Ecological and life-history traits predict temporal trends in biomass of boreal moths

Abstract

1. Dramatic insect declines, and their consequences for ecosystems globally, have received considerable attention recently. Yet, it is still poorly known if ecological and life-history traits can explain declines and whether insect decline occurs also at high latitudes. Insects' diversity and abundance are dramatically lower at high latitudes compared to the tropics, and insects might benefit from climate warming in high-latitude environments.
2. We adopted a trait- and biomass-based approach to estimate temporal change between 1993 and 2019 in Finnish macro-moth communities by using data from 85 long-running light traps. We analysed spatio-temporal variation in biomass of moth functional groups with Joint Dynamic Species Distribution Models while accounting for environmental variables.
3. We did not detect any declining trends in total moth biomass of moth functional groups, and most groups were stable over time. Moreover, biomass increased for species using coniferous trees, lichens, or mushrooms as hosts, multivoltine species, as well as monophagous and oligophagous species feeding on trees. We found that length and temperature of the growing season, winter climatic conditions, and habitat structure all partially explained variation in moth biomass.
4. Although boreal moth communities are rapidly changing due to species turnover, in terms of total biomass they seem to contradict the trend of dramatic insect declines observed globally. This may lessen the immediate possibility of negative bottom-up trophic cascades in boreal food webs.

INTRODUCTION

Declining insect abundance following anthropogenic change, has been reported repeatedly in recent years (Habel, Trusch, et al., 2019; Hallmann et al., 2017; Janzen & Hallwachs, 2019; Lister & Garcia, 2018; Seibold et al., 2019; Wepprich et al., 2019). Such loss of insects has potentially severe consequences for ecosystem functioning (e.g., predator–prey relationships) and ecosystem services (e.g., pollination), since insects play ecologically important roles in almost all terrestrial ecosystems (Dirzo et al., 2014; Wagner, 2020; Walther, 2010). Although insect decline has received much attention, the responses of insects to environmental change are complex and heterogeneous, and insects are not declining everywhere (e.g., Crossley et al., 2020; Macgregor et al., 2019; Montgomery et al., 2020; van Klink et al., 2022). Furthermore, temporal abundance trends differ between insect taxa (Crossley et al., 2020; Engelhardt et al., 2022; van Klink et al., 2022), and biomass trends appear to depend on the spatio-temporal resolution of the studies. For example, Macgregor et al. (2019) concluded that estimates of long-term biomass change may be sensitive to the length of the time series and the number of sampling sites.

Global change affects distribution, occurrence, diversity and phenology of insects (Forister et al., 2010; Fox et al., 2014; Habel et al., 2021; Pilotto et al., 2020; Raven & Wagner, 2021; Wagner et al., 2021; Wilson & Fox, 2021). Although there are many insect abundance datasets from temperate environments (e.g., Hallmann et al., 2017; Wagner et al., 2021), northern high-latitude environments are underrepresented. Nonetheless, climate warming is more pronounced in these regions (IPCC, 2014; Rantanen et al., 2022) and species turnover is hence likely to be stronger at high latitudes (Antão et al., 2022; Pilotto et al., 2020). There are two main processes concerning insect abundance change in high-latitude environments. On the one hand, insects are generally a low-latitude taxon with the highest diversity and abundance in the tropics, and relatively low diversity and abundance at high latitudes. It is thus expected that climate warming increases insect abundance at high latitudes as it supports influx of new species. On the other hand, land use change, eutrophication, pesticide use, light pollution and climate change (negative effects on northern species) are expected to lead to insect decline. Both abundance declines (Antão et al., 2020; Hällfors et al., 2021) and no change in abundance and biomass (Kohonen, 2020) have been reported in Finland. Here, we aim to investigate temporal biomass trends of moths from the boreal region in Finland and to see if any of these two processes could explain the changes we observe in the biomass of moth functional groups.

In addition to uncertainties in the abundance trends of insects in general, in specific areas, and/or specific taxa, there is a lack of knowledge on the association between ecological traits and temporal abundance trends. Consequently, a trait-based approach could potentially be informative of the ecological correlates of changes in abundance. Studies analysing insect abundance change have so far focused on comparisons of taxonomic groups (Wagner, 2020), freshwater versus terrestrial species (van Klink et al., 2022), or species living in different habitats (Seibold et al., 2019; Van Swaay et al., 2006). Although some studies investigating phenological and distribution shifts in species have used information about ecological traits (Hällfors et al., 2021; Poniatowski et al., 2020; Pöyry et al., 2009), it remains insufficiently understood if and how ecological and life-history traits of insects are associated with abundance trends (Habel, Samways, & Schmitt, 2019; Habel, Trusch, et al., 2019; Hunter et al., 2014; Tordoff et al., 2022; Wepprich et al., 2019). Ecological and life-history traits are known to affect environmentally induced shifts in lepidopteran phenology (Hällfors et al., 2021; Teder, 2020) and distribution (Betzholtz et al., 2013; MacLean & Beissinger, 2017; Pöyry et al., 2009; Robinson et al., 2014; but see Angert et al., 2011; Beissinger & Riddell, 2021). However, the trait-based approach has not been used widely in studies of biomass and abundance changes in Lepidoptera (Blumgart et al., 2022; Coulthard et al., 2019; Roth et al., 2021). In temperate moth communities, the traits most strongly correlated with abundance decline are diet breadth, body size, voltinism, length of the flight period, dispersal ability and overwintering stage (Coulthard et al., 2019; Wagner et al., 2021). For instance, Coulthard et al. (2019) reported that larger-bodied moths are declining more often in the UK, and according to Roth et al. (2021), moth declines were more pronounced in host plant specialists and dark-coloured species. Here, we extend the trait-based approach to boreal moth communities.

To comprehensively understand temporal insect abundance change, there is a need to identify key environmental variables affecting short-term abundance fluctuations and long-term abundance trends. Environmental drivers that are commonly associated with global insect collapse include habitat destruction, climate change, agricultural intensification (including pesticide use), and invasive species (Fox, 2013; Thomas, 2016; Wagner, 2020; Wepprich et al., 2019). Short-term abundance fluctuations are typically influenced by weather conditions (see e.g., Pöyry et al., 2011; Roy et al., 2001). In boreal environments, the growing season is short, winter is long and severe, and uniform snow cover may last up to 7 months. Duration and temperature of the growing season determine the physiological time available for insect growth and development and the number of generations appearing per year (i.e., voltinism) (Kivelä et al., 2013, 2016; Pöyry et al., 2011), which affects abundance. In addition to conditions during the growing season, winter conditions likely affect insect abundance in boreal environments too, both through cold mortality (Leather et al., 1993) and increased energy expenditure during diapause due to climate warming (Bosch et al., 2010; Nielsen et al., 2022). All resident species living in the boreal region are adapted to winter conditions, but overwinter survival may be compromised under exceptional winters (Abarca et al., 2019). On the other hand, duration and depth of insulating snow cover affect survival of species overwintering primarily under snow. Because climate change is rapid at high latitudes (e.g., polar amplification: stronger warming at high latitudes than the global average; IPCC, 2014; Rantanen et al., 2022), climate change is expected to adversely affect cold-adapted insects, and this may translate into long-term abundance declines. Furthermore, anthropogenic changes in land cover and consequent effects on habitat structure may affect the long-term abundance trends of insects (Habel et al., 2019).

To address the poorly known temporal change in insect abundance and its trait-dependency in boreal environments, we take advantage of the spatially replicated and long-running (27 years) Finnish Moth Monitoring Scheme and of the detailed knowledge of traits of Finnish moth species. We aim to show how ecological and life-history traits as well as environmental factors affect abundances of boreal macro-moths over time. We classify species into functional groups based on species trait information for (1) larval host type, (2) diet breadth within host type, (3) overwintering stage, (4) voltinism and (5) body size. Then, to extract the spatio-temporal abundance trends of functional groups, we use Joint Dynamic Species Distribution Models (JDSDMs). Recent advancement in computational power has made this approach possible and has boosted community ecology studies (Ovaskainen et al., 2017; Thorson et al., 2016; Thorson & Barnett, 2017). Here, we take advantage of the VAST R package, which has been developed to model spatio-temporally dynamic communities (Thorson, 2019; Thorson & Barnett, 2017).

In the analysis, we also take growing season and winter climatic conditions into account, as well as habitat structure effects on moth abundance. Our aims in this study are (1) to investigate the temporal biomass trends in moth functional groups, (2) to explore the spatial variation in temporal trends of functional groups, and (3) to investigate the effect of climatic and land-cover covariates on the biomass variation of functional groups.

MATERIALS AND METHODS

Moth data

We used macro-moth abundance data from Finland. Almost 90% of these data come from the National Moth Monitoring Scheme (Nocturna), which is coordinated by the Finnish Environment Institute (SYKE). This monitoring scheme has extensive spatial coverage throughout Finland and has been running annually since 1993 in 248 locations (Leinonen et al., 2017). Sampling covers the entire adult moth activity period from early spring to late autumn, and light traps are usually emptied weekly. We supplemented these Nocturna data with data from the Värriö nocturnal moth monitoring scheme (10% of data), collected daily at the Värriö research station in Värriötunturi, north-eastern Finland (Hunter et al., 2014). This dataset has been collected since 1978 starting with 11 light traps, two of which are still running. When combining these two monitoring schemes, we only considered data from those 85 traps (out of total of 262 traps) located in a forest environment with ≥5 years of sampling between 1993 and 2019, yielding 1452 trap × year combinations (Figure 1e). These selection criteria were chosen to minimise habitat variation and to avoid any biases that might be caused by short-running traps (many short-running traps were running only in the very beginning or the very end of the time series). The final data for the abundance trend analysis included observations of more than 4.3 million individuals and 736 species (Trait data file in Supplementary material).

FIGURE 1

(a) Spatial variation in mean growing season length, (b) spatial variation in temporal trends (colour gradient) in growing season temperature sum, (c) snow cover duration and (d) number of cold days (mean daily temperature < −5°C) in Finland. Panel a shows a purely spatial variable as is, while panels b–d show rates of change over time in variables with yearly values with more reddish colours showing stronger warming of summer and shortening of winter. Panel e shows the locations of light traps (circles) and mean proportions of forest and other habitat types within a 2.5 km radius from the trap sites (i.e., pie charts within the circles). The landcover has yearly values, but temporal variation in it is relatively small.

Biomass represents the overall functionality of a species, or a group of species, within the community better than abundance, as biomass is a key variable in terms of energy flow, productivity, and food-web dynamics (Brown et al., 2004; Saint-Germain et al., 2007). Hence, we converted annual abundance into annual fresh biomass (mg) by multiplying the annual totals of species- and trap-specific numbers of individuals by species-specific body mass estimates. We used a linear regression model to estimate body mass (mg) as a function of species wingspan (mm) and body plan (i.e., stout or slender) using empirical data on fresh masses of 1542 specimens across 164 genera (9 families; 244 species; R-squared = 0.93, Table S1). We then used this model to predict the fresh body masses of all species based on their wingspans and body plan (Kinsella et al., 2020; Kohonen, 2020), as given in literature (e.g., Silvonen et al., 2014). We calculated annual total biomass per species per trap location and used the pooled biomass of species grouped together based on their traits (see below) as the response variables in JDSDMs.

Moth functional groups

We aggregated moth species into functional groups in five separate ways; we selected five ecological and life-history traits and classified all macro-moth species according to the values of these functional traits (Pöyry et al., 2017; Finnish Lepidopterology handbooks listed in the Supplementary material; Trait data file). The functional traits considered were: (1) host type, (2) diet breadth within host type, (3) overwintering stage, (4) voltinism and (5) body size (Table 1; Trait data file).

TABLE 1. Summary of the five functional traits (left column) and the consequent functional groups determined by the trait values, with an added description of the functional group characteristics and number of species in each functional group.

