Sam ; Roberts

Peter Kappeler

Arbts Grp

Verhaltenforschung

Keywords: Allen's rule, Bergmann's rule, Microcebus, cryptic species, sexual size dimorphism Primate biology [behavior, ecology, physiology, anatomy], Theory Allen's rule, Bergmann's rule, Microcebus, cryptic species, sexual size dimorphism 97

 Morphological variation is commonly used to infer environmental adaptations and phylogenetic 57 relationships in animal taxa, but multi-researcher datasets can hamper analyses due to inter-58 observer biases. 59  Our filtering pipeline markedly improved data quality for downstream analyses and highlighted 60 key steps in data quality assessment for generating reliable results. 61  Across species, mouse lemurs are sexually dimorphic (with larger females), they follow neither 62 Rensch's rule for sexual size dimorphism nor Bergmann's rule (larger mouse lemur taxa live in 63 warmer and wetter climates), but are concordant with Allen's rule with regard to having shorter 64 tails in colder environments.

Table 1: Quality filtering pipeline for the reduction of inter-observer bias in multi-observer datasets.

Step Aim Description Example 1 Define geographic and taxonomic units

Hierarchical levels in the dataset (e.g., species or population level) must be defined. Individuals should be grouped accordingly for the next steps.

The taxonomy of mouse lemurs is still a topic of ongoing debate (e.g., Poelstra et al., 2021;[START_REF] Tiley | Population genomic structure in Goodman's mouse lemur reveals long-standing 851 separation of Madagascar's Central Highlands and eastern rainforests[END_REF], and geographic variation in morphological traits for taxa with wide distributions are possible (e.g., [START_REF] Schüßler | 810 Ecology and morphology of mouse lemurs (Microcebus spp.) in a hotspot of microendemism in 811 northeastern Madagascar, with the description of a new species[END_REF]. We defined populations per taxon as our units of investigation. 2 Exclude certain individuals from the population It must be clarified whether there is strong sexual size dimorphism or intra-annual variability of certain variables (e.g., body mass, girth, fat deposits) in any of the study taxa. Can adults/juveniles or pregnant/non-pregnant females be distinguished from each other? Is there strong seasonal variation in the composition of the population? In these cases, the dataset may be split into male/female sub-categories, some individuals may be excluded completely (e.g., juveniles), or certain variables set to NA (e.g., body mass of pregnant females).

Mouse lemurs are fast-growing seasonal breeders with high population turnover rates and a relatively high mortality of young individuals [START_REF] Radespiel | Sociogenetic structure in a free-771 living nocturnal primate population: sex-specific differences in the grey mouse lemur (Microcebus 772 murinus)[END_REF][START_REF] Lutermann | The role of 709 survival for the evolution of female philopatry in a solitary forager, the grey mouse lemur (Microcebus murinus)[END_REF][START_REF] Kraus | The costs of risky male behaviour: sex differences in 690 seasonal survival in a small sexually monomorphic primate[END_REF][START_REF] Blanco | Unpredictable environments, opportunistic 593 responses: Reproduction and population turnover in two wild mouse lemur species (Microcebus rufus 594 and M. griseorufus) from eastern and western Madagascar[END_REF][START_REF] Evasoa | Variation in reproduction of the smallest-bodied primate radiation, the 629 mouse lemurs (Microcebus spp.): A synopsis[END_REF]. To remove the confounding effect of age, juveniles were excluded from the dataset. Furthermore, we excluded measurements of individuals from the nonbreeding season (as indicated by the lack of males with enlarged testes per species; [START_REF] Evasoa | Variation in reproduction of the smallest-bodied primate radiation, the 629 mouse lemurs (Microcebus spp.): A synopsis[END_REF] to control for the high proportion of not yet fully-grown individuals in the population before the reproductive season starts. For pregnant females (identified during capture or as numerical outliers, i.e., if heavier than heaviest male), body mass was set to NA.

