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Citation for the published paper: Tobias Jeppsson, Anders Lindhe, Ulf Gärdenfors, Pär Forslund. (2010) The use of historical collections to estimate population trends: A case

study using Swedish longhorn beetles (Coleoptera: Cerambycidae). Biological Conservation. Volume: 143 Number: 9, pp 1940-1950.

http://dx.doi.org/10.1016/j.biocon.2010.04.015

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Published in Biological Conservation 143 (2010) 1940–1950 doi:10.1016/j.biocon.2010.04.015

The use of historical collections to estimate population trends: A case

study using Swedish longhorn beetles (Coleoptera: Cerambycidae)

Tobias Jeppsson∗1, Anders Lindhe2, Ulf Gardenfors3, and Par Forslund1

1Department of Ecology, The Swedish University of Agricultural Sciences SLU, Box 7044,SE-75007 Uppsala, Sweden.

2Armfaltsgatan 16, 2 tr. SE-115 34 Stockholm, Sweden.3Swedish Species Information Centre, SLU, Box 7007, SE-750 05 Uppsala, Sweden.

Abstract

Long term data to estimate population trends among species are generally lacking. However, NaturalHistory Collections (NHCs) can provide such information, but may suffer from biases due to varying samplingeffort. To analyze population trends and range-abundance dynamics of Swedish longhorn beetles (Coleoptera:Cerambycidae), we used collections of 108 species stretching over 100 years. We controlled for varying samplingeffort by using the total number of database records as a reference for non-red-listed species. Because thegeneral frequency of red-listed species increased over time, a separate estimate of sampling effort was used forthat group. We observed large interspecific variation in population changes, from declines of 60% to severalhundred percent increases. Most species showed stable or increasing ranges, whereas few seemed to decline inrange. Among increasing species, rare species seemed to expand their range more than common species did,but this pattern was not observed in declining species. Historically, rare species did not seem to be at largerrisk of local extinction, and population declines were mostly due to lower population density and not loss ofsub-populations. We also evaluated the species’ declines under IUCN red-list criterion A, and four currentlynot red-listed species meet the suggested threshold for Near Threatened (NT). The results also suggested thatspecies’ declines may be overlooked if estimated only from changes in species range.

Keywords: Population trends, Natural history collection (NHC), Range-abundance dynamics, IUCN Red list,Species conservation, Population dynamics, museum data, Coleoptera: Cerambycidae.

1 Introduction

The current loss of biodiversity is rapid with un-precedented rates of species’ declines and extinctions[33, 47]. Local extirpations and population declinescan both have detrimental effects on ecosystems func-tion and services (i.e. [4, 36]). Although it is welldocumented that populations of species decline orgo extinct [23, 59], we lack quantitative informationon rates of population decline for the majority ofspecies. Assessing the rate of population change oftenrequires a proxy for population size, which is usu-ally acquired from standardised population countsfrom long-term inventories. Unfortunately, for mostspecies such long-term time series are not available,thus an alternative is to use other proxies of popula-tion size such as natural history collections (NHC) orhunting/fishing bag rates [53, 60]. A benefit of usingsuch alternative proxies is that they allow us to usehistorical data, whereas this is seldom available for

∗Corresponding authorEmail: [email protected]: (+47)46501636

direct population counts. This aspect is clearly valu-able, since an excessive focus on recent abundancepatterns can be misleading when analysing causes ofpopulation change, and the time window may be tooshort to reveal natural patterns of population sizefluctuations [52]. A major drawback in using histor-ical collections, however, is that such data sources inmost cases are based on non-standardised samplingmethods. Thus one needs to consider potential bi-ases in the data, such as changes in sampling effort,changes in sampling methods and the effects of in-creased knowledge on species biology.

1.1 Using NHC data for estimatingtrends

NHCs are records of biological material collectedby expeditions, professional biologists and amateurs.They encompass all major taxonomic groups, but atdifferent frequencies and time frames (from tens tohundreds of years). The ecological value of NHCs wasrecognised in the mid 1990s [8, 32, 41, 52], but also see[14], and since then studies utilizing information from

1


NHCs fall into three general categories: they aim to(1) estimate the probability that a species is or willgo extinct [43], (2) study temporal trends in speciesabundance or range [13, 27], or (3) estimate commu-nity features such as species richness [24, 42, 46].

When estimating population trends, species datafrom NHCs have several characteristics that are po-tential sources of bias: (1) No records of absences– There is usually no knowledge about species ab-sences, making it difficult to know whether a lack ofrecords is due to species absence, non-detection (e.g.from stratified presence/absence studies) or that thelocation has not been visited (e.g. for presence onlydata); the real reason is, however, usually unknown[37]; (2) Changes in sampling effort – It cannot beassumed that the sampling effort has been constantover time, because NHCs usually stem from manyyears and many sources. Therefore, the yearly num-ber of records of a specific species is not only depen-dent on the population size, but also on the yearlysampling effort; (3) Changes in spatial coverage ofsampling – Three possible biases may arise. First,if the sampled area is constant, population increasesdue to expansion of the distribution may remain un-detected. Second, if the sampled area changes withtime, perceived population changes may not be real[21, 57]. Third, different collectors may recognizea name of a location as different in extension, giv-ing the impression of changed spatial coverage; (4)Changes of collection methods – New knowledge onspecies’ biology and new collection methods may in-crease species detection and collection, resulting inapparent but not necessarily true positive popula-tion trends; (5) The attractiveness to collect a cer-tain species may change – NHCs depend on the will-ingness of collectors to collect specimens of differentspecies. The attractiveness of a species is, amongother things, influenced by the charisma of its vi-sual characteristics, its sensitivity to human-inducedhabitat changes, its rarity, its red-list status, or itsprotection status. If any of these factors change withtime, so may the collection effort for the species.

1.2 Causes of population changes

While population changes are regulated by many fac-tors, declines are often linked to a reduction or degra-dation of existing habitat. When available habitat isreduced, associated population declines will be ac-companied by a proportional reduction in the rangeof the focal species, whereas habitat degradation mayresult in population decline via a reduction in speciesdensity which is unrelated to a change in range [20].These two avenues to population decline are not mu-tually exclusive, and may occur simultaneously or intemporal sequence, e.g. changes in habitat qualitymay reduce population density in sub-populations,which then increases the risk of local extinction be-cause of demographic stochasticity and loss of geneticvariation [25]. This leads to the prediction that rangecontractions are more likely to be linked to popula-

tion declines in rare species but not necessarily incommon species. Population declines and increasedextinction risk are often positively correlated withthe degree of habitat or resource specialization of thespecies, since species that are more specialized are as-sumed to be more sensitive to environmental changes[18]. Thus, specialized species would be expected tobe overrepresented among species experiencing popu-lation decline. This expectation will, however, rest onthe assumption that the required habitat or resourceof the specialized species is diminishing or deteriorat-ing, and that population declines are mainly causedby habitat degradation.