Trait	Functional group	Description	Number of species
Larval host type	Coniferous trees	Exclusively or mostly feeding on woody coniferous trees	32
	Deciduous trees	Exclusively or mostly feeding on woody deciduous trees	268
	Forbs	Feeding mostly on non-graminoid herbs or low-growing prostrate shrubs	63
	Graminoids	Strictly specialised to feed on graminoid herbs	83
	Herbaceous plants	Feeding on other herbaceous plants	293
	Lichens	Feeding on epiphytic lichens growing on the stems and branches of trees	21
	Other hosts	Any other hosts that do not fit the above categories	24
Larval diet breadth within host type	Lichen	Feeding on epiphytic lichens growing on the stems and branches of trees	21
	Mushroom	Mushroom-eater (a single species Parascotia fuliginaria)	1
	Monophagous-Tree	Feeding only on a single tree species	17
	Monophagous-Understory	Feeding only on a single understory plant species	46
	Oligophagous-Tree	Feeding on a few (2–4) tree species	106
	Oligophagous-Understory	Feeding on a few (2–4) understory plant species	149
	Polyphagous-Tree	Feeding on several (more than 4) tree species	175
	Polyphagous-Understory	Feeding on several (more than 4) understory plant species	265
Overwintering stage	Adult	Species overwintering (diapause) at adult developmental stage	25
	Egg	Species overwintering (diapause) at egg developmental stage	126
	Larva	Species overwintering (diapause) at larva developmental stage	299
	Pupa	Species overwintering (diapause) at pupa developmental stage	327
Voltinism	Semivoltine	A life cycle that lasts at least two years	17
	Univoltine	Species producing one generation per year	547
	Multivoltine	Species producing two or more generations per year	220
Body size	Small	<20 mg	269
	Medium	20–100 mg	278
	Large	>100 mg	237

To capture the variation among these functional groups in their spatio-temporal biomass trends over the past three decades, we analysed each of the five functional trait groupings separately. Annual total biomass of each functional group was set as response variable, which was calculated by summing the biomasses of all species belonging to a particular functional group separately for each year and each trap.

Environmental data

We used spatially interpolated weather data on daily mean temperatures and snow depths in a 10 km grid (Finnish meteorological institute; https://paituli.csc.fi/, 2021). We defined the length of the growing season as the number of days between the dates when the daily mean temperature rises above 5°C for seven consecutive days for the first time in a year and falls below 5°C for seven consecutive days in the latter half of the year. We used the mean growing season length over the period 1993–2019 to describe the spatial variation in climate across the latitudinal gradient (Figure 1a). To describe temporal changes in summer thermal conditions, we defined growing season temperature sum anomaly as the difference between sum of day degrees above a 5°C threshold accumulated during the growing season as defined above, and the mean growing season thermal sum over the period 1984–2020 (note that a longer time period was used to derive a solid estimate of the baseline climate than the duration of the moth time series; cf. Figure 1b). To describe the amount of insulating snow cover, we defined snow duration anomaly as the number of days during the previous winter when snow depth exceeded 10 cm, subtracted by the location-specific mean snow duration for 1984–2020 (Figure 1c). To describe overwintering conditions, we defined cold days anomaly as the difference between the number of days with mean daily temperature below −5°C during the previous winter, and the mean number of cold days over the period 1984–2020 (Figure 1d). For summer variables, we used values from the previous summer and, for winter variables, those from the winter preceding the summer of moth trapping, as they affect the sizes of the founder populations in the current year. Finland was divided into 84,571 grids of 2 × 2 km, and each of the above-mentioned climate variables were extracted for the geographical centroid of each grid, for each year.

Land cover data were obtained from the European Space Agency (ESA Land Cover CCI project team & Defourny, 2019) for the period 1992–2020 (http://maps.elie.ucl.ac.be/CCI/viewer/download.php, 2021). We reclassified the original categories into forested (i.e., deciduous and coniferous combined) and non-forested (i.e., all other land types) categories. Then, we calculated the percentage of forested land in a 2.5 km radius around each 2 × 2 km grid centroid for each year, and we used this as a proxy of land cover. The 2.5 km radius was chosen as it entails dispersal ranges of several moth species (e.g., Ovaskainen et al., 2019; Slade et al., 2013; see also flight mill estimates of flight distances of noctuid moths by Jones et al., 2016), so that a trap should mainly sample the species pool present within that area.

Joint Dynamic Species Distribution Model

We analysed spatial and spatio-temporal variation in macro-moth biomass with JDSDMs, fitted as Vector-Autoregressive Spatio-Temporal models by using the VAST package (release number 8.2.0; Thorson, 2019; Thorson & Barnett, 2017) in R version 4.1.2. (R Core Team, 2021). JDSDMs, as implemented in VAST, simultaneously analyse correlated spatial and spatio-temporal variation in population densities of multiple species while accounting for effects of environmental variables on occurrence and abundance of species (Thorson et al., 2016; Thorson & Barnett, 2017). VAST is a powerful tool that can be used to model spatio-temporally dynamic moth biomass data.

Instead of using species-specific biomasses as response variables, we defined the response variables of the multivariate JDSDMs to be the functional-group-specific biomasses. With this approach, we reduced the species pool from 736 species to three to eight functional groups, depending on the trait categories in the five alternative groupings, which in turn facilitated JDSDM modelling, and enabled us to draw inferences on how ecosystem-level services and functions may have changed in the past three decades. We repeated the analysis for each of the five different functional groupings of species, based on the above-mentioned ecological and life-history traits (see Table 1). On the grounds of observed biomass of functional group c at site s and year t, VAST estimates the biomass density (biomass per unit area), d(s, c, t) for each functional group.

Four climatic variables (i.e., anomaly of growing season temperature sum in the previous year, growing season length, anomaly of snow duration in the previous winter and anomaly of cold days in the previous winter) and one land cover variable (i.e., percentage of forested land within a 2.5 km radius around trap sites) were set as covariates in the analyses. All the covariates were standardised prior to analyses (i.e., subtracted by mean and divided by standard deviation).

We specified the VAST model as a Poisson-link delta model that approximates a Tweedie distribution (Thorson, 2018). In delta models, the probability distribution for data $b\left(s,c,t\right)$ is parsed into two components representing (i) occurrence probability $r_1\left(s,c,t\right)$ for location $s$, functional group $c$ and year $t$, and (ii) biomass of functional group $c$, $r_2\left(s,c,t\right)$, given that functional group $c$ is predicted to occur at location $s$ and year $t$.

Lognormal and Gamma distributions for biomass were evaluated. Gamma distribution models fitted the data better than models using lognormal distribution (ΔAIC > 2; Table S2). Therefore, the probability distribution of biomass data was specified as:

$$\mathrm{Pr}\left(b_{s,c,t}=B\right)=\left\{\begin{array}{ll}1-r_1\left(s,c,t\right)& \mathit{\text{if}}B=0\\ r_1\left(s,c,t\right)\times \mathit{\text{gamma}}\left\{B\mid r_2\left(s,c,t\right),{\sigma }_m^2\left(c\right)\right\}& \mathit{\text{if}}B>0\end{array}\right.$$ (1)

where $r_1\left(s,c,t\right)$ is the probability that at least one species belonging to functional group c occurs in site s, in year t (i.e., biomass of functional group c > 0), and $r_2\left(s,c,t\right)$ is the biomass of functional group c given that it occurs in site s, in year t. The residual variance in biomass of the gamma-distributed probability distribution is ${\sigma }_m^2$.

To make the models computationally efficient and covariate effects easier to interpret, we used the Poisson-link specification of the delta model which facilitates expressing $r_1\left(s,c,t\right)$ as a function of ‘density of individuals’ $N\left(s,c,t\right)$ (i.e., number of individuals per unit area), and $r_2\left(s,c,t\right)$ as a function of both $N\left(s,c,t\right)$ and $W\left(s,c,t\right)$ ‘average individual body mass’ (i.e., mean body mass of an individual; Thorson, 2018, 2019):

$$r_1\left(s,c,t\right)=1-\exp \left(-a_i\times \exp \left(N\left(s,c,t\right)\right)\right)$$ (2)

$$r_2\left(s,c,t\right)=\frac{a_i\times \mathit{exp}\left(N\left(s,c,t\right)\right)}{r_1\left(s,c,t\right)}\times \exp \left(W\left(s,c,t\right)\right)$$ (3)

where $a_i$ is the sampling area (km²) that is used to estimate the functional groups' densities (biomass/km²). We assumed that a light trap samples moths within a circular area of 0.031 km², that is, within 100 m radius from the trap. We acknowledge that the effective sampling area likely varies among species (see e.g., Merckx & Slade, 2014), habitats, dates (due to seasonal variation in night duration and darkness) and sites (due to spatial variation in night duration, darkness and habitat characteristics). However, in this analysis, the sampling area only affects the scale of the density estimates (i.e., biomass/km²), and not the relative densities among sampling locations and times, the relative differences being important for our inferences. Hence, the specific choice of the sampling area does not affect our inferences.

In this specification of the delta model, density of individuals $N\left(s,c,t\right)$ and average individual body mass $W\left(s,c,t\right)$ were modelled via two linear predictors that both included the spatial and spatio-temporal variation terms. The first linear predictor models the density of individuals (i.e., number of individuals per km²), $N\left(s,c,t\right)$,

$$\ln \left(N\left(s,c,t\right)\right)={\beta }_1\left(c\right)+\sum _{f=1}^{n_{\omega 1}}{\mathit{L}}_{\omega 1}\left(c,f\right){\omega }_1\left(s,f\right)+\sum _{f=1}^{n_{\epsilon 1}}{\mathit{L}}_{\epsilon 1}\left(c,f\right){\epsilon }_1\left(s,t,f\right)+\sum _{j=1}^5{\gamma }_j{x}_j\left(s,t\right)+\sum _{f=1}^{n_{\eta 1}}{\mathit{L}}_{\eta 1}\left(c,f\right){\eta }_1\left(\upsilon ,f\right)$$ (4)

while the second linear predictor models average individual body mass, $W\left(s,c,t\right)$,

$$\ln \left(\mathrm{W}\left(s,c,t\right)\right)={\beta }_2\left(c\right)+\sum _{f=1}^{n_{\omega 2}}{\mathit{L}}_{\omega 2}\left(c,f\right){\omega }_2\left(s,f\right)+\sum _{f=1}^{n_{\epsilon 2}}{\mathit{L}}_{\epsilon 2}\left(c,f\right){\epsilon }_2\left(s,t,f\right)+\sum _{j=1}^5{\delta }_j{x}_j\left(s,t\right)+\sum _{f=1}^{n_{\eta 2}}{\mathit{L}}_{\eta 2}\left(c,f\right){\eta }_2\left(\upsilon ,f\right)$$ (5)

where ${\beta }_{\mathrm{i}}\left(c\right)$ (i = {1, 2}; 1 refers to the first linear predictor and 2 to the second one) is the intercept for density of individuals (i = 1) or average individual body mass (i = 2) in functional group c in year t, ${\omega }_{\mathrm{i}}\left(s,f\right)$ is the spatial variation among locations s for factor f, and ${\epsilon }_{\mathrm{i}}\left(s,t,f\right)$ represents spatio-temporal variation across years t, and locations s. ${\eta }_i\left(\upsilon ,f\right)$ represents random variation in catchability among traps for each factor f (of ${n_{\eta }}_i$ factors representing overdispersion, i.e., light trap IDs). Parameter ${\gamma }_j$ describes the effect of the environmental covariate j (j = {anomaly of growing season temperature sum of previous year, growing season mean length, anomaly of snow duration, anomaly of cold days, percentage of forested land}) on density of individuals, x_j(s,t) being the value of covariate j at site s in year t. Similarly, δ_j is the effect of covariate j on average individual body mass. ${\mathit{L}}_{\mathit{\omega i}}\left(c,f\right)$, ${\mathit{L}}_{\mathit{\epsilon i}}\left(c,f\right)$ and ${\mathit{L}}_{\mathit{\eta i}}\left(c,f\right)$ are loadings matrices for spatial, spatio-temporal and catchability covariation (here, light trap IDs), respectively, specifying the relationship between $n_{\mathit{\omega i}}$, $n_{\mathit{\epsilon i}}$ and $n_{\mathit{\eta i}}$ latent factors, f, for density of individuals (i = 1) or average individual body mass (i = 2) in functional group c. The choice of the number of latent factors was based on the maximum number of factors that could be included without hindering model convergence. For spatial and spatio-temporal variation in both linear predictors, we included three latent factors for host type, and two factors for spatial and spatio-temporal variation in both linear predictors for voltinism, overwintering stage and body size. For the diet breadth and host plant group, five and four factors for spatial and spatio-temporal variation in first linear predictor, and four and five factors for spatial and spatio-temporal variation in the second linear predictor were included, respectively.

We used the ‘grid’ method for spatial modelling and set 100 spatial knots (except for 95 in voltinism group due to model convergence issues). In this method, VAST defines the locations of knots as the centroids of grid cells that have at least one sampling site, which produces a systematically distributed network of knots to be assigned to each sample. Spatial ω_i(s, f) and spatio-temporal ε_i(s, t, f) random effects were modelled at the knot locations as Gaussian random fields following multivariate normal probability distributions. VAST uses stochastic partial differentiation equations (Lindgren et al., 2011) to approximate Gaussian random fields using package R-INLA (Lindgren & Rue, 2015). The Gaussian random fields are used for estimating all spatial processes (see Thorson et al., 2015 for details).

We defined Gaussian random fields to specify the spatial variation as constant over time (Equation 6), and spatio-temporal variation was set without temporal structure (i.e., independent among years; Equation 7) for both linear predictors:

$${\omega }_i\left(s,f\right)~\mathit{MVN}\left(0,{\mathit{R}}_{\mathit{i}}\right)$$ (6)

$${\epsilon }_i\left(s,f,t\right)~\left\{\begin{array}{ll}\mathit{MVN}\left(0,{\mathit{R}}_{\mathit{i}}\right)& \text{if}\mathrm{t}=t_1\\ \mathit{MVN}\left({\epsilon }_i\left(s,f,t-1\right),{\mathit{R}}_i\right)& \text{if}\mathrm{t}>t_1\end{array}\right.$$ (7)

where R_i is a spatial correlation matrix approximating spatial covariation in density of individuals (i = 1) and average individual body mass (i = 2), among locations s (s = 1, …, k). Spatial and spatio-temporal Gaussian random fields (${\sigma }_{\omega }^2$ and ${\sigma }_{\epsilon }^2$, respectively) had the variance of 1 to ensure that the covariance among functional groups is defined by the loadings matrix for that term. Spatial correlation between neighbouring locations generally declines with an increased distance between the two locations (Toblers' law of geography; Tobler, 1970). To account for this correlation, we specified a Matèrn correlation function with smoothness v = 1, and an estimated rate parameter κ_i (i = {1,2}) specifying the distance at which locations are essentially uncorrelated, as well as an estimated matrix H specifying geometric anisotropy (asymmetry in decorrelation distance to different directions; see Thorson et al., 2016; Thorson & Barnett, 2017 for details).

We set functional group-specific intercepts ${\beta }_{\mathrm{i}}\left(c\right)$ to be constant among years and treated them as fixed effects. Variance in spatial (${\sigma }_{\omega }^2$) and spatio-temporal (${\sigma }_{\epsilon }^2$) variation, factor loadings matrices (${\mathit{L}}_{\mathit{\omega i}}$, ${\mathit{L}}_{\mathit{\epsilon i}}$), effect of environmental covariates ${\gamma }_j$ and δ_j and the parameters governing the geometric anisotropy (H) and decorrelation distance (к) in the Matèrn correlation function were also treated as fixed effects. Spatial ${\omega }_i\left(s,f\right)$ and spatio-temporal ${\epsilon }_i\left(s,f,t\right)$ variations were treated as random effects.

All parameters were estimated with maximum marginal likelihood using Template Model Builder (R package TMB; Kristensen et al., 2016). TMB uses Laplace approximation (Skaug & Fournier, 2006) to estimate fixed effects by maximising the log-marginal likelihood of fixed effects in the R statistical environment (R Core Team, 2019) after integrating over the random effects. The likelihood is then optimised, and standard errors are obtained using generalisation of the delta method. Random effects are then predicted based on the joint likelihood of random effects and data, given the maximum likelihood estimates of the fixed effects (more details in Thorson et al., 2015). We considered all parameters whose 95% confidence interval did not encompass zero as statistically significant.

The Autumnal moth (Epirrita autumnata) shows outbreaks with intervals of 8–11 years in northern Fennoscandia (Jepsen et al., 2008; Tenow, 1972; Tenow et al., 2007). Models including this species—even when excluding the highest observations exceeding 10,000 individuals per trap per year—did not converge. Hence, we completely excluded E. autumnata from our analyses, which is a practice that was previously done in other Finnish moth studies (Dallas et al., 2020; Kohonen, 2020). We repeated the analysis for each of the five functional groupings based on the five ecological and life-history traits (Table 1) and used all five environmental covariates in each case. However, the model for voltinism-based grouping did not converge when including all five environmental covariates. Therefore, we fitted models to the voltinism-based grouping data so that one of the environmental covariates was excluded at a time, and then chose the model with the lowest AIC value for inferences. None of the models for body size grouping converged, most probably due to high within group variations, thus we do not report any results from this grouping. We investigated goodness-of-fit of the VAST models on the grounds of the diagnostic residual plots produced with the tools available in the R package ‘DHARMa’ (Hartig, 2020). Lastly, we used a 7-fold cross-validation procedure to evaluate the model performance, and calculated proportion of deviance explained by using tools in the VAST package to measure model explanatory power (Table S3).

Analysing temporal biomass trends

The year-specific, Finland-wide total moth biomass estimates, produced by the VAST models, were used as response variables in generalised least squares linear models to analyse the temporal biomass trends of these functional groups. The function ‘gls’ from R package ‘nlme’ (release number 3.1–157; Pinheiro and Bates, 2022) was used. For consistency among functional groups, and in order to set the scale easy-to-read, all total biomass estimates were standardised prior to the analysis, by dividing their value (in milligrams) by 10⁸ (largest value with which the biomass of all functional groups could be divided while maintaining the value >1) to derive an biomass index for illustration of the results (see Figure 2). The biomass index values were ln-transformed to normalise residuals. Temporal autocorrelation in functional-group-specific total biomass estimates was modelled with the ‘corAR1’ autocorrelation function (Pinheiro and Bates, 2022).

FIGURE 2

Temporal biomass trends in moth functional groups based on (a) host type, (b) diet breadth within host type, (c) overwintering developmental stage and (d) voltinism traits. Points are estimates of total biomass in Finland derived from the Joint Dynamic Species Distribution Models and converted (divided by 10⁸ and ln-transformed) into a biomass index. The lines are regression lines fitted to the functional-group-specific biomass index values, and the colour shading around the lines shows the 95% confidence intervals of the lines. The Y-axis range is the same for all panels to make figures comparable.

To depict spatial variation in temporal biomass trends of 22 functional groups (Figure 3), we used the predicted yearly moth biomasses at the VAST models' spatial knots (systematically arranged 95 locations in voltinism group and 100 locations in three other groups). We then used linear regression to ln-transformed biomass time series at each knot. Finally, we took the estimate (slope for year effect) at each knot and interpolated the values to cover the whole area of Finland (Voronoi diagram of the knots) using nearest neighbour (five nearest) interpolation.

FIGURE 3

Spatial variation in temporal biomass (mg) trends over 27 years (1993–2019) across Finland in functional groups based on (a) host type, (b) diet breadth within host type, (c) overwintering developmental stage and (d) voltinism. Each panel corresponds to a separate analysis.

RESULTS

Temporal biomass trends in moth functional groups in Finland

The biomass of 15 functional groups was stable, while seven functional groups increased in biomass over the study period (1993–2019) in Finland. Adult biomass of species using coniferous trees and lichens as larval hosts increased, but all other functional groups in host type trait were stable over time (Table S4, Figure 2a). Half of the diet breadth within host type functional groups (i.e., lichen, monophagous-trees, mushroom and oligophagous-trees; note that lichen-feeding moths are present in two of the traits groupings; in host type and in diet breadth within host type) showed positive biomass trends, while the four other groups remained stable (Table S4, Figure 2b). Biomass of all functional groups based on the overwintering developmental stage remained stable too (Table S4, Figure 2c). The biomass of multivoltine species increased, while univoltine and semivoltine species were stable over time (Table S4, Figure 2d).

The models estimated moth biomass over the whole area of Finland (338,440 km²). Conifer-feeders increased by 337 (95% CI [310, 366]) tons per decade and lichen-feeders by 99 [76, 130] tons per decade in the whole country, so the total biomass added by conifer-feeding moths (32 species) was more than 3.5 times higher than that of lichen-feeding moths (21 species) in the host type trait group. Multivoltine species (220 species) increased by 2692 [2505, 2892] tons per decade in Finland. As for the diet breadth within host type trait groups, lichen-feeders (21 species) increased by 118 [88, 158] tons per decade, monophagous tree-feeders (17 species) by 38 [31, 47] tons per decade, mushroom-feeder (1 species) by 3.5 [3, 4.3] tons per decade, and oligophagous tree-feeders (106 species) by 1574 [1477, 1677] tons per decade. The total biomass added by oligophagous tree-feeding species was 41.2 times larger than that of monophagous tree-feeders, 13.45 times higher than that of lichen-feeders, and 527 times higher than that of the mushroom-feeding moth. We highlight that these biomass predictions are country-wide and should not be misinterpreted as trap-level biomass predictions.

Spatial variation in temporal biomass trends of moth functional groups in Finland

There was spatial variation in the temporal biomass trends of all functional groups. The increase in conifer-feeders occurred country-wide, with the lowest rates in Central Finland, and the highest rates in the north and the south, while moths feeding on deciduous trees declined in much of the southern half of Finland (Figure 3a). The lichen-feeding species increased in biomass almost in all parts of the country, except for coastal areas in the south and the west (Figure 3a). All the functional groups in the diet breadth and host type grouping seem to follow the same spatial pattern but at different scales (Figure 3b), with the weakest biomass trends occurring along the coast, as well as in the northwest and southeast. The steepest biomass increases for lichen-eating moths in the diet breadth and host type grouping took place in the southern half of the country, except for coastal areas in the south and southwest; however, the increasing trend is country-wide (Figure 3b). The increase in mushroom-eating, monophagous understory-feeding, and oligophagous understory-feeding moths seem to be aligned with the decreasing trend in the number of cold days and increased towards the east (compare Figure 3b with Figure 1d). In adult-overwinterers, the increasing temporal biomass trend was strongest in the north, and there was biomass loss in the central-western part of the country (Figure 3c). Egg-overwinterers increased steepest in the northern and north-eastern part of the country (Figure 3c). In multivoltine species, temporal biomass increase was lowest in the south-west, a pattern repeated slightly more strongly for univoltine moths (Figure 3d). Semivoltine species had more dramatic declines both in the southern and northern parts of the country.

Effect of covariates on biomass of functional groups

The effects of covariates on biomass of functional groups were separately estimated for the two linear predictors of the model (Table S5): density of individuals (i.e., density of individuals per unit area; hereafter density) and average body mass (i.e., average body mass of an individual belonging to the group; hereafter body size). In the functional group context, covariate effects on the body size predictor (second linear predictor) can be interpreted as environmental effects on species turnover: for example, longer growing season might favour increasing abundance of larger-bodied or smaller-bodied species in each functional group. Here, we explain the results for those functional groups that were associated significantly with the environmental variables considered. However, Figure 4a–e shows the results for all functional groups, and we ask readers to check the non-significant results from the figures (see also Table S5).

FIGURE 4

Effect of five environmental covariates on the density of individuals (solid line) and average body size (dashed line) of functional groups. (a) Effect of mean growing season length (i.e., the number of days between the dates when the daily mean temperature rises above 5°C for seven consecutive days for the first time in a year and falls below 5°C for seven consecutive days in the latter half of the year. (b) Effect of growing season temperature sum anomaly of previous year (i.e., the difference between sum of day degrees above a 5°C threshold accumulated during the growing season, and the mean growing season thermal sum over the period 1984–2020). Note that the effect of this variable on voltinism could not be assessed in our models due to convergence issues. (c) Effect of anomaly of snow duration (i.e., the number of days during the previous winter when snow depth exceeded 10 cm, subtracted by the location-specific mean snow duration for 1984–2020. (d) Effect of anomaly of cold days (i.e., number of days with mean daily temperature below −5°C during the previous winter, and the mean number of cold days over the period 1984–2020. (e) Effect of percentage of forested land. Points and squares stand for the estimated coefficient and whiskers the 95% confidence intervals. Red and blue colours indicate positive and negative effects, respectively. Grey colour stands for no effect.

Mean growing season length was negatively associated with the density of semivoltine, mushroom-feeding, oligophagous-understory-feeding and polyphagous-understory-feeding species, and positively with adult-overwinterers (Figure 4a). It was negatively associated with the body size of adult- and egg-overwinterers and positively with the body size of conifer-feeders, species feeding on other-hosts, as well as monophagous-understory-feeding and oligophagous-understory-feeding species (Figure 4a).

Anomaly of growing season temperature sum in the previous year was negatively associated with the body size of monophagous-tree-feeders and positively associated with the body size of the mushroom-feeder (Figure 4b). This suggests that warmer growing seasons increase the abundance of smaller-bodied monophagous-tree-feeders (Figure 4b). The results on the body size of the mushroom-eating moth should be interpreted with caution, as our data did not include body size variation for this single species (Parascotia fuliginaria). For egg-overwinterers, the anomaly of growing season temperature sum in the previous year was negatively associated with density (Figure 4b) and positively with body size (Figure 4b). Hence, warmer growing seasons favour lower densities and large-bodied species of egg-overwinterers (Figure 4b).