Exclude outliers

Outliers are data points that are inconsistent with the majority of data and their statistical distribution [START_REF] Pearson | Outliers in process modeling and identification[END_REF][START_REF] Liu | On-line outlier detection and data cleaning[END_REF]. These may stem from methodological errors or may be actual biological variation. We argue that for the analysis of interspecific differences both kinds of outliers should be excluded. Biological outliers are of low value, because they do not contribute much to the statistical distribution of a certain measurement. Instead, their inclusion may impair

We set this breakpoint to the median ± 3*MAD [START_REF] Davies | The identification of multiple outliers[END_REF][START_REF] Pearson | Mining imperfect data: Dealing with contamination and incomplete records[END_REF], and identified outliers were replaced with NA.

the power of subsequent statistical procedures. Therefore, we propose to use the generic but powerful Hampel identifier [START_REF] Davies | The identification of multiple outliers[END_REF][START_REF] Pearson | Outliers in process modeling and identification[END_REF] to exclude outliers of any kind. It uses the median and the median absolute deviation (MAD; [START_REF] Hampel | The breakdown points of the mean combined with some rejection rules[END_REF] to define breakpoints below/above which measurements are identified as outliers [START_REF] Pearson | Outliers in process modeling and identification[END_REF]. 4

Exclude researchers that contribute too high variation

It should be assessed whether some researchers were less consistent than others in measuring. Such an observer effect introduces additional noise that may dilute subtle differences among the units of investigation, particularly in small-sized and cryptic species. We propose the use of the coefficient of variation (CV) as a measure of variability in a sample in relation to its mean (e.g., [START_REF] Pélabon | On the use of the coefficient of variation to 744 quantify and compare trait variation[END_REF]. It is often applied to quantify the plasticity in a phenotypic trait and to compare it across different traits [START_REF] Houle | Comparing evolvability and variability of quantitative traits[END_REF][START_REF] Pélabon | On the use of the coefficient of variation to 744 quantify and compare trait variation[END_REF]. To distinguish between actual phenotypic plasticity of species and high measurement variation of certain researchers, we assumed that (1) CV values per species-researcher combination follow a normal distribution, and (2) researchers with high CV values generate a long right tail in the distribution. In practice, the CVs for all researchers per unit of investigation were calculated separately for each variable.

We determined the upper limit of the Hampel identifier (see above) and used it as a truncation value, above which variables measured by the respective researchers were replaced with NA (Figure 2B). 99 Morphological variation is central to the quantification of within-and between-species diversity and thus 100 at the heart of evolutionary biology and ecology [START_REF] Darwin | On the Origin of Species[END_REF]Mayr, 1963;[START_REF] Wright | Evolution and the genetics of populations: Variability within and among natural 867 populations[END_REF]. In times of 101 increasing computational power that allows the analyses of large genomic datasets, combining 102 morphological data from multiple sources will become an increasingly important task, for example, when 103 aiming to combine multiple lines of evidence to derive conclusions in taxonomy (i.e., integrative 104 taxonomy; [START_REF] De Queiroz | Species concepts and species delimitation[END_REF][START_REF] Padial | The integrative future of taxonomy[END_REF]Leaché et al., 2019). In this and other contexts, 105 distinguishing between biologically meaningful signals and methodological biases arising from the 106 compilation of multi-observer datasets is of utmost importance [START_REF] Burghardt | Perspectives -Minimizing observer bias in behavioral studies: A review and 600 recommendations[END_REF][START_REF] Howard | Control of variability[END_REF]107 Morrison 2015).

108

In general, processing of large datasets should involve the following steps: 1) contextualization of the 109 dataset, 2) assessment, and 3) improvement of data quality (Van den Berghe 2017). In morphological 110 studies, in particular, data quality can be severely constrained and impaired by intra-and inter-observer 111 biases. These biases can principally arise from a differential use of measurement definitions, variable 112 care or stringency while handling an animal, and the use of different tools by different researchers 113 [START_REF] Howard | Control of variability[END_REF][START_REF] Roitberg | Inter-observer and intra-observer 797 differences in measuring body length: a test in the common lizard, Zootoca vivipara[END_REF]. A similar problem may arise if one researcher collects data over 114 longer time periods and slowly and unconsciously shifts the measurement technique by developing 115 routines [START_REF] Roitberg | Inter-observer and intra-observer 797 differences in measuring body length: a test in the common lizard, Zootoca vivipara[END_REF]. Particularly for small-bodied species, small measurement errors can 116 introduce large relative biases. A problematic case in this respect is the study of cryptic species that 117 display only subtle phenotypic variation by definition [START_REF] Bickford | 588 Cryptic species as a window on diversity and conservation[END_REF], which may become easily 118 masked when such biases are strong [START_REF] Howard | Control of variability[END_REF]. Therefore, the detection and reduction of intra-119 and inter-observer biases should be a priority for studies aiming to investigate species-or population-120 specific morphological variability.

121 Different approaches can be taken to ensure dataset reliability and to counteract confounding effects of 122 inter-or intra-observer biases. Although transparency about measurement routines for morphological 123 traits (e.g., information on how many researchers were involved, how training was conducted, how 124 precise measurements were conducted) would be useful, it is remarkably rare in the literature. Yet, 125 detailing which measurement definitions were used is rather common, either by citing other sources or 126 by directly defining morphological variables [START_REF] Chornelia | How to accurately delineate morphologically conserved taxa 609 and diagnose their phenotypic disparities: Species delimitation in cryptic Rhinolophidae (Chiroptera)[END_REF][START_REF] Michaloudi | 730 Reverse taxonomy applied to the Brachionus calycifiorus cryptic species complex: Morphometric analysis 731 conforms species delimitations revealed by molecular phylogenetic analysis and allows the 732 (re)description of four species[END_REF]Radespiel et 127 al., 2008Radespiel et 127 al., , 2012)). Using these definitions and measuring individuals by only one researcher is occasionally 128 stated to dispel doubts about the reliability of a dataset (e.g., Macdonald et al., 2018;Benites et al., 129 2020), even though intra-observer bias can still exist. If more than one researcher is taking 130 measurements, the researcher's experience is sometimes reported (e.g., Pilloud et al., 2019), while joint 131 researcher trainings are considered particularly useful by others (e.g., [START_REF] Morrison | Observer error in vegetation surveys: a review[END_REF]. Further 132 approaches include taking measurements of each individual by different researchers and calculating an 133 agreement or mean value for later analyses (e.g., [START_REF] Shirley | Rigorous approaches to species delimitation 828 have significant implications for African crocodilian systematics and conservation[END_REF]. Such measures are typically 134 applied in studies that require a high sampling effort and are designed prior to any data collection. In 135 reality, however, huge datasets are often compiled after data collection has taken place. In these cases, 136 a posteriori methods for reducing observer biases are needed but largely absent in the literature 137 [START_REF] Burghardt | Perspectives -Minimizing observer bias in behavioral studies: A review and 600 recommendations[END_REF]. A standardized framework of how to assess the quality of compiled 138 morphological data and how to improve data quality has not been defined so far.

139

After data compatibility and quality of a dataset have been secured, patterns of morphological variation 140 among taxa can be investigated. Variation itself can then be attributed to a variety of potentially interacting factors [START_REF] Hallgrímsson | Variation and variability: central concepts in biology[END_REF]. These factors could be phylogenetic signals with 142 relevance to species delimitation studies (e.g., [START_REF] Cadena | Issues and perspectives in species delimitation using 602 phenotypic data: Atlantean evolution in Darwin's finches[END_REF][START_REF] Chornelia | How to accurately delineate morphologically conserved taxa 609 and diagnose their phenotypic disparities: Species delimitation in cryptic Rhinolophidae (Chiroptera)[END_REF]Schüßler et al., 143 2020), large scale ecological adaptation to different environments or small scale niche differentiation 144 (e.g., [START_REF] Agostini | The befuddling nature of mouse lemur hands 563 and feet at Bezà Mahafaly, SW Madagascar[END_REF]Meachen et al., 2015;Santini et al., 2018). Variation can further be attributed 145 to mechanisms of sexual selection acting differently on males and females (e.g., [START_REF] Jennions | Sexually selected traits and adult survival: A meta-668 analysis[END_REF] or 146 natural selection for sex-dependent environmental niches [START_REF] Kamilar | Does body mass dimorphism increase male-female dietary niche 674 separation? a comparative study of primates[END_REF]Williamson et 147 al., 2021). An established principle in the context of sexual size dimorphism is Rensch's rule (Rensch, 148 1950), which predicts that at the genus level "the ratio of female to male size is larger the smaller the 149 body size of a species" [START_REF] Meiri | Rensch's rule -Definitions and statistics[END_REF]. Regardless of sex, ecogeographic relationships such as 150 those formulated in Allen's and Bergmann's rules [START_REF] Mayr | Systematics and the origin of species, from the viewpoint of a zoologist[END_REF], could also be the underlying forces of 151 morphological variation. According to both, thermoregulatory constraints in endothermic animals are a 152 dominant driver in size variation, either in body size or in the proportions of appendages. In colder 153 climates, body appendages like ears and a tail tend to be smaller (Allan's rule) and body size larger 154 (Bergmann's rule; [START_REF] Mayr | Systematics and the origin of species, from the viewpoint of a zoologist[END_REF] than in warmer climates. Deviation from these "rules" can in turn provide 155 insights into the evolutionary history of a lineage or relevant physiological processes (Maestri et al., 156 2016;[START_REF] Rodríguez | Bergmann's rule and the geography of 793 mammal body size in the Western Hemisphere[END_REF][START_REF] Webb | Only half right: Species with female-biased sexual size dimorphism 862 consistently break Rensch's rule[END_REF].

157

This study aims to (1) develop and apply an objective multi-stage quality filtering pipeline to eliminate 158 problematic data points and reduce the confounding effects of sampling, inter-observer bias and intra-159 observer variation, (2) evaluate the improvement of data quality across different filtering steps, and (3) 160 use the filtered dataset to exemplarily test hypotheses concerning morphological variation in a genus of 161 small, cryptic primates in Madagascar (i.e., Microcebus). Specifically, we test for (3a) the presence of 162 sexual size dimorphism and predictions of (3b) Rensch's rule, (3c) Allen's rule and (3d) Bergmann's rule.

Methods 164 165

Study system 166 Mouse lemurs (genus Microcebus) are among the most speciose genera of lemurs, containing >20 cryptic 167 species [START_REF] Zimmermann | Histograms for ear length and interorbital distance, respectively, with truncation threshold (median + 3 * median absolute deviation) introduced as[END_REF][START_REF] Hotaling | Species discovery and validation in a cryptic radiation 660 of endangered primates: coalescent-based species delimitation in Madagascar's mouse lemurs[END_REF][START_REF] Sgarlata | Genetic and morphological diversity of mouse lemurs (Microcebus spp.) in northern 823 Madagascar: The discovery of a putative new species?[END_REF]Schüßler et al., 168 2020). These small-bodied and omnivorous primates can be found in all major ecosystems across 169

Madagascar [START_REF] Mittermeier | Lemurs 735 of Madagascar[END_REF]. Although subtle, morphological variation has often been used to 170 support taxonomic decisions in mouse lemurs (e.g., [START_REF] Rasoloarison | Taxonomic revision of mouse lemurs 785 (Microcebus) in the western portions of Madagascar[END_REF][START_REF] Radespiel | First indications of a highland 765 specialist among mouse lemurs (Microcebus spp.) and evidence for a new mouse lemur species from 766 eastern Madagascar[END_REF]171 Schüßler et al., 2020), genus-wide tests to assess morphological variability among mouse lemurs have 172 not been conducted so far. Female-biased sexual dimorphism has been reported for Microcebus murinus 173 body mass and head length [START_REF] Jenkins | Sexual dimorphism and sex ratios in Madagascan prosimians[END_REF][START_REF] Kappeler | Patterns of sexual dimorphism in body weight among prosimian primates[END_REF][START_REF] Schmid | Fluctuating sexual dimorphism and differential hibernation by sex in 806 a primate, the gray mouse lemur (Microcebus murinus)[END_REF]. Body 174 mass variation, e.g., with heavier females, was also found in several other recent and extinct lemur 175 species [START_REF] Godfrey | Sexual dimorphism in large-bodied primates: the 639 case of the subfossil lemurs[END_REF][START_REF] Kappeler | The island of female power? Intersexual dominance 681 relationships in the lemurs of Madagascar[END_REF], and sex differences in canine length were found in 176 M. griseorufus [START_REF] Rasoazanabary | The human factor in mouse lemur (Microcebus griseorufus) conservation: 782 Local resource utilization and habitat disturbance at Beza Mahafaly, SW Madagascar[END_REF]. Mouse lemurs have been studied by various research groups over 177 the years. Due to small distributions (i.e., micro-endemism) scattered across all forest habitats of the 178 island, the high levels of endangerment of some species, the remoteness of their remaining forest 179 habitats, and the difficulties of capturing larger numbers of mouse lemurs in various habitat types, it is 180 not possible for one or a few researchers to generate a large morphological dataset encompassing all 181 taxa. Consequently, the assembly of multiple datasets generated by multiple research groups, as it was 182 done here, is the only way to study morphological variation in this genus. However, we believe that this 183 problem is a general one and applies to a large variety of cryptic species worldwide.

184 185

Dataset characteristics 186

We assembled a morphological dataset comprised of 3,073 individual mouse lemurs belonging to 25 187 different taxa [START_REF] Hotaling | Species discovery and validation in a cryptic radiation 660 of endangered primates: coalescent-based species delimitation in Madagascar's mouse lemurs[END_REF][START_REF] Schüßler | 810 Ecology and morphology of mouse lemurs (Microcebus spp.) in a hotspot of microendemism in 811 northeastern Madagascar, with the description of a new species[END_REF][START_REF] Sgarlata | Genetic and morphological diversity of mouse lemurs (Microcebus spp.) in northern 823 Madagascar: The discovery of a putative new species?[END_REF] that were captured and 188 measured by 48 researchers in 153 study sites across Madagascar (Figure 1). Representation of the 189 different taxa was highly variable with a maximum of 748 individuals for M. ravelobensis and only two or 190 four individuals for M. marohita and M. manitatra, respectively (Table S1). We collected measurements 191 for the 13 most widely used morphological variables (ear length, ear width, head length, head width, 192 inter-and intra-orbital distance, snout length, lower leg length, hind foot length, third toe length, body 193 length, tail length, body mass; [START_REF] Hafen | Acoustically dimorphic 647 advertisement calls separate morphologically and genetically homogenous populations of the grey 648 mouse lemur (Microcebus murinus)[END_REF][START_REF] Zimmermann | Sympatric mouse 877 lemurs in north-west Madagascar: A new rufous mouse lemur species (Microcebus ravelobensis)[END_REF] as defined in Table S2. 194 However, these were not available for all individuals. We carefully checked which definitions were 195 applied by different researchers and excluded all measures that did not conform to the most common 196 definitions (Table S2). [START_REF] Templeton | Population Genetics and Microevolutionary Theory[END_REF][START_REF] Cadena | Issues and perspectives in species delimitation using 602 phenotypic data: Atlantean evolution in Darwin's finches[END_REF] if the sample is drawn at random from a 219 randomly mating population and if the inheritance of a polygenic phenotypic trait is close to Hardy-220

Weinberg equilibrium [START_REF] Templeton | Population Genetics and Microevolutionary Theory[END_REF]. Furthermore, cryptic species are per definition characterized 221 by a similar and non-diverging morphology [START_REF] Bickford | 588 Cryptic species as a window on diversity and conservation[END_REF], consistent with the idea of a unimodal 222 normal distribution. This basic assumption for the distribution of measurements of a specific trait for a 223 certain taxon should hold irrespective of the researcher. Accordingly, biases originating from multiple 224 researchers should produce a deviation from a normal distribution for the trait under study. 225

Any assessment of data quality should be based on well-founded and comprehensible metrics (Heinrich 226 et al., 2018). 227

To test the effectiveness of the data quality filtering pipeline, we first quantified the deviation from a 228 normal distribution by subtracting the Shapiro-Francia test statistic W' from 1 (i.e., deviation from 229 normality = 1-W'; "nortest" package in R; [START_REF] Gross | nortest: Tests for Normality[END_REF] for each filtering step and variable 230 separately. This modification of the widely used Shapiro-Wilk test for assessment of data normality 231 represents the squared correlation between the ordered data values and the expected normal order 232 statistics (i.e., the theoretical quantiles in a qqplot; [START_REF] Shapiro | An approximate analysis of variance test for normality[END_REF][START_REF] Royston | Estimating departure from normality[END_REF]. In contrast 233 to the typical Shapiro-Wilk test statistic W [START_REF] Royston | Estimating departure from normality[END_REF], W' has been shown to powerfully capture the 234 deviation from a normal distribution [START_REF] Mbah | Shapiro-Francia test compared to other normality test using expected 723 p-value[END_REF], while making the test statistic directly 235 interpretable without relying on a P-value. Values of 1-W' range from 0 (perfectly normally distributed 236 data) to 1 (no normal distribution). 237

Second, we calculated the skewness, which measures asymmetry or horizontal deviation from a normal 238 distribution, and excess kurtosis of the distribution, which measures "tailedness" or vertical deviation 239 from normality [START_REF] Hopkins | Tests for normality and measures of skewness and kurtosis: Their 656 place in research reporting[END_REF] for each filtering step and variable separately. We used 240 absolute values for both measures to establish comparability across metrics, with 0 indicating perfect fit 241 to a normal distribution. For both measures, deviations of 0 -2 units are considered acceptable for a 242 normal distribution [START_REF] Trochim | The research methods knowledge base[END_REF][START_REF] Gravetter | Essentials of statistics for the behavioral sciences[END_REF]. 243

Finally, we assessed the inter-observer reproducibility of measures for each variable after the last quality 244 filtering step: we used the coefficients of variation (CV) calculated for each population-researcher 245 combination and compared them across the 13 different morphological variables. CV-values < 0.1 were 246 defined as very good quality and CV = 0.1-0.2 as good quality (Aronhime et al. 2013).

248

Testing hypotheses of morphological variation 249 Using the quality-filtered dataset, we tested for (1) the presence of sexual size dimorphism, as well as 250 predictions of (2) Rensch's, (3) Allen's and (4) Bergmann's rule in the genus Microcebus:

251

(1) Genus-wide sexual size dimorphism was tested in all 13 morphometric variables using linear mixed 252 effects models, including each variable individually as the response variable, sex as a fixed effect and 253 taxonomic affiliation (i.e., population of a species) as a random effect. Modelling was done using the 254 "lme4" and "lmerTest" packages in R, while the "performance" package was used to check the 255 assumptions of the models (Bates et al., 2015;[START_REF] Kuznetsova | lmerTest package: Tests in linear mixed 693 effects models[END_REF][START_REF] Lüdecke | performance: An R 706 Package for Assessment, Comparison and Testing of Statistical Models[END_REF]. Variables 256

were standardized prior to analysis to improve comparability.

257

(2) Support for Rensch's rule (i.e., female-biased sexual size dimorphism is larger the smaller the body 258 size) is usually inferred if the slope of an allometric line is significantly >1 (i.e., male size on the y-axis and 259 female size on the x-axis, using standardized major axis regression; [START_REF] Meiri | Rensch's rule -Definitions and statistics[END_REF]Webb & 260 Freckleton, 2007). A slope equal to 1 would instead indicate an isometric relationship in which sexual size 261 dimorphism does not change in magnitude when overall size of the taxon changes. This effect was tested 262 first by regressing, for each species, female and male mean body length and mean body mass separately. 263

Statistical deviation from regression slopes of 1 was then tested by a standardized major axis regression 264 method as implemented in the "smatr" R package [START_REF] Warton | smatr 3: R package for estimation and 859 inference about allometric lines[END_REF]. For comprehensive illustration 265 purposes, we further estimated a linear model for both variables (i.e, body length and body mass).

266

( (LMs) were then fitted using tail length and ear size separately as response variables and the log-scaled 277 bioclimatic variables as explanatory variables. To control for body size effects on appendage length, we 278 included head and body length as further explanatory variables [START_REF] Alhajeri | A global test of Allen's rule in rodents[END_REF]. We also computed 279 a null model excluding the bioclimatic variables. All possible variable combinations were calculated using 280 the "MuMIn" package in R [START_REF] Barton | MuMIn: Multi-Model Inference[END_REF], which ranks the best models based on their AIC (Akaike 281 Information Criterion). Only models with ∆AIC <2 and the null model are reported here. Akaike weights 282 [START_REF] Wagenmakers | AIC model selection using akaike weights[END_REF]) were further calculated using the "qpcR" R package (Spiess, 2018).

283

(4) Conformity with Bergmann's rule (i.e., body size is larger in colder climates) was tested by using body 284 mass and body length separately as response variables and the bioclimatic variables as explanatory 285 variables following the same modeling approach as described above for the test of Allen's rule (Alhajeri 286 et al., 2020;[START_REF] Alhajeri | Association between climate and body size in rodents: A 569 phylogenetic test of Bergmann's rule[END_REF].

Results

289

Assessment of data quality 290 After quality filtering, a total of 1,679 individuals (54.6%) remained in our dataset, while the number of 291 individuals represented in the filtered dataset further differed among morphological variables (Figure 292 3A). The sharpest decline in sample size was observed from step 1 to step 2, in which juvenile individuals 293 and those captured outside of the reproductive season were dropped from the dataset. The consecutive 294 steps then led to minor declines in sample size for certain variables, taxa and/or researchers. The highest 295 decline was found for ear and third toe length (reduction of 43.3% and 42.1%, respectively; Figure 3A). 296

Changes in the Shapiro 1-W' statistic, skewness and kurtosis across the five filtering steps were different 297 among variables and across filtering steps (Figure 3). However, after applying our filtering pipeline, the 298 distributions of most variables showed substantially improved quality, i.e., higher conformity to a normal 299 distribution (Figure 3). After quality filtering, mean inter-observer reproducibility was CV < 0.1 for most variables indicating very 304 good quality (Figure 4), and CV < 0.2 for the others (i.e., good quality): particularly reliable variables were 305 head length and width, interorbital distance, lower leg length, hind foot length and tail length. Others 306 like intraorbital distance, snout length and body mass were still associated with higher levels of variation 307 across researchers (Figure 4). Significant and consistent size differences between the two sexes were found for eight out of 13 315 variables, with females being the larger sex concerning head length and width, as well as snout length, 316 hind foot length, body length and tail length. Males exhibited larger ears (i.e., ear length and width). 317

Most pronounced was the difference in ear dimensions, head and body length (Figure 5; Table S3). 318

Significant differences were visible as a congruent direction in male vs. female means in 58.8-84.2% of 319 the studied taxa (Table S3). The test for Rensch's rule revealed that the regressions between female (x-axis) and male (y-axis) body 325 length and mass in log-scale had slopes of β = 0.969 (95% CI: 0.817-1.149) and β = 1.002 (95% CI: 0.865-326 1.160), respectively (Figure 6). In both cases, the relationship was not significantly different from 1 (body 327 length: test statistic r = -0.090, df = 18, P = 0.705; body mass: r = 0.006, df = 24, P = 0.978) and therefore 328 our data does not support Rensch's rule. Female and male body length and body mass (investigated 329 separately) were significantly and positively correlated with each other (body length: r² = 0.880, P < 330 0.001, n = 20; body mass: r² = 0.878, P < 0.001, n = 26, respectively), indicating that taxa with larger and 331 heavier males also had larger and heavier females (Figure 6). Conformity with Allen's rule was found for tail length but not for ear size. The best model for tail length 337 explained 68.3% of the variation by including different bioclimatic variables (Figure 7, Table S4). Tail 338 length increased with increasing minimum temperature of the coldest month and decreased with 339 increasing precipitation of the wettest and driest month (Figure 7A-C). The best model for ear size 340 explained 45.0% of the variation but did not include temperature-related variables. Instead, ear size 341 decreased with increasing precipitation of the driest month (Figure 7E). In both cases, null models 342 received much larger ∆AICc values and smaller Akaike weights than the preferred models (Table S4). 343

Positive relationships were found for tail length with head length and for ear size with body length 344 (Figure 7D, F; Table S4). 4). As morphological variation between cryptic species can be subtle (e.g., 388 [START_REF] Radespiel | First indications of a highland 765 specialist among mouse lemurs (Microcebus spp.) and evidence for a new mouse lemur species from 766 eastern Madagascar[END_REF][START_REF] Schüßler | 810 Ecology and morphology of mouse lemurs (Microcebus spp.) in a hotspot of microendemism in 811 northeastern Madagascar, with the description of a new species[END_REF][START_REF] Sgarlata | Genetic and morphological diversity of mouse lemurs (Microcebus spp.) in northern 823 Madagascar: The discovery of a putative new species?[END_REF], methodological biases can easily dilute 389 biological variation, especially in multivariate analyses.

391

Guidelines for future morphological studies 392

We have presented a pipeline to reduce inter-observer biases after data collection has been completed. 393

Besides illustrating the usefulness of the pipeline for the improvement of data quality, we make the 394 following suggestions for future data collection and a streamlined quality assessment based on our 395 findings:

396 1) Multi-observer studies should be designed in such a way that larger sample sizes (morphometric 397 measurements for > 30 individuals; Yusoff & Wah 2012) are collected by several researchers (n > 398

3) for each taxon in the dataset, so that inter-observer biases can be reliably identified and 399 reduced. 2) Several measures to improve inter-observer reproducibility should be implemented before data 401 collection (e.g., defining standard protocols, co-training of researchers) 402

3) We suggest calculating the inter-observer reproducibility before any further analyses, and to use 403 only those measures that are most reliably measured by the set of researchers involved. 404 4) Measurements should be used that don't rely on ambiguous start or endpoints for caliper 405 placement. Start and endpoints of measures must be clearly defined and identifiable across taxa. 406 5) Sampling protocols and measures undertaken for quality filtering must be transparently 407 reported. Ideally, data quality and inter-observer reproducibility for every variable is briefly 408 outlined to warrant reliability of the results in downstream analyses. differentiation and by differential growth patterns [START_REF] Cassini | Sexual size dimorphism and sexual selection in primates[END_REF][START_REF] Kappeler | Evolutionary dynamics of sexual size 684 dimorphism in non-volant mammals following their independent colonization of Madagascar[END_REF][START_REF] Plavcan | Sexual dimorphism in primate evolution[END_REF]415 Thomas et al., 2015). Strepsirrhine primates and particularly the lemurs of Madagascar have generally 416 been considered as sexually monomorphic compared to most other primate families (Jenkins & Albrecht, 417 1991;[START_REF] Kappeler | Patterns of sexual dimorphism in body weight among prosimian primates[END_REF][START_REF] Plavcan | Sexual dimorphism in primate evolution[END_REF]. Counterexamples were only rarely reported but included few 418 mouse lemur species like Microcebus murinus [START_REF] Kappeler | Patterns of sexual dimorphism in body weight among prosimian primates[END_REF][START_REF] Kappeler | The island of female power? Intersexual dominance 681 relationships in the lemurs of Madagascar[END_REF], warranting further 419 investigation. We found a consistent pattern of sexual size dimorphism across the whole genus that was 420 visible in the majority of the studied mouse lemur species, with females being significantly larger in six of 421 the 13 morphological variables investigated. In contrast, males had significantly larger ears than females, 422 and no sexual dimorphism was detected in the remaining five variables.

423

Female dominance is a trait found in a variety of species from all five extant lemur families (Evasoa et al., 424 2019;[START_REF] Kappeler | The island of female power? Intersexual dominance 681 relationships in the lemurs of Madagascar[END_REF]. However, variation among species exists, suggesting some level of species 425 divergence in this behavioral trait [START_REF] Evasoa | Sources of variation in social tolerance in mouse lemurs (Microcebus spp.)[END_REF]. Although a relationship between body mass 426 dimorphism and female dominance was not supported in a recent review study on 37 lemur species 427 (including nine mouse lemur species; [START_REF] Kappeler | The island of female power? Intersexual dominance 681 relationships in the lemurs of Madagascar[END_REF], subtle size differences between sexes may 428 have gone undetected due to the non-exclusive confounding effects of small sample size, large error-to-429 measurement ratio in small animals, inter-observer bias and seasonal variations in body condition 430 (making body mass a rather unreliable variable to assess sexual size dimorphism).

431

Sex differences in skull size may also be explained by sex differences in ecological niches. A larger head 432 size has been empirically associated with increased bite force in M. murinus [START_REF] Chazeau | Proximate determinants of bite force 607 capacity in the mouse lemur[END_REF]433 Thomas et al. 2015). Together with differences in canine length, as found in M. griseorufus 434 [START_REF] Rasoazanabary | The human factor in mouse lemur (Microcebus griseorufus) conservation: 782 Local resource utilization and habitat disturbance at Beza Mahafaly, SW Madagascar[END_REF], this could indicate subtle niche differentiation between male and female mouse 435 lemurs concerning feeding habits or preferred food items. Sex-dependent feeding and foraging 436 strategies have so far not been reported for mouse lemurs. However, female M. murinus in Kirindy (W 437 Madagascar) were observed in higher densities around gum trees than males, which was interpreted as a 438 consequence of female dominance [START_REF] Génin | Female dominance in competition for gum trees in the grey mouse lemur Microcebus 632 murinus[END_REF]. The solitary foraging mode of the omnivorous mouse 439 lemurs (e.g., [START_REF] Dammhahn | Small-scale coexistence of two mouse lemur species 615 (Microcebus berthae and M. murinus) within a homogeneous competitive environment[END_REF][START_REF] Radespiel | Feeding ecology of sympatric 768 mouse lemur species in northwestern Madagascar[END_REF]) certainly conveys considerable flexibility with respect to individualized or sex-dependent feeding habits. Detailed studies comparing the 441 feeding ecology of both sexes across mouse lemur species are needed to further test these hypotheses.

442 Interestingly, males had significantly larger ears than females, which may also have biological relevance, 443 since mouse lemurs rely extensively on acoustic communication in addition to olfaction (Glatston et al. 444 1993;[START_REF] Zimmermann | High frequency/ultrasonic communication in basal primates: The mouse and 874 dwarf lemurs of Madagascar[END_REF]. Sensory experiments with mouse lemurs in in Mandena, SE Madagascar, and 445 in captivity showed that auditory cues can initiate and guide insect hunting besides its relevance for 446 intraspecific communication such as during mate localization and advertisement [START_REF] