can negatively affect otter foraging. Shannon index of fish fauna at the sites. Various food sources can widen otters’ feeding selection. Evenness index of fish fauna at the sites. Evenness of food sources can sustain otters’ selective feeding. Margalef index of fish fauna at the sites. Abundant food sources can widen otters’ feeding selection. Species number of benthic macro-invertebrate. Various benthic macro-invertebrates could sustain the food-web structure. Number of benthic macro-invertebrate individuals. Various benthic macro-invertebrates could sustain the food-web structure. Dominance index of benthic macro-invertebrate fauna at the sites. Dominance of specific species can represent an unhealthy aquatic system. Shannon index of benthic macro-invertebrate fauna at the sites. Various food sources may represent healthy aquatic systems. Evenness index of benthic macro-invertebrate fauna at the sites. Even food sources may represent healthy aquatic systems. Margalef index of benthic macro-invertebrate fauna at the sites. Rich food sources may represent healthy food-web structure.

D_For (m) Z_For (%) Z_Water (%) Sub-basin

Anthropogenic factors

Aquatic health indices

FAI Fish_No Fish_Dom Fish_Sha Fish_Even Fish_Mar BMI_No BMI_Indi BMI_Dom BMI_Sha BMI_Even BMI_Mar Prey Abundance

No. Indi

Meteorological factors

Preci (mm) Temp (°C)

Number of fish individuals caught using scoop-nets over 30 min and casting nets 10 times. Prey abundance can positively affect sprainting activity. Water maintenance in the streams is vital for otter populations. Average temperature can affect otter intrinsic behaviour

variables with the ‘dredge’ function (MuMln) applied them to all linear terms (Barton, 2018). We did not exceed 12 variables because runs more than 12 variables require much more time (1 week). We then averaged the models until the cumulative sum of weights exceeded 0.95 (95% confidence set). We only considered variables whose relative importance was > 0.5 and statistically significant for the next steps, where we applied the same procedure using these key variables and other variables. Then, we checked the statistical significance of linear and quadratic terms separately. Finally, to select statistically significant variables, we listed the linear and quadratic mixed models within a 95% confidence interval (MacNally, 2000). Due to different walking distances each year, we used “years” and “sites” as random effect variables in all equations to reduce the different sampling conditions of each year. To further reduce the effects of the river sub-basin and stream order, we also considered these variables as random effects (Hong, 2018), allowing the models’ intercepts to vary according to this predictor. We compared the AICc to identify the best explanatory model. The ‘goodness-of-fit’ of the model was measured using R2 (Zar, 1999). We applied Moran’s I test using ‘spdep’ packages to check for spatial autocorrelation between residuals and spatial weights based on the nearest neighbourhood (Bivand and Wong, 2018).

Scorpio et al., 2016 Teresa et al., 2014 Bego and Hysaj, 2013; Cianfrani et al., 2011 Hong et al., unpublished Hong et al., 2019a,b Conroy et al., 1998 Hong et al., 2018 Robitaille and Laurence, 2002 Prigioni et al., 2007 Hong et al., 2018 Robitaille and Laurence, 2002 Philcox et al., 1999 Hong et al., submitted. Hong et al., 2018

Hong et al., 2018 Erlinge, 1968a Carss, 1995 Almeida et al., 2012b Almeida et al., 2012a,b Carss, 1995 Odum and Barrett, 2005 Odum and Barrett, 2005 Odum and Barrett, 2005 Odum and Barrett, 2005 Odum and Barrett, 2005 Odum and Barrett, 2005 Kruuk, 1992 Cianfrani et al., 2011 Quaglietta et al., 2018

To assess the uncertainty of the model, we averaged parameter estimates and intercepts determined by the weights of the models. We quantified the relative importance for each variable using an index constructed by summing the Akaike weights for all models containing the variables (Burnham and Anderson, 1998). Then, we compared the significance and the importance of the parameters with the selected parameters of the most parsimonious model (Rhodes et al., 2006). Finally, we compared the AICc of each of the most parsimonious models, applying two distributions (Poisson and negative binomial) using the ‘AICctab’ function because the dependent variables between beta regressions (ratios), and Poisson and NB (counts), were different (Bolker and R development core team, 2017). To reveal the relationship between otter spraint presence/absence and environmental variables, we used binomial GLMMs. We selected the most parsimonious models and averaged them in accordance with the previously mentioned methods. Goodness-of-fit was measured using Nagelkerke’s pseudo-R2 (Nagelkerke, 1991). After model selection, we estimated the area under the ROC curves and the true skill statistics (TSS) to evaluate the predictive power of the model (Pearce and Ferrier, 2000; Allouche et al., 2006). We used an optimal cut-off value that equalized specificity (proportion of unoccupied sites correctly 5

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Table 3 The relationship of the selected significant variables (+: positive and −: negative).