Anomaly of snow duration in the preceding winter was positively associated with the density of species feeding on coniferous trees (Figure 4c), suggesting that a long-lasting snow cover benefits these species. Conversely, anomaly of snow cover was negatively associated with the densities of species feeding on lichens and other hosts (from the host type grouping), lichen-feeding species (from the diet breadth within host type grouping), and multivoltine species (Figure 4c), suggesting that these groups of species benefit from shorter-lasting snow cover. Anomaly of snow duration was negatively associated with the body size of species feeding on coniferous trees, while its association was positive with the body size of multivoltine species (Figure 4c), indicating that a long-lasting snow cover helps small conifer-feeders and large multivoltine species. Anomaly of cold days in the preceding winter was negatively associated with the body size of species using lichens as hosts, lichen-feeders and monophagous tree-feeders (Figure 4d), suggesting that cold winters are favourable for small-bodied species in these groups. Anomaly of cold days in the preceding winter had no association with the density of any functional group (Figure 4d).

Percentage of forested land around trap sites was positively associated with the density of species using coniferous trees as hosts, semivoltine species, adult- and egg-overwinterers and monophagous-understory-feeders, while the density of species using herbaceous plants as hosts was negatively associated with this forest coverage (Figure 4e). Forest coverage was negatively associated with the body size of conifer-feeding, graminoid-feeding, multivoltine and adult-overwintering species (Figure 4e), suggesting that increasing forest cover favours small-bodied species in these groups.

DISCUSSION

We did not detect any country-wide declining trends in biomass of macro-moth functional groups when analysing the long-term systematic monitoring data in Finland from the past three decades. With our functional-group- and biomass-based approach, we found a country-wide increase in biomass of species feeding on conifers or lichens, multivoltine species, monophagous and oligophagous tree-feeders and the single mushroom-eating moth. Country-wide biomass remained stable in all the other functional groups considered in this study. These results are consistent with Hunter et al. (2014) who showed that abundances of 90% of 80 forest moths were either stable or increasing over 32 years in a subarctic location, and with Kozlov et al. (2010) who reported that 74% out of 42 abundant species were stable, 14% increased, and 14% decreased in another subarctic location over 26 years. However, a species-level analysis using the same monitoring data as the present study, but over a shorter period (1993–2012) found an abundance decline (Antão et al., 2020; but see Kohonen, 2020).

While our study shows that the biomass of moth functional groups–as defined by key ecological and life-history traits–are either stable or increasing in Finland, most previous studies of moth communities in other areas have reported long-term declines (Fox, 2013; Franzén & Johannesson, 2007; Groenendijk & Ellis, 2011; Valtonen et al., 2017). Comparing our results to the earlier reports from Finland (Antão et al., 2020; Kohonen, 2020) provides some insights into the importance and effect of temporal resolutions of time series in detecting abundance and biomass trends. Antão et al. (2020) reported a decline in the total abundance of moths over 20 years (1993–2012) in Finland, associated with a decline of the 10 most abundant species, and an increase in species richness due to poleward range shifts. However, Kohonen (2020) investigated four more years (1993–2016) and did not detect any long-term trends in either abundance or biomass of Finnish moths. Similar sensitivity of insect abundance trends to the length of time series and number of sampling locations was previously shown in the US and the UK (Crossley et al., 2020; Macgregor et al., 2019, 2021). Crossley et al. (2020), reported a lack of overall increase or decrease in the abundance of insects across US long-term ecological research sites, even though some taxa decreased, increased, or remained stable in different sites. Similarly, Macgregor et al. (2021) reported a lack of an overall trend in moth biomass in the UK over 50 years, despite high between-year variability.

The trait-based approach is informative of the ecological correlates of abundance changes, and the biomass approach measures abundance changes in units relevant to ecosystem functioning. Biomass is a key variable in terms of energy flow, productivity and food-web dynamics (Brown et al., 2004). Hence, biomass better represents the overall functionality of a species or a group of species within the community, and biomass of a specific trophic level better represents resource flows within an ecosystem than abundance (Saint-Germain et al., 2007). Using moth biomass allows us to draw inferences about the consequences of moth abundance changes, or lack of changes, on ecosystem functioning. As moth biomass seems either stable or increasing across some functional groups, food availability for insectivores should be stable or increasing, thus supporting ecosystem functionality. However, despite the benefits of the trait- and biomass-based approach, we cannot exclude the possibility that some species are declining. In fact, we know that there are more declining than increasing moth species in the dataset in 1993–2012 (Antão et al., 2020), but some species have increased a lot, balancing the overall change in total abundance and biomass (Kohonen, 2020). For example, increasing abundance of larger-bodied species could mask decreasing abundance of smaller-bodied species. Such turnover under temporally stable or increasing biomass was suggested by Pöyry et al. (2017). Moreover, turnover of rare and abundant species stays undetected by the biomass-based approach, if body sizes of rare and abundant species are similar.

The environmental covariates we addressed were associated with temporal biomass changes in most of the functional groups (15 out of 22), both in functional groups whose biomasses were temporally stable and increasing over time. Conditions during the previous summer (growing season temperature sum anomaly in the previous year) capture temporal fluctuations in local thermal conditions and are expected to affect the sizes of the moth founder populations in the current year (Roy et al., 2001). The climatic gradient explained density and body size variation in some groups, but not for multivoltine species. The unexpected lack of effect for multivoltine species can be explained if summer warming occurs mainly in late summer when it is unlikely to affect the induction of diapause versus direct development. Note also that we could not include growing season temperature sum in the previous year as a predictor when modelling voltinism-based groups due to model convergence issues. The overall increasing trend in biomass of multivoltine species shown here was expected because increasing voltinism results in an increasing number of individuals flying during the growing season, and climate warming expands the area where multivoltinism is possible (Altermatt, 2010; Pöyry et al., 2011; Virtanen & Neuvonen, 1999). With the northward expansion of many lepidopteran species, it is highly likely that new multivoltine species are also arriving in Finland (Pöyry et al., 2009, 2011).

We used anomalies of snow cover duration and number of cold days to describe overwintering conditions. The duration of snow cover is shortening, especially in southern Finland, and the number of cold days is decreasing, especially in eastern and northern Finland, due to climate change (Figure 1). Milder winters (i.e., shorter snow cover, fewer cold days) had a positive effect on the density of larger species of lichen-feeders. While this partially explains the rapidly rising biomass of these species, their food source, especially epiphytic lichens growing on the stems and branches of trees, have also recovered during the study period. This is consistent with Coulthard et al. (2019) reporting increasing abundance of lichen-feeding moths because of the increase in nitrophilic lichens in agricultural landscapes in the UK in recent decades. Shorter snow cover duration resulted in higher densities and a bias towards smaller-bodied multivoltine species. Winter severity effects on multivoltine species probably arise because milder winters likely advance spring, which increases the probability of the emergence and/or size of a second generation. The increased density of multivoltine species is also consistent with earlier studies showing that multivoltine lepidopterans show more positive population trends than univoltine or semivoltine species (Macgregor et al., 2019; see also Van Swaay et al., 2006; WallisDeVries, 2014). Pöyry et al. (2017) suggested that abundance of multivoltine species should increase with increasing abundance of nitrophilous plants, which results from nitrogen deposition. Habitat structure effects were captured by the annual percentages of forest coverage around the sampling locations. As expected, forest-dwelling species benefited from increasing forest coverage. Moreover, density of semivoltine species also increased with increasing forest coverage, which is expected as most of the semivoltine species are forest dwellers.

We found no associations between overwintering developmental stage and biomass trends. Earlier studies hypothesized that the egg- and adult-overwinterers benefit from warmer summer conditions, and species that overwinter as a pupa are more likely to produce more generations per year (Teder, 2020; Virtanen & Neuvonen, 1999). However, increase in voltinism is maladaptive if environmental change results in emergence of extra generations so late in the season that overwinter survival fails (Blackshaw & Esbjerg, 2018; Pöyry et al., 2011; Van Dyck et al., 2014). Species overwintering as a pupa are vulnerable to extension of the warm period in the end of summer and autumn (Nielsen et al., 2022), and this may hold for egg-overwinterers too. Our results suggest that egg- and adult-overwinterers are smaller in southern than northern Finland. Moreover, increasing biomass of adult-overwinterers in northern Finland suggests that they benefit from longer-lasting snow cover. Biomass of egg-overwinterers increased in northern and north-eastern Finland, which coincides with the pattern of a decrease in the number of cold days. The growing season is, on average, getting warmer all over the country, which favours higher relative abundance of larger egg-overwintering species, but in lower densities. Therefore, these two opposing factors–density versus body size–may have levelled out biomass trends of the egg- and adult-overwintering groups. Furthermore, increase in energy expenditure during diapause may be another mechanism that prevents milder winters to be beneficial for adult-, egg- and pupal-overwinterers. Unlike species which overwinter as larvae, they are unable to replenish energy reserves during winter. Another issue many adult-, egg- and spring-flying pupal-overwinterers might face is matching larval hatching with tree budburst (Both et al., 2009; Visser & Holleman, 2001). Such phenological mismatching is suggested by the spatial pattern seen in adult overwintering and deciduous tree-feeding species–many of which are shared between the groups–where the southern half of the country displays negative trends.

A possible caveat in the Nocturnal moth monitoring dataset is that the sampling sites do not have either a random or systematic distribution. The sites have typically been selected by convenience of sampling and the availability of electricity, or to cover particularly good spots for trapping rare species. Thus, land use changes around the sampling sites may differ from typical nation-wide patterns. Secondly, changing phenology may complicate abundance estimation of moths, as light trap efficiency is reduced during short and light summer nights at high latitudes. For example, advancing late-summer flight of adult overwintering species may reduce their attraction to light traps and vice versa for spring-flying species. Finally, the macro- to microclimate relationship is not straightforward (Aalto et al., 2022), which complicates inferences concerning climate or weather effects on functional groups including species that use different microhabitats. Extreme and even short-term weather events like droughts, heatwaves, midwinter warm spells and completely snow-free winters may affect moth abundances more than the average conditions over the growing season or winter.

In our study, we showed that boreal moth functional groups perform better than expected on the grounds of recent results from many other parts of the world. Some functional groups even increased in biomass. These results suggest that moth-mediated ecosystem functions and services are not under immediate threat in Finland. Yet, we need to be cautious because, with our approach, we cannot exclude the possibility that some species are declining, or that there are negative and time-lagged community-level effects of climate and habitat changes (see e.g., Hanski et al., 1996; Tilman et al., 1994). The increasing total moth biomass trends in functional groups reported in this study should not be misinterpreted as increase in abundance at the species level, because species-level patterns cannot be inferred from our community-level approach. Ecological and life-history traits of moths explain some temporal biomass trends, and thus call for similar approaches in other studies on insect abundance change. Trait-based approaches will help in understanding the causes and consequences of insect abundance change and insect response to (a)biotic environmental change (but see Tordoff et al., 2022). As such, we strongly encourage such approaches to be used in future studies.

AUTHOR CONTRIBUTIONS

Mahtab Yazdanian: Conceptualization; data curation; formal analysis; investigation; methodology; validation; visualization; writing – original draft; writing – review and editing. Tuomas Kankaanpää: Conceptualization; data curation; formal analysis; investigation; validation; visualization; writing – review and editing. Juhani Itämies: Resources; writing – review and editing. Reima Leinonen: Resources. Thomas Merckx: Conceptualization; writing – review and editing. Juha Pöyry: Resources; writing – review and editing. Pasi Sihvonen: Resources; writing – review and editing. Anna Suuronen: Resources; writing – review and editing. Panu Välimäki: Resources. Sami M. Kivelä: Conceptualization; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; supervision; validation; writing – original draft; writing – review and editing.

ACKNOWLEDGEMENTS

We thank Mahdi Aminikhah, Reetta Hämäläinen, Mira Kajanus and Matthew Nielsen, who supplied valuable feedback on the manuscript. The Finnish Ministry of the Environment supported the National Moth Monitoring Scheme (Nocturna), and we thank volunteers for identifying moth species. We acknowledge CSC—IT Centre for Science, Finland, for computational resources. This study was funded by the Kvantum Institute at the University of Oulu and Academy of Finland (grants 314833 and 345363 to Sami M. Kivelä).

CONFLICT OF INTEREST STATEMENT

The authors declare no conflict of interest.

Open Research

DATA AVAILABILITY STATEMENT

All data underlying the results of this study and R scripts of analyses are available in Dryad (https://doi.org/10.5061/dryad.6hdr7sr5h).