Turning to increasing species, analogous patternsof density and range dynamics may occur. Popula-tion densities may increase without range expansionor there may be range expansions due to colonizationof new areas but no increase in density, or populationdensity and range both increase [19]. In contrast todeclining species, however, it is difficult to make anygeneral predictions on how these dynamics may re-late to the commonness of a species. One key factoris colonization ability which is a complex and difficultvariable to estimate, as it depends on dispersal abil-ity, demographic traits and degree of specialization[19]. However, common species may have alreadyfilled areas of suitable habitat; thus range cannot ex-pand in contrast to less-common species which maystill have the opportunity to invade vacant suitablehabitats [19, 28, 62]. This suggests that from a habi-tat filling aspect it can be predicted that as popu-lations increase, less-common species should expandtheir range more than common species [19]. Thus,knowing the relationship between changes in popula-tion size and range can have important implicationsfor species conservation. The question is whether ob-served changes of species’ distributions can be usedas indicators of population changes, especially whenthere are non-linear relationships between the twovariables [20]. Increasing population densities havealso been observed due to temporary crowding ef-fects following habitat destruction or fragmentation[5, 12, 58], but that is usually found on relativelysmall spatial scales. These transient effects are alsorapid in short lived species such as insects [18, 45],which makes them less relevant for long term studies.

In this study we analyse a comprehensive data setconsisting of records of Swedish longhorn beetles (Ce-rambycidae, Coleoptera), with the aim of estimatingpopulation trends over the last 100 years, and to in-vestigate whether changes in population abundancesare related to changes in population range. In theanalysis, we use the total number of yearly recordsof all species as an estimate of effort to calculate rel-ative species abundances, and account for the num-ber of active collectors. To understand the dynam-ics behind the range-abundance relationships, we testwhether changes in population size are accompaniedby changes in range, and if this interacts with rarity.We also test whether the degree of substrate special-ization explains how species’ abundances change tem-

2


porally. Finally, we use the results to evaluate speciesunder IUCN red-list criterion for population decline,and discuss implications for species’ conservation.

2 Methods

2.1 Data and variables

In Sweden, 117 species of longhorn beetles have beenfound occurring naturally in Sweden during the 19th–20th century [16]. Fifty species (approx. 45% of theSwedish species) are currently listed on the SwedishRed List [23], out of which 22 are categorized asthreatened (VU, EN or CR; see appendix for de-scription) and 5 as Regionally Extinct (RE). Nomen-clature in this study was based on Ehnstrom andHolmer[16]. Longhorn beetles are, in comparison tomany other groups of insects, well known and rela-tively easy to identify, and this has resulted in a largenumber of NHC records. Most species are saprox-ylic and their larvae depend on dying or dead-woodsubstrates, but a few species feed on alternative sub-strates such as living trees, herb roots or litter. Ingeneral, an estimated 20–25% of all forest living in-sects in Sweden depend on dead-wood substrates [54],so knowledge of population trends over the last cen-tury in one taxon using these substrates may be in-dicative of many other species utilizing the same sub-strates. Larval development times generally lie be-tween one and five years [16], but can be much longerfor some species. The adults are believed to be rel-atively short lived, although comprehensive informa-tion on longevity is almost lacking. No species pro-duces more than one generation within the same year.Changes in human land use in forests during thelast two centuries [3, 34, 56, 54] and the agriculturallandscape [29], have affected the amount and qual-ity of wood substrates, and the changes have acceler-ated over the 20th century due to intensified forestry.Many longhorn beetle species are considered to be de-clining as a consequence of these changes, with thelargest perceived threats being human-induced habi-tat destruction or degradation [16], as well as smallor few sub-populations [23].

A database of species records was compiled dur-ing 2000–2008. Here, a record refers to an individualaccount of a collected beetle, stemming either froma physical specimen or a literature account, that in-cludes information on the location and date of the col-lection event. Since few records included informationon whether one or several individuals were found, weexcluded this information in the analysis. If severalrecords existed from the same collection event, e.g.from different museum collections, duplicates wereremoved. The sources used were museums, privatecollections, records from the Swedish Species Infor-mation centre, fauna literature, entomological peri-odicals, and written reports from the Swedish En-vironmental Protection Agency and county boards.Records were also obtained from some local author-

ities, inventories of key habitats, and other reportsand publications. The museum sources include themajor natural history museums, university collec-tions, provincial museums, and former college col-lections. Collectors were first contacted collectivelythrough an advertisement in the entomological peri-odical ’Entomologisk tidskrift’ in 2001. As this re-quest generated little response, about two hundredmembers of the Swedish Entomological Society witha stated interest in beetles, wood-living insects, for-est insects or conservation were contacted individu-ally. In the end, 138 individual collectors made theirrecords or collections available. For a full descriptionof the database, see Lindhe et al.[35]. The databasespans records from the 18th century to present time,but only records from the 20th century have the qual-ity and resolution necessary for assessing changes ofspecies’ abundance. All entries were assigned geo-referred coordinates according to the Swedish geo-graphical referencing system ’Rikets nat’ (RT90).

The NHC data is the result of collections made byexpeditions, amateur entomologists and researchersand is therefore normally not standardised or ran-domly selected. The appearance of a record of oc-currence in the database is the endpoint in the chainof events where a collector: (1) visits an area wherethe species is present, (2) encounters the species, (3)collects a specimen, and (4) identifies and mountsthe specimen, (5) preserves it in a collection, (6) therecord is made available for us to study. Thus, acommon species may be encountered (2), but notcollected (3), whereas a rare species, once encoun-tered, is more likely to pass through all the steps.The number of visits to localities where a species ispresent (1) may vary over time, affecting the num-ber of collections of this species. This factor is likelyto be correlated to the number of active collectors,since more collectors probably mean a larger spatialcoverage of the search effort. For some records, suchas records based on the literature, some steps in thechain can be bypassed (No. 5) or slightly modified(No. 6 representing publication). To minimize bias inspecies trends, biases over time and space that affectthe links in the chain of events described above needto be identified and accounted for as far as possible.

To minimize the effect of changes in collec-tion methods and collector’s behaviour we excludedrecords of specimens labelled as collected in traps,records based on larvae feeding galleries, and recordsreferring to specimens hatched from larvae in col-lected substrate. Therefore, only records that can beassumed to stem from collectors actively searching forthe species in the field have been used in the analy-ses. Furthermore, all records made before year 1901or after year 2000 were excluded, the old records dueto their low information content, the very recent be-cause we expect a lag between the collecting of spec-imens and their appearance in a collection or a mu-seum, and because the database has not been contin-uously updated since the first compilation. Thus, allanalyses are based on 20th century, actively searched

3


for and handpicked specimens. In the final analy-sis the subset of the database that we used consistedof 42,931 records from 108 species. For the general-ized mixed model (see Section 2.2) we also removedrecords not tied to a specific location.

The first Swedish Red List that included longhornbeetles was published in 1986 [1, 17], and since thena varying number of longhorn beetles has been in-cluded in different versions of the Red List. Our ex-pectation was that the inclusion on a Red List led toan increased effort to survey these species especiallyin the 80s and 90s. To take into account that red-listed and non red-listed species have been sampleddifferently we classified all species in our databaseas those that have been red-listed at least once overtime (in the following denoted as red-listed, in total59 species) and those that have not. We includedall the Swedish Red Lists or similar classifications ofthreatened species, both the newer ones applying theIUCN (IUCN 2001) quantitative criteria [22, 23] andthe older qualitative systems [1, 15, 17].

To adjust for the change of overall collection effortover time, we used the yearly sampling intensity asreflected by records in the database as an estimateof sampling effort. Thus, for each species a yearlyemphPopulation Size Index (PSI) was calculated as:

Population size index =Number of species records

Sampling effort

As an estimate of species’ specific Sampling effort, weused the total number of yearly records of all non-red-listed species from the Swedish provinces (’Land-skap’) where the focal species was found during thestudy period. Thus, rather than just an estimate ofcollecting activities at the national level, the relevantsampling effort is restricted to areas where the specieshas been found. Only non-red-listed species wereused for the estimation of sampling effort, since thesampling intensity of red-listed species has changedover time (see below). Our method yields a yearlyfrequency of each species, relative to the overall sam-pling effort, and this quantity is used as a proxyfor species abundance. The approach is similar toHedenas et al.[27] and Ponder et al.’s [48] use of back-ground groups. As records of the same species fromthe same local area and period of time may be seenas interdependent to a certain extent, we combinedmerged any records from the same locality (the sec-ond lowest site classification used in the database –used with town or general area), month and year be-fore calculating the indices.

Since the sampling of longhorn beetles before 1918was of a lower intensity than later years and there-fore probably more uncertain (Fig. 1a), all analy-ses only used data from 1918 and forward. Prelimi-nary analyses showed a general increase in number ofrecords of red-listed species over time (Fig.1d). Thispattern probably reflects an increasing focus on rarespecies rather than real increases in abundance. Theincreased interest in red-listed species can be seen

as a gradual shift in the attitudes and priorities ofcollectors, demonstrated in the functional relation-ship between number of records of red-listed speciesand time. Therefore, we used the average trend inthe yearly frequency of red-listed species (Fig.1d)as the background against which red-listed specieswere compared. To characterize the average trend ofred-listed species we fitted several generic functionsthat could be expected to capture the overall pattern(Sigmoid, Gompertz, Holling disc, linear polynomial,quadratic polynomial). As model selection criteria,we used AICc [9]. The Gompertz function was usedfor later analysis, since it provided the best fit to thedata (AICc= -63.5; next best model AICc= -62.3),with parameters α = 0.134, β=0.064, γ=48.3:

y(t) = αe−e−β(t−γ)

For red-listed species, the predicted yearly valuesfrom the selected function were used to adjust ourestimate of effort. Consequently, the PSI of red-listed species was calculated in relation to the averagetrend of all red-listed species, not in relation to otherspecies. Since a number of red-listed species were notfound in Sweden until late in the 20th century, theaverage trend for red-listed species was modelled onlyon species that occurred in the database before 1930.

An observed change in species’ range can be due toan actual change in species range, a change in spatialsampling effort or both. We wanted to determine if achange in PSI was related to a change in range, there-fore the direct measure of species’ range, as grid celloccurrences, did not suffice as it can be influencedby spatial sampling effort. To separate changes inrange and sampling we calculated a yearly relativespecies’ range, using a centred moving window. Thetotal occupancy of grid cells using all records in thewindow, i.e. including all species, was calculated asa measure of spatial sampling effort. As above, onlyrecords from provinces where the focal species hasbeen found was used. The relative species range wasthen estimated as the number of grid cells occupiedby the focal species divided by the total number ofoccupied grid cells with records of longhorn beetles,i.e. the spatial sampling effort. A similar solutionwas put forward by Ponder et al.[48] in their use ofbackground data as pseudo-absences, and evaluatedby Joseph and Possingham[31]. We used a movingwindow centred on the year of focus to smooth thedistributional changes over time. The moving win-dow was 11 years wide and the grid size used to cal-culate occupancy was 100 × 100km2. To quantifythe overall change in relative species range betweentime periods we calculated the proportional changeby pooling all records from each time period (for timeperiods see Section 2.2). Proportional changes of gridoccurrences at a relatively large scale has been shownto work well as a proxy for detecting changes in rangeat smaller scales, and does not require scale correc-tion [31]. As a higher number of collectors can re-sult in a larger spatial coverage of species sampling –

4


more collectors may cover wider areas on a local leveland/or visit a wider range of habitats – we calculatedthe yearly number of collectors represented in thedatabase from the area where the species was found,and used this as a covariate in the analysis. Bothproportional changes of grid occurrences and num-ber of collectors were standardised to zero mean andunit variance before the analysis. The total numberof records of each species in the database, after themodifications described above, was used as a proxyfor the species’ overall rarity. The degree of speciesspecialization was determined as the number of larvalhost plant species used in Sweden [16].

2.2 Statistical analysis

A comparison of species frequency between time peri-ods was performed using a generalized linear model.We compared three 20 year time periods, coded asa class variable, namely 1921–1940 (p1), 1951–1970(p2) and 1981–2000 (p3). We chose these specific pe-riods to obtain estimates of long and shorter termchanges between evenly spaced points in time. Thenatural logarithm of sampling effort was used asmodel offset. The data show overdispersion, so weused the negative binomial function as distributionfunction, with a log link. The analysis was performedusing Proc GENMOD [50]. The relationship betweenestimated changes in the PSI or relative species rangeand species traits were analysed with general linearmodels using Proc GLM [50]. To test if the effect onrange differs between rare and common species andincreasing or decreasing species, we fitted a modelwith increasing/decreasing as a class variable, andtested the interaction effect between this class vari-able and rarity (measured as the logarithm of num-ber of records). This analysis is focused on the recentchanges in range and abundance, i.e. between p2 andp3.

In additions to the overall changes in the PSI andrange between time periods, and how these relateto each other, we explored the continuous effects ofthe predictive variables on the PSI. To that end, weperformed a generalized linear mixed model analy-sis. The model included a 2nd degree polynomial oftime, relative species range, and the number of col-lectors. The aim was to partition the change in thePSI between contributions from range and the overalltime trend, while controlling for number of collectors.Since we used an 11-year moving window to estimaterange, the years used in the analysis were 1923–1995.As in the previous model, we used the negative bino-mial function as distribution function, and the nat-ural logarithm of sampling effort was used as modeloffset. The full model included:

Population size index ∼ time, time2,

relative species range, collectors

Because population sizes of adjacent years can be cor-

related, we modelled the correlation between yearsusing a negative exponential relationship. We alsoevaluated other correlation structures, such as apower relationship, but this did not change the re-sults in any significant way. The analysis was per-formed using Proc GLIMMIX [51]. The full modelwas reduced using backward elimination. We re-moved the factor with the largest p-value first, butremoved time2 before time if both factors were non-significant. We did not remove time if time2 wassignificant, since these terms together merely serveto produce a description of temporal trend [55]. Theelimination stopped when all factors had a p-valuesmaller than 0.1. To improve the variable estimatesin the final model we removed outliers with a stu-dentized residual larger than 3. The reason thatwe did not remove outliers earlier in the modellingprocess was to follow a conservative approach, andthis did usually not affect the structure of the fi-nal model, only the variable estimates from the fi-nal model. We did not use information criteria, suchas AICc, for model selection, because the pseudo-likelihoods that are used for model estimation in theGLIMMIX procedure cannot be used for model com-parisons [51]. Because of problems with model con-vergence for species with few records and considera-tions of sample size, the analysis was performed onlyfor species with more than 100 records in the 20thcentury. An effect of this selection criteria was thatonly 14 out of the 50 currently red-listed longhornspecies [23] were analysed. We compared the fre-quency of predictor variables being significant be-tween red-listed and non-red-listed species using chisquare tests.

Collinearity between predictor variables can influ-ence the model reduction process, and a model ofsignificant factors does not necessarily equate to themodel with most explanatory power [38]. To comple-ment the significance tests from the model reductionexercise, and to evaluate how much of the explainedvariation that can be independently attributed toeach predictive variable, we performed a hierarchi-cal partitioning analysis [10, 38]. Hierarchical parti-tioning evaluates all 2k sub-models, based on the kpredictor variables, and calculates the independentproportional influence of each variable based on allmodel combinations where the variable is present.The comparison between sub-models is based on agoodness of fit criterion. In our analysis, we used themodel deviance to estimate goodness of fit and mod-elled the response variable as a Poisson distributionwith the natural logarithm of collection effort usedas model offset. The analysis was performed in R[49] with a modified version of the hier.part package[61], to accommodate for using a model offset and thedeviance as goodness of fit criterion. The indepen-dent effects (I%) for each variable show how much ofthe total variation that is independently explained bythis factor, and the change in deviance (∆Deviance)show the percentage difference in deviance betweenthe intercept model and the full model.

5


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6


3 Results

3.1 Records and sampling effort overtime

The overall number of records, and hence the gen-eral sampling effort, increased during the 20th cen-tury (Fig. 1a). The inflection point of the Gompertzfunction was estimated to 1948, which indicate thatthe increased collection of red-listed species startedwell before the Red List was introduced. At aboutthe same time, the number of records, the number ofcollectors and the total number of species appear tohave roughly reached asymptotic levels (Fig. 1a–c).The number of species of non red-listed species, how-ever, had already approached the asymptotic levelaround 1920 (Fig 1b).

3.2 Temporal trends in populationsize

When using time period as a class variable 55% (42)of the species showed significant differences in Pop-ulation Size Index (PSI) between time periods, and46% (35 species) showed significant differences be-tween the first (p1: 1921–1940) and the last (p3:1981–2000) time periods (Table 1). Out of the 35species, 40% showed a decrease and 60% an increasein the Population Size Index. The proportion of sig-nificant change from the first to the last time pe-riod among red-listed species was approximately thesame, 42%. The estimated changes are also quitelarge, with many species showing 25–60% declines be-tween the first and the last periods, while indices ofothers, often quite rare species, showed dramatic in-creases. Later changes (from p2: 1951–1970) were ofa similar magnitude. Figure 2 shows the histogramsof changes in PSI for time periods p2–p3 and p1–p3.The distributions are approximately normal for bothcontrasts, but with an excess of large positive changesbetween p1 and p3.

3.3 Range-abundance relationships

There was a weak overall positive relationship be-tween the change in relative species range and changein PSI between p2 and p3 (r2 = 0.19, p < 0.001).Change in relative species range for increasing specieswas negatively related to number of records (AN-COVA post-hoc test; t = 6.18, p < 0.001) whereasthere was no such significant relationship for decreas-ing species (t = 0.97, p = 0.34, Fig. 3); this in-teraction was significant (interaction log(number ofrecords)×category: F = 7.43, p < 0.01; the modelwas ∆range ∼ category, log(number of records),log(number of records)×category, i.e. a separate in-tercepts model). The results were qualitatively un-changed when the change in PSI (Table 2.2) was in-serted as a predictor with separate slopes betweenclasses, or if the change in range was calculated onraw grid numbers instead of relative species range.

3.4 Factors explaining temporalchanges in PSI

Time and range were the two variables that mostoften explained the variation in the PSI (Supple-mentary information). Across all species there was,however, a large statistical range as to what extentthe predictors explained variation in PSI (range of∆Deviance = 0–55%, Supplementary information)

For most species, a linear effect of time and rela-tive range seemed to predict variation in PSI betterthan the number of collectors (Fig. 4). The samepattern was present for both methods of analysis,i.e. GLMM significance tests and independent effectfrom hierarchical partitioning. The frequency of sig-nificant explanatory variables did not differ betweenred-listed and non-red-listed species for any variables(chi2-values: time = 0.07, time2 = 0.49, range =0.00, collectors = 0.27, p > 0.1 for all tests – lowsample sizes in some classes). The two methods gen-erally identified the same predictor variables as themost important ones, the species-wise concordancebetween GLMM significance and hierarchical parti-tioning was 81%. When calculating the concordancewe pooled the two time factors, so the three cate-gories were time, relative species range and collectors.

To assess if range is a more powerful predictor ofPSI for rare species than common ones, we plot-ted the model estimates of the range variable forspecies where it was significant against species fre-quency in the database (Fig. 5). This plot revealedthat range had a larger effect on PSI for rarer species.(n = 36, t = 3.54, p < 0.01, r2 = 0.27). The regres-sion of change in PSI on the number of larval hostspecies was not significant for either of the time pe-riods, with the effect close to zero (p > 0.2 for bothtime periods).