Beta Poisson NegativeBinomial Binomial Zero-inflated

BMI

FAI

Alt

Preci

D_Resi

Z_Road

Z_Water

No.Indi

BMI_Mar

+ + +

+ − +

+

+

− −

− − −

−

−

+ +

−

+ +

BMI_Sha

+

Fish_No

+

BMI_Dom

Pop

+

− − −

3.2. Selected models using GLMMs with beta, Poisson, and negative binomial distribution

predicted) and sensitivity (proportion of occupied sites correctly predicted) using the ‘pROC’ package of R studio (Robin et al., 2011). To check for spatial autocorrelation between residuals and spatial weights based on the nearest neighbourhood, we applied Moran’s I test using ‘spdep’ packages (Bivand and Wong, 2018). Finally, because there were many sites with zero spraints, we applied zero-inflated models using prior results (Zuur et al., 2009; Brooks et al., 2017). We applied the more parsimonious distribution between Poisson and negative binomial models with the same selected variables. If the negative binomial model was more parsimonious, we applied nbinom1 and nbinom2; nbinom1 function variance increases linearly with the mean and nbinom2 increases quadratically. For the zero-inflated model, we used the selected variables of the most parsimonious binary model. When variables were not statistically significant, we did not consider them further (MacNally, 2000). For the beta and zero-inflated negative binomial regressions, we applied the ‘glmmTMB’ function (Brooks et al., 2017). The relationships in the plots were made with 1000 replicated simulations using a ‘stats’ package (R Core Team, 2018).

Compared to the Poisson distribution, the NB distribution was more parsimonious (NB: dAICc = 0, df = 12 and Poisson: dAICc = 977.3, df = 13), although the goodness-of-fit of the Poisson distribution (r2 = 0.95) was much higher than for NB (r2 = 0.64). Beta regression demonstrated very low explanatory power (r2 = 0.43) compared to the other two distributions. However, our three models showed similar relationships between otter spraint densities and environmental factors (Table 3; Appendices S1–3). Otter spraint densities were commonly found to be negatively associated with human populations, and positively associated with healthy aquatic indices (BMI and FAI). Interestingly, a negative relationship with fish abundance was found using the Poisson distribution, but not using any other distribution (beta and NB). 3.3. Variable relationships with otter spraint presence/absence For the binomial distribution, we checked 9589 combinations of variables. Our best model indicated that otter spraint occurrence was affected positively by benthic macro-invertebrate index, fish assessment index, and precipitation, and negatively by human population densities and water areas within home range (Appendix S4; cut-off values: 0.85, AUC = 0.90 and TSS = 0.55, Nagelkerke R2 = 0.34). Otter spraint presence was most sensitive to the benthic macroinvertebrate index and the number of fish species. We observed no significant spatial autocorrelation (statistic = −0.003; p = 0.52) and no significant collinearity between variables. The 95% confidence set of linear and quadratic mixed models contained only the most parsimonious model with a total of 31 combinations (weight = 0.78).

3. Results 3.1. Hot spots of otter populations Between 2014 and 2016, the distributions of otter spraint densities and counts varied slightly between years (Appendix S1). The distributions of otter spraints densities and counts were similar between years, but our count data in 2014 (Appendix S1d) was lower compared to the other two years, owing to different transect distances (Appendix S1e, f). Otter spraint counts and densities of the upper areas of Nakdong River basin slightly decreased between 2014 and 2016, while those in the lower left areas remained high, and in the lower right areas increased. Otter spraint absent sites were mostly located in the middle and lower areas of the main Nakdong River and its tributaries. Our spatial autocorrelation (Moran’s I) and hot spot analysis of otter spraint densities and counts showed similar patterns, and two different indices provided almost identical geographical information. Yearly, we obtained different degrees of z-scores suggesting different spatial clusters across survey periods (lines of Fig. 2). Moran’s I predicted that spraint densities and counts were highly spatially auto-correlated at distances of more than 80 km (Fig. 2). The maximum auto-correlation distance was approximately 100 km and our results clearly show that spraint density was spatially heterogeneous. When we conducted a hot spot analysis, the results indicated yearly variation (Fig. 3). During 2014 and 2015, we identified hot spots in the upper Nakdong River basins, but locations differed between years. In 2014, we observed that hot spots in the upper river basins appeared more distinctive than those in the lower areas (Fig. 3a, d). When we examined spraint densities in 2015, hot spots were identified in both the upper and lower river basins (Fig. 3b). Using counts, we observed that hot spots in the lower basins were similar, but those in the upper basins were less distinct (Fig. 3e). Our results may reflect the differences in the transect distance (see ‘Study area and sample collection’ in Materials and Methods). Walking distances in upper areas in 2015 were mostly less than 600 m. In 2016, we only identified hot spots in the lower parts of river basins (Fig. 3c, f).

3.4. Testing the zero-inflated model using prior information Because models using the negative binomial distribution were more parsimonious than those using the Poisson distribution, we considered nbinom1 (linear) and nbinom2 (quadratic) as the families. We maintained the conditional model as the most parsimonious model using negative binomial; considering BMI, FAI, precipitation, Pop, and Z_Water as fixed effects, and sites, year, and sub-basins as random effects. Because otter spraint presence was significantly affected by Pop, Fish_No, BMI_Dom, BMI_Sha, and Alt, we considered those factors to produce a zero-inflation probability. The conditional models we analysed using nbinom2 showed that otter spraint densities were significantly positively influenced by Benthic Micro-invertebrate Index (Fig. 4a, linear slope = 0.15 ± 0.05, p < 0.01) and precipitation (Fig. 4b, quadratic slope = 0.08 ± 0.02, p < 0.001), and negatively by percentage of water area within 20 km2 (Fig. 4c, linear slope = −0.17 ± 0.06, p < 0.01; Fig. 4). In addition, the zero-inflation model showed that otter spraint absences were significantly positively influenced by human population (Fig. 5a, linear slope = 0.50 ± 0.13, p < 0.001), and negatively by number of fish species (Fig. 5b, linear slope = −1.28 ± 0.24, p < 0.001) and altitude (Fig. 5c, linear slope = −1.54 ± 0.41, p < 0.001). Compared to the other models (Poisson and negative binomial), this model was the most parsimonious (dAICc of Poisson: 6

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Fig. 4. Relationship between otter spraint counts in relation to three environmental variables (BMI (a), precipitation (b, mm), and water areas within home range (c, %)) for each of the three years (2014–2016) using a zero-inflated negative binomial regression model. The grey strip along lines represents the x and y values corresponding to the values along both axes.

1116.0, negative binomial: 68.3) with an explanatory (R2) value of 0.64. There was no significant spatial autocorrelation (statistic = −0.015, p = 0.97).

dynamics, such as camera traps (Guter et al., 2008), scent analysis of spraints (otters’ faeces, Kean et al., 2011), and non-invasive genetic capture–recapture (Lampa et al., 2015); as well as defining a relationship between otter spraint densities and environmental factors at large scales (Hong et al., 2018). However, prior to our study, there were no otter spraint surveys with large-scale repeated measurements (Kruuk and Conroy, 1987; Prigioni et al., 2005). Our study sites covered 23,800 km2 (a quarter of South Korea’s land area), which allowed us to

4. Discussion Researchers have used various methods to determine the significance of otter spraint densities in relation to otter population

Fig. 5. Relationship between otter spraint absence probability in relation to three environmental variables (human population densities (a, n/km2), number of fish species (b, n), and altitude (c, m)) for each of the three years (2014–2016) using zero-inflated negative binomial regression model. The grey strip along lines represented the x and y values corresponding to the values along both axes. 7

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rates (Kruuk and Conroy, 1987). However, at sites with abundant fish, otters tend to defecate more frequently, and larger and older individuals tend to remain at these sites (Sulkava et al., 2007; Remonti et al., 2011). In view of that, otters more frequently visited sites with higher aquatic health indices. The otters’ feeding flexibility allows them to take advantage of varied food sources (Remonti et al., 2009). Sites with higher aquatic health indices might provide opportunities for otters to broaden their food selection, particularly if they are living in remote mountainous areas undisturbed by anthropogenic factors (Almeida et al., 2012b; Hong et al., 2019a,b). Our models suggest that otter spraint counts were higher in the remote countryside. In contrast to many previous studies, our result interestingly indicated that otters defecated more in areas with lower water source density (Bego and Hysaj, 2013). These areas were upper streams with little industrialisation and high quality stream environments (Jeong et al., 2010). In the areas, most streams were covered by riparian forests; providing otters with lots of spaces for resting sites and shelters (Green et al., 1984). In Italy, newly developed riparian forests (1954–2007) sustained otter presence (Scorpio et al., 2016). Otters frequently visited such areas, suggesting they were avoiding human disturbances. On the other hand, locations where there have been considerable anthropogenic effects the habitat availability for the otters has often been significantly reduced (Jeong et al., 2010; Woo, 2010). Otters defecated more in sites with higher precipitation at lower water source densities (Ruiz-Olmo et al., 2002; Cianfrani et al., 2011), but this relationship exhibited different annual patterns (2014: negative and 2015–2016: positive). During the time of our surveys, total precipitation in 2014 (142.06 mm) was lower than that in 2015 (176.19 mm) and 2016 (244.51 mm), but precipitation was more even in 2014 (S.D. = 60.64 mm) than both 2015 (117.93 mm) and 2016 (97.29 mm). Thus, our results support the conclusion that otters more actively used sites with higher precipitation in 2015 and 2016 (Almeida et al., 2012a; Bego and Hysaj, 2013). Observing this relationship between precipitation and otter sprainting activities is more convincing when examined in long term studies (Recher et al., 2009). The zero-inflated model indicated that otter spraint absence was closely related to lower number of fish species, higher human population densities, and higher altitudes. Our results, based on otter spraint counts, support our suggestion that otter populations have been negatively affected by anthropogenic disturbance. In summary, otters visited more sites in remote areas with high aquatic health compared to highly industrial or fish-abundant areas. Consequently, our data indicates that habitat security seems to be more important than food sources, because none of our models included fish abundance as an influential factor. Thirty five years ago in Scotland, male otters already seemed to have adapted to anthropogenic disturbance, and nowadays, the otters show tolerance to humans in Eurasia (Green et al., 1984; Weinberger et al., 2016; Hong et al., 2017). However, for breeding females, habitat security is the most crucial factor (Green et al., 1984; Ruiz-Olmo et al., 2006) and populations often establish in such areas (Ruiz-Olmo and Jimenez, 2009; Ruiz-Olmo et al., 2011). Thus, even in urban areas where otters are observed, the important thing is to assess whether the habitats can be protected from humans to help restore the population. Rather than a direct relationship with food abundance we consider that otters’ spraint deposition may be directly related to the abundance of specific fish species rather than the overall abundance of fish (Erlinge, 1968a,b; Hong et al., 2019a,b). With their limited foraging abilities, otters require favourable environments for hunting; such as shallow waters with strong flows (Almeida et al., 2012a; Cote et al., 2008). Therefore, if few large or slow-swimming fish live in such streams, otters could more easily forage there than in deeper, slower moving streams, and coincidently defecate more in the area (Remonti et al., 2011). Although large-scale resource availability and use by otters should be further analysed, we found otter spraints were evenly distributed overall within the 600 m transects, except for nesting places

undertake the first large-scale study of otter spraint distribution. If we assume one solitary adult has a territory of 20 km2 (home range of adult otter in South Korea; Min, 2007), about 1200 individual otters potentially resided in our study area. We conducted our surveys for short periods during the same time each year to avoid effects of seasonality (Cho et al., 2009). Our extensive sampling is the first to provide sufficient data to evaluate the relationship between otter spraint occurrence and environmental factors in Eurasia (Hong et al., 2018). 4.1. Geographically clustered otter spraint data Our geographical hot spot analysis showed heterogeneous otter spraint densities at the landscape scale. Spatial heterogeneity encompassed large areas sustaining a few regional sub-populations, supporting the relationship of otters’ socio-spatial organization to habitat conditions and food availability (Hung et al., 2004; Quaglietta et al., 2014). In freshwater systems, female otters presented more sociality and flexible home ranges related to habitat security compared to males (Erlinge, 1968b; Green et al., 1984; Quaglietta et al., 2014). Females with pups shared space with non-breeding females (Kruuk, 2006). In addition, females used more of the core habitats than peripheral areas implying that female otter densities could be highly concentrated in areas far from human disturbance (Kruuk and Moorhouse, 1990). In Shetland, male otters shared some areas with different males, although that behaviour was not observed in freshwater systems (Erlinge, 1968b; Kruuk and Moorhouse, 1990; Quaglietta et al., 2014). These results imply that sociality of Eurasian otters can be flexible with a heterogeneous environment (Kruuk, 2006). Therefore, we could define the hot spots of otter populations using the spatial analysis accordingly. The cold and hot spots changed yearly (Fig. 3) and because we only surveyed two months in a year, we could not define the general status of otter populations yearly by not incorporating the seasonal variation of space use (Erlinge, 1967). Seasonal changes of fish communities, mostly related to migratory fish, are likely to affect the seasonal habitat use of otter populations (Kruuk and Moorhouse, 1990; Ruiz-Olmo et al., 2002; Cho et al., 2009). Because most of our study sites were fragmented by weir and dam constructions, seasonal community changes involving migratory fishes might have been less important (Korea Rural Community Corporation, 2012; Yoon et al., 2016). However, in our study area streams are frozen in the most pristine sites during winter, limiting access to foraging sites (Kruuk et al., 1987). This food limitation may increase mortality and promote long distance dispersal. However, monitoring using camera trapping at a few such study sites showed steady occurrence during winter in the hot spots which suggests that the home range might not be as different from the survey periods (Erlinge, 1967; Hong, unpublished). On the other hand, only ten years ago otters were not observed in urban areas, but have since begun to reinvade those habitats. These yearly changes suggest that otter population habitats may not be stabilized yet and are still recovering (Hong et al., 2019a,b). 4.2. Description of environmental conditions of hot and cold spot areas using GLMMs By exploring the repeated survey data at the same sites from 2014 to 2016, we were able to describe the environmental conditions of hot and cold-spot areas and were able to develop models scoring fair levels of goodness-of-fit (r2 = 0.64). The most parsimonious model (zero inflated model) indicated that otter spraint counts were highly related to the benthic macro-invertebrate index (BMI), precipitation, and water availability within their home range (Fig. 4). BMI is a useful and commonly used index of aquatic health (Harrel and Dorris, 1968). These results suggest that at sites with higher aquatic health scores, (i) otter spraints are more likely to be found (Green et al., 1984), (ii) otters may visit and stay at such sites more often or longer (Guter et al., 2008), and (iii) such sites are used by older/larger otters with higher sprainting 8

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(holts) and under bridges (Hong S, personal observation).

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4.3. Coherent relationships between otter spraint presence, counts and densities, and environmental factors Influential environmental factors we identified using binary data (otter spraint presence/absence) showed somewhat similar patterns to those identified from otter spraint count data (Table 3). We determined that altitude and human population densities were common influential factors, while some factors (number of fish species, and Shannon and dominance indices of benthic macro-invertebrates) were only significant to the presence/absence data. Our results also suggested that otter spraint presence is related to otter spraint densities (Hong et al., 2018). 4.4. Application for otter spraint counts as monitoring tools Areas with higher otter spraint occurrences and counts may indicate areas that sustain more otter individuals (Guter et al., 2008). We found that such sites were locally clustered and hypothesise that otter spraint densities may be related to otter densities. Genetic analysis by Mowry et al. (2011) of spraints of North American river otter (Lontra. canadensis (Schreber, 1777)), a species with similar socio-behaviour to Eurasian otters (Kruuk, 2006), identified a highly positive relationship between spraint densities and otter densities (r2 = 0.58). Therefore, we recommend large scale surveys of otter spraint counts to assess the population source and sink areas. Because we surveyed using different transect distances each year, we can use different types of spraint data and compare the pros and cons of each data type using statistical analyses. Spraint density data can be applied to analyse the hot-spots, but the distribution of the data cannot be included in the models with high scores of goodness of fit (r2 = 0.43). Otter spraint counts walking randomly different distances established good scores (r2 = 0.64), but compared to the densities, the consistency and reliability of the data was slightly lower (Fig. 3b, e, and Appendix S1). Thus, we determined that otter spraint count data by walking 600 m is the most ideal to analyse the otter population status using hot-spots and GLMM analyses. 4.5. Prospect for faecal counts as monitoring tools We conducted the monitoring of the otter populations by primarily determining otter presence by investigating otter evidences at survey sites. However, currently otter recovery has been detected in large areas of Europe and South Korea, including the city centre, the importance of which may be overestimated (Jo et al., 2019; Sainsbury et al., 2019). Our results scoring fair levels of goodness of fit using GLMMs with repeated measurement can be developed as tools for the broader scope of population status in large scales. This study may also apply to other carnivores (Webbon et al., 2004). Acknowledgements This work was supported by an NRF (National Research Foundation of Korea) [Young Researcher Program (2018R1A6A3A01013478)] and [Basic Research (2016R1D1A1B01009492)]. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecolind.2019.105844. References Allouche, O., Tsoar, A., Kadmon, R., 2006. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43,

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