3.5 Threatened species

The PSI could be used to classify species according toIUCN red-list criterion A2 (IUCN 2001). When usingthe most recent change, period 2–3, only one species,Chlorophorus herbstii, showed an interpolated popu-lation decline that meets the threshold for Vulnerable(VU) A2 (=30 % decline over 10 years). Accordingto the guidelines for applying the IUCN Criteria forthe Swedish Red List evaluation, a 15 % decline over10 years (or three generations) can be used as a lowerthreshold for classifying species as Near Threatened(NT) according to criterion A [23]. Additional speciesthat declined in PSI to meet this threshold includedCallidium violaceum, Exocentrus lusitanus, Hylotru-pes bajulus, Mesosa nebulosa, Oplosia cinerea, andRhagium sycophanta. Four of these species are notcurrently red listed, and the other three are red listedbased on other criteria than population trends. If theresults from this study are viewed as underestima-tions of the real trend, a smaller significant changesuch as 5 % decline over 10 years may be deemednoteworthy. Additional species that meet that level

7


a) b)

0

100

200

300

400

500

600

700

800

900

1000

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Year

Nu

mb

er

of

co

lle

cti

on

s

0

10

20

30

40

50

60

70

80

90

100

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Year

Nu

mb

er

of

sp

ec

ies

c)

0

20

40

60

80

100

120

140

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Year

nu

mb

er

of

co

llecto

rs

d)

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0,45

0,5

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Year

Fre

qu

en

cy

Figure 1: Descriptive statistics of the database. a) Total number of records per year. b) Total number of species per year. c)Total number of collectors per year. d) Relative frequency of red-listed species out of total records. The hatched line correspondsto the fitted Gompertz function, used to adjust the sampling effort for red listed species. For figure 1a and 1b, the light grey barsrepresent non red-listed species and the dark grey bars represent red-listed species.

0

5

10

15

20

25

-50% -25% 0% 25% 50% 75% 100% 125% > 125%

No. sp

ecie

s

Change PSI (%)

Figure 2: Histogram of changes in Population size index be-tween time periods p2 and p3 (grey bars) and p1 and p3 (openbars).

Figure 3: The relationship between the change in relativespecies range and log(number of records) for increasing anddecreasing species. Open squares and dashed line is for in-creasing species, and closed squares and solid line is for de-creasing species. Higher values of log(no. records) representsmore common species.

8


0,15

0,2

0,25

0,3

0,35

0,4

0,45

requ

en

cy / a

ve

rage

I%

time

time2

range

collectors

0

0,05

0,1

0,15

GLMM Hier.part

fre

qu

en

Figure 4: The frequency of each explanatory variable beingsignificant in the generalized mixed linear model, n = 73 (leftseries), and the average individual effect in percent from hi-erarchical partitioning (right series). Note that the left seriesdoes not sum to 1, since each species does not necessarily haveonly one significant explanatory variable.

Figure 5: Estimates of the range variable coefficient againstthe species frequency in the database (n = 36, t = 3.54, p <0.01, r2 = 0.27). Open symbols correspond to non red-listedspecies and closed symbols to species that are or have been redlisted.

of decline were Acanthocinus aedilis, Stictolepturamaculicornis, Stictoleptura rubra, Pogonocherus fas-ciculatus, Pogonocherus hispidus, Prionus coriariusand Tetrops praeusta.

4 Discussion

More than half of the Swedish longhorn species ap-peared to have changed in population size during thestudy time, as indicated by significant differences inthe Population Size Indexes between time periods.Changes occurred both over the longer time perspec-tive (1921–1940 vs. 1981–2000) and over the shortertime period comparisons (1921–1940 vs. 1951–1970and 1951–1970 vs 1981–2000). The temporal pat-terns were similar in red-listed and non red-listedspecies, indicating that both small and large popula-tions were subject to decreases or increases. However,the population dynamic mechanisms seemed to de-pend on population size, as the Population Size Index(PSI) was more closely related to range among rare

species than in common ones (Fig. 5). The mecha-nisms also differed between increasing and decreasingspecies – with the ranges of decreasing species beinggenerally unchanged (at the scale of the study) re-gardless of commonness, i.e. a population thinning.In contrast, increasing species showed correspondingrange expansions for rare but not common species.Since most species show a change in relative speciesrange in the spectrum from neutral to expanding,this might indicate rather stable population ranges,or a higher spatial effort in sampling that was notaccounted for in our analysis. The species-specific re-sults generally agree well between the model reduc-tion analysis and the hierarchical partitioning, andfor the majority of species time and/or relative rangewere the most important variables, with the numberof collectors generally not being as important.

Our expectation was that declining rarer specieswould exhibit larger range contractions than declin-ing common species, but the results did not supportthis. One explanation for this is that population den-sity, but not range, has decreased, perhaps because ofdeteriorating habitat quality or poorer climatic con-ditions. However, even if a declining species is stillpresent in all of its sub-populations, the risk of lo-cal (and ultimately total) extinction risk may be el-evated due to lower population density [25]. If thereis no clear meta-population structure, we generallyexpect a more gradual increase in extinction risk,other things equal. Obviously, to evaluate the fu-ture prospects of declining longhorn beetles it may becritical to know their population structure, especiallyamong rarer species that can be suspected to be gov-erned by meta-population dynamics. Interestingly,information in the older entomological literature in-dicates that the perceived range and commonnessof many longhorn beetles in Sweden have been re-markably constant over even longer time frames [35],which suggests that even relatively rare species havebeen able to survive in spite of what appears to befew, fragmented or small populations. An alternativeexplanation for our results is that range, and possi-bly density, has decreased for declining species, butat a smaller spatial scale than was used here. If so,the range contraction is scattered, i.e. the specieshas disappeared from local sites distributed over theentire range of the species. This means that localsub-populations have gone extinct, which could bedue to processes linked to small population size or todeteriorating environmental conditions. This type ofrange contraction would correspond to a decrease insmaller scale area of occupancy (AOO), but not inextent of occurrence (EOO) [26, 30]. However, thecurrent analysis assumes that regional rarity, as to-tal number of records, reflects local rarity, and thisis not necessarily the case. A general positive corre-lation between global and local abundance has of-ten been observed [6, 7], but this might not holdfor Swedish longhorn beetles, and empirical data oflocal commonness is needed to substantiate this as-sumption. Looking at our data, comparing common-

9


ness at global and local scales, we find examples ofglobally common species being locally common (e.g.Anoplodera sanguinolenta), but also species that arerelatively rare on the global scale being locally com-mon (e.g. Pyrrhidium sanguineum) (Jeppsson et al.,unpublished). This is, however, still a measure oflocal commonness at a relatively coarse scale andpooled over time, and not a substitute for estimatesof commonness from a standardized inventory.

For increasing species, rare species showed largerrange expansions relative to common species; thussupporting the prediction that rarer species expandtheir range more than increasing common speciesdo. This suggests that common species occupy mostavailable suitable habitat and that increases in pop-ulation size are caused by higher population den-sity. Rarer species, on the other hand, seemed tooccupy suitable existing but vacant areas when pop-ulations increase [19, 28]. An alternative explanationfor larger range expansions in rarer species is thatrecent changes in land use or conservation measureshave created more suitable habitat, thereby facilitat-ing the range expansion. While possible, this expla-nation fails to account for why additional suitablehabitat has not also been created for the commonspecies to the same extent.

We did not observe larger population declines inspecies with a larger degree of host specialization,where a negative effect has often been hypothesised[18] or observed (i.e. [11, 40]. However, the num-ber of host species only deals with a small amount ofthe possible dimensions of specialization. Some larvalhost trees are also, ignoring other aspects of substratequality, extremely common, which can make numberof host tree species an unreliable predictor of speciessensitivity. For instance, some species in this datasetfeed exclusively on the two coniferous species ScotsPine (Pinus sylvestris) and Norwegian spruce (Piceaabies) that make up the boreal forests in Sweden, andaspects of substrate coarseness, sun-exposure, mois-ture and stages of decomposition are most certainlymore important for the sensitivity of these speciesthan the number of host tree species that they utilize[16].

4.1 Data evaluation

All trends presented here are conditional on the esti-mate of sampling effort. Therefore, a main assump-tion is that the overall number of longhorn beetles,i.e. the sum of the population sizes of all species, hasnot changed in any direction during our study period.Unfortunately there is no independent measure of theoverall trend of longhorn beetles in Sweden, althoughsuch information would be the ideal backdrop forevaluating our species-specific trends. However, withthe assumption that the overall number of records is afair description of the abundance of longhorn beetles,there is no evidence for an overall trend as seen fromthe asymptote in record numbers in the later partof the 20th century (Fig 1a). Three alternatives can

produce this asymptotic curve; a) constant number oflonghorn and constant effort, b) decreasing longhornsand increasing effort, or c) increasing longhorns anddecreasing effort. We view alternative c) as highlyunlikely, as the numbers of collectors and species col-lected per year has remained more or less the samefor decades (Fig 1), and since the biodiversity issuegained influence in the later part of the 20th century,leading to an increased interest in rare species. Itis harder to discriminate between alternative a) andb). This means that the overall trend of longhornbeetles is in the spectrum of neutral to decreasing,which corresponds to our species trends being fairlyunbiased to biased upwards, so estimates of nega-tive population trends may under-estimate the ratesof decline whereas estimates of population increasemay be larger than the actual trends. It should alsobe remembered that the same argument applies tored-listed species, and the analysis assumes that red-listed species do not change in abundance as a group.If we believe that red-listed species are generally de-clining, then the trends estimated here are underes-timations. Furthermore, the estimates of change areconservative in the sense that the number of individu-als observed at each collection event is not included inthe database, but only represented by a single record.Hence, the database does not differentiate between acollection event where a large number of individualswere observed from one where only a single specimenwas found, underestimating the magnitude of change.Since the sampling intensity has increased over time,both for red-listed and non-red-listed species, recentestimates of abundance or range should be more cer-tain than older ones. This would result in declines be-ing underestimated and increases overestimated, es-pecially for range, a conclusion shared by Olden andPoff[44]. It can be argued that the effect of red-listingshould only show after the publication of the first red-list in 1986, but the first red-list only confirmed whathad already been expressed before, and the identifica-tion and focus on later-to-be red-listed species startedearlier, as can be seen in the Swedish entomologicaljournal ’Entomologisk tidskrift’. Therefore we choseto model the sampling effort for red-listed species forthe entire time period studied, something that is alsocorroborated by the inflection point of the fitted func-tion.

Theoretically, our measure of effort could be heav-ily influenced by a couple of species showing strongtrends. This would however result in a bimodaldistribution of PSI changes, which we did not ob-serve (Fig. 2). The observed excess of large positivechanges, especially between the first and the last 20-year-periods, stem from uncommon species that onlyhave a marginal effect on the estimate of samplingeffort. Another basic assumption is obviously thatchanges in the records-based species’ frequency re-flect actual population changes, but since there are noother trend estimates for Swedish longhorn beetles,this cannot be verified. However, the positive corre-lation between the number of records for each species

10


with the red list categorization or with the species’appraisal done by Swedish entomologists (Jeppssonet al. unpublished) suggests that the commonness ofspecies in the database reflect the commonness in thewild.

Clearly, improved methods of collecting specimensand recording species presence, e.g. flight-trapping,hatching of beetles from substrates taken indoors, useof UV lamps for attracting night-active beetles andidentification of exit holes, galleries and other tracesof beetle activity may significantly increase the prob-ability that certain species are recorded. In this studywe controlled for such effects by using only recordsthat refer to handpicked specimens. It is, however,important to remember that for species with a highproportion of records from other collection methods,such as Nothorhina muricata, that these records arenot taken into account in this study. Similar changesin relative species frequencies in NHCs without cor-responding changes in nature may be the result ofthat certain species are perceived as becoming more– or less – valuable. To evaluate such effects weasked collectors how they value species and com-pared their ranking of species with a grading sys-tem for trading beetles between collectors [2]. Theresults indicate that declining species have becomemore, and increasing species less, interesting to col-lect [35]. For instance, Agapanthia villosoviridescens,Grammoptera ruficornis and Leiopus nebulosus (allincreasing) are the three species showing the largestdeclines in collector appreciation, while Hylotrupesbajulus and Rhagium sycophanta (both declining) arethe two species with the largest increases in appreci-ation [35]. This suggests that our indices, both pos-itive and negative, generally under-estimate the realpopulation trends. To summarize the discussion ofpossible biases; even though independent estimates ofspecies trends would be desirable to evaluate our re-sults against, after reviewing the evidence, we arguethat our estimates of PSI are not flawed. We haveconstructed a species-adjusted measure of samplingeffort, which takes red-list status and distributioninto account. Naturally, an explicit species-specificestimate of sampling effort would be preferable, butsuch data are just not available.

Another factor that may influence the collectingprobabilities is new knowledge related to specieshabitats and biology, and this is not taken into ac-count in this study. However, such aspects are mostlyrelevant for extremely rare species that we did notanalyse anyway due to the scarcity of records. Fi-nally, the fact that the measure of relative speciesrange is not independent from the number of recordsmay interfere with the analysis for some species.However, since we do not have an independent mea-sure of species range, there is no way around thisproblem. Rather, it serves to illustrate that funda-mentally, the attributes of population size and pop-ulation range cannot be entirely separated, since alarger range will also equate to a larger overall popu-lation, holding other factors such as density constant.

4.2 Practical applications

The present results can help to guide and comple-ment the red-listing process, in broadening the datasource to be used for the red list evaluation pro-cess. As the discussion of biases have shown, thelevels of decline are not likely to be exaggerated, butrather the opposite, so from a conservation perspec-tive estimates of consistent negative trends should betaken seriously. Currently, with the general deficit ofpopulation trend data, all of the red-listed Swedishlonghorn species are listed under red-list criteria Bor C [23], which mainly evaluate small geographicalrange or small population size, criteria that are basedon meta-population ecology and conservation genet-ics [39]. We believe that studies like this one can sup-plement the evaluation of specie’ conservation status,as well as making it possible to evaluate the speciesagainst particularly the red list criterion A, i.e. de-cline in population size, to a larger extent. When us-ing PSI to evaluate the species analysed here, sevenspecies meet the criteria for VU or NT, and anotherseven show declines of more than 5 % over 10 years.Only 4 out of these 14 species are currently red-listed(Chlorophorus herbstii, Mesosa nebulosa, Prionus co-riarius, Rhagium sycophanta), and one has been red-listed earlier (Oplosia cinerea). It should be empha-sised that many red-listed species are not analysedin this study since they occur at very low frequen-cies in the database, and that our study should notbe used to evaluate the precision of the Red List.Therefore, we believe that this type of analysis ofNHC data is most useful for identifying declines inless rare species.

Another finding with practical implications forevaluating species’ status was that relative ranges ofdeclining species, irrespective of perceived populationsize, did not decrease. Thus, if the status of thesepopulations were to be evaluated only from knowl-edge of changes in their distribution, the declineswould have been overlooked, at least at the scale ofstudy used here. In fact, since collectors have becomemore mobile in recent times, leading to more inten-sive spatial sampling, this may even produce the falseimpression that decreasing species are increasing inarea of distribution. Consequently, information ondistribution changes must be used with great cau-tion as a tool for identifying increasing or decliningspecies.

Acknowledgements

We thank all Swedish beetle collectors, living anddead, that have made the analysis of this databasepossible. We also thank everybody that has helpedwith the compilation of the database over the years.We are grateful for helpful comments from TomasPart, Matt Low and four anonymous reviewers onearlier drafts of this article.

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5 References

[1] Andersson, H., Coulianos, C.-C., Ehnstrom,B., Hammarstedt, O., Imby, L., Janzon, L.-.,Lindelow, ., and Walden, H. Hotade evertebrateri sverige. Entomologisk tidskrift 108, 3 (1987), 65–75.

[2] Anon. Enumeratio insectorum Fenniae et Sue-cicae, II. Coleoptera. Helsingfors EntomologiskaBytesforening, Helsingfors, Finland, 1947.

[3] Axelsson, A. L., Ostlund, L., and Hellberg,E. Changes in mixed deciduous forests of boreal swe-den 1866-1999 based on interpretation of historicalrecords. Landscape Ecology 17, 5 (2002), 403–418.

[4] Balvanera, P., Pfisterer, A. B., Buchmann,N., He, J.-S., Nakashizuka, T., Raffaelli, D.,and Schmid, B. Quantifying the evidence for biodi-versity effects on ecosystem functioning and services.Ecology Letters 9, 10 (2006), 1146–1156.

[5] Bierregaard, Richard O., J., Lovejoy, T. E.,Kapos, V., Santos, A. A. d., and Hutchings,R. W. The biological dynamics of tropical rainforestfragments. Bioscience 42, 11 (1992), 859–866.

[6] Brown, J. H. On the relationship between abun-dance and distribution of species. American Natu-ralist 124, 2 (1984), 255–279.

[7] Brown, J. H., Mehlman, D. W., and Stevens,G. C. Spatial variation in abundance. Ecology 76, 7(1995), 2028–2043.

[8] Burgman, M. A., Grimson, R. C., and Ferson,S. Inferring threat from scientific collections. Con-servation Biology 9, 4 (1995), 923–928.

[9] Burnham, K. P., and White, G. C. Evaluation ofsome random effects methodology applicable to birdringing data. Journal of Applied Statistics 29, 1-4(2002), 245–264.

[10] Chevan, A., and Sutherland, M. Hierarchicalpartitioning. The American Statistician 45, 2 (1991),90–96.

[11] Davies, K. F., Margules, C. R., and Lawrence,J. F. A synergistic effect puts rare, specializedspecies at greater risk of extinction. Ecology 85, 1(2004), 265–271.

[12] Debinski, D. M., and Holt, R. D. A surveyand overview of habitat fragmentation experiments.Conservation Biology 14, 2 (2000), 342–355.

[13] Delisle, F., Lavoie, C., Jean, M., andLachance, D. Reconstructing the spread of invasiveplants: taking into account biases associated withherbarium specimens. Journal of Biogeography 30, 7(2003), 1033–1042.

[14] Desender, K., and Turin, H. Loss of habitats andchanges in the composition of the ground and tigerbeetle fauna in four west european countries since1950 (coleoptera: Carabidae, cicindelidae). Biologi-cal Conservation 48, 4 (1989), 277–294.

[15] Ehnstrom, B., Gardenfors, U., and Lindelow,. Rodlistade evertebrater i Sverige 1993. Databankenfor hotade arter, SLU, Uppsala, Sweden, 1993. kopiai cerambparm.

[16] Ehnstrom, B., and Holmer, M. National-nyckeln till Sveriges flora och fauna. Skalbaggar:Langhorningar. Coleoptera: Cerambycidae. Artdata-banken, SLU, Uppsala, Sweden, 2007.

[17] Ehnstrom, B., and Walden, H. Faunavard iSkogsbruket del 2 – den lagre faunan. Skogsstyrelsen,Jonkoping, Sweden, 1986. kopia i cerambparm.

[18] Ewers, R. M., and Didham, R. K. Confoundingfactors in the detection of species responses to habi-tat fragmentation. Biological Reviews 81, 1 (2006),117–142.

[19] Freckleton, R. P., Gill, J. A., Noble, D., andWatkinson, A. R. Large-scale population dynam-ics, abundance-occupancy relationships and the scal-ing from local to regional population size. Journalof Animal Ecology 74, 2 (2005), 353–364.

[20] Gaston, K. J. Rarity. Chapman & Hall, 1994. hardelkopia, 751.

[21] Graham, C. H., Ferrier, S., Huettman, F.,Moritz, C., and Peterson, A. T. New devel-opments in museum-based informatics and applica-tions in biodiversity analysis. Trends in Ecology &Evolution 19, 9 (2004), 497–503.

[22] Gardenfors, U. Rodlistade arter i Sverige 2000– The 2000 Red List of Swedish Species. ArtData-banken, SLU, Uppsala, Sweden, 2000.

[23] Gardenfors, U. Rodlistade arter i Sverige 2005- The 2005 Red List of Swedish Species. Artdata-banken, SLU, Uppsala, 2005.

[24] Grixti, J. C., Wong, L. T., Cameron, S. A.,and Favret, C. Decline of bumble bees (bombus)in the north american midwest. Biological Conser-vation 142, 1 (2009), 75–84.

[25] Hanski, I. A., and Gaggiotti, O. E. Ecology, Ge-netics And Evolution Of Metapopulations. ElsevierAcademic Press, San Diego, 2004.

[26] Hartley, S., and Kunin, W. E. Scale dependencyof rarity, extinction risk, and conservation priority.Conservation Biology 17, 6 (2003), 1559–1570.

[27] Hedenas, L., Bisang, I., Tehler, A., Hamnede,M., Jaederfelt, K., and Odelvik, G. Aherbarium-based method for estimates of temporalfrequency changes: mosses in sweden. BiologicalConservation 105, 3 (2002), 321–331.

[28] Holt, R. D., Lawton, J. H., Gaston, K. J.,and Blackburn, T. M. On the relationship be-tween range size and local abundance: Back to ba-sics. Oikos 78, 1 (1997), 183–190.

[29] Ihse, M. Swedish agricultural landscapes - patternsand changes during the last 50 years, studied byaerial photos. Landscape and Urban Planning 31,1-3 (1995), 21–37.

[30] IUCN. IUCN Red List Categories and Criteria :Version 3.1. IUCN Species Survival Commission,Gland, Switzerland and Cambridge, UK, 2001.

[31] Joseph, L. N., and Possingham, H. P. Grid-basedmonitoring methods for detecting population de-clines: Sensitivity to spatial scale and consequencesof scale correction. Biological Conservation 141, 7(2008), 1868–1875.

[32] Lane, M. A. Roles of natural history collec-tions. Annals of the Missouri Botanical Garden 83,4 (1996), 536–545.

[33] Lawton, J., and May, R. Extinction Rates. OxfordUniversity Press, 1995.

12


[34] Linder, P., and Ostlund, L. Structural changesin three mid-boreal swedish forest landscapes, 1885-1996. Biological Conservation 85, 1-2 (1998), 9–19.

[35] Lindhe, A., Jeppsson, T., and Ehnstrom, B.Longhorn beetles in Sweden - changes in distribu-tion and abundance over the last two hundred years.Entomologisk tidskrift 131, 4 (2010), 241–510.

[36] Loreau, M., Naeem, S., Inchausti, P., Bengts-son, J., Grime, J. P., Hector, A., Hooper,D. U., Huston, M. A., Raffaelli, D., Schmid,B., Tilman, D., and Wardle, D. A. Ecology- biodiversity and ecosystem functioning: Currentknowledge and future challenges. Science 294, 5543(2001), 804–808.

[37] Low, M., Meyer, L., and Southwell, C. Num-ber and distribution of adelie penguin (pygoscelisadeliae) breeding sites in the robinson group of is-lands, mac.robertson land coast, east antarctica. Po-lar Record 43, 226 (2007), 225–229.

[38] Mac Nally, R. Regression and model-building inconservation biology, biogeography and ecology: Thedistinction between – and reconciliation of – ‘pre-dictive’ and ‘explanatory’ models. Biodiversity andConservation 9, 5 (2000), 655–671.

[39] Mace, G. M., Collar, N. J., Gaston, K. J.,Hilton-Taylor, C., Akcakaya, H. R., Leader-Williams, N., Milner-Gulland, E. J., and Stu-art, S. N. Quantification of extinction risk: Iucn’ssystem for classifying threatened species. Conserva-tion Biology 22, 6 (2008), 1424–1442.

[40] Mattila, N., Kaitala, V., Komonen, A., Koti-aho, J. S., and Paivinen, J. Ecological determi-nants of distribution decline and risk of extinctionin moths. Conservation Biology 20, 4 (2006), 1161–1168.

[41] McCarthy, M. A. Identifying declining and threat-ened species with museum data. Biological Conser-vation 83, 1 (1998), 9–17.

[42] McCorquodale, D. B., Brown, J. M., and Mar-shall, S. A. A decline in the number of long-horned wood boring beetle (coleoptera: Ceramby-cidae) species in ontario during the 20th century?Journal of the Entomological Society of Ontario 138(2007), 107–135.

[43] McInerny, G. J., Roberts, D. L., Davy, A. J.,and Cribb, P. J. Significance of sighting rate in in-ferring extinction and threat. Conservation Biology20, 2 (2006), 562–567.

[44] Olden, J., and Poff, N. Long-term trends ofnative and non-native fish faunas in the americansouthwest. Animal biodiversity and conservation 28,1 (2005), 75–89.

[45] Parker, M., and Mac Nally, R. Habitat lossand the habitat fragmentation threshold: an exper-imental evaluation of impacts on richness and totalabundances using grassland invertebrates. BiologicalConservation 105, 2 (2002), 217–229.

[46] Petersen, F. T., and Meier, R. Testing species-richness estimation methods on single-sample collec-tion data using the danish diptera. Biodiversity andConservation 12, 4 (2003), 667–686.

[47] Pimm, S. L., Russell, G. J., Gittleman, J. L.,and Brooks, T. M. The future of biodiversity. Sci-ence 269, 5222 (1995), 347–350.

[48] Ponder, W. F., Carter, G. A., Flemons, P.,and Chapman, R. R. Evaluation of museum col-lection data for use in biodiversity assessment. Con-servation Biology 15, 3 (2001), 648–657.

[49] R Development Core Team. R: A language andenvironment for statistical computing, 2007.

[50] SAS Institute Inc. Sas 9.1.3, 2003.

[51] SAS Institute Inc. The glimmix procedure, june2006; retrieved 2009-07-07., 2006.

[52] Shaffer, H. B., Fisher, R. N., and Davidson, C.The role of natural history collections in document-ing species declines. Trends in Ecology & Evolution13, 1 (1998), 27–30.

[53] Shaw, D. J., Haydon, D. T., Cattadori, I. M.,Hudson, P. J., and Thirgood, S. J. The shape ofred grouse cycles. Journal of Animal Ecology 73, 4(2004), 767–776.

[54] Siitonen, J. Forest management, coarse woody de-bris and saproxylic organisms: Fennoscandian borealforests as an example. Ecological bulletins 49 (2001),11–41.

[55] Sokal, R., and Rohlf, F. Biometry. W.H. Free-man and Company, USA, 1981.

[56] Ostlund, L., Zackrisson, O., and Axelsson,A. L. The history and transformation of a scan-dinavian boreal forest landscape since the 19th cen-tury. Canadian Journal of Forest Research-RevueCanadienne De Recherche Forestiere 27, 8 (1997),1198–1206.

[57] Stockwell, D., and Peterson, A. T. Controllingbias in biodiversity data. Island Press, 2002, pp. 537–546.

[58] Vogeli, M., Serrano, D., Pacios, F., andTella, J. L. The relative importance of patch habi-tat quality and landscape attributes on a decliningsteppe-bird metapopulation. Biological Conserva-tion In Press, Corrected Proof (2010).

[59] Vie, J.-C., Hilton-Taylor, C., and Stuart, S. e.Wildlife in a Changing World – An Analysis of the2008 IUCN Red List of Threatened Species. IUCN,Gland, Switzerland, 2009.

[60] Vik, J. O., Brinch, C. N., Boutin, S., andStenseth, N. C. Interlinking hare and lynx dy-namics using a century’s worth of annual data. Pop-ulation Ecology 50, 3 (2008), 267–274.

[61] Walsh, C., and Mac Nally, R. Hierarchical par-titioning. r package ver.1.0-3, 2005.

[62] Webb, T. J., Noble, D., and Freckleton, R. P.Abundance-occupancy dynamics in a human dom-inated environment: linking interspecific and in-traspecific trends in british farmland and woodlandbirds. Journal of Animal Ecology 76, 1 (2007), 123–134.

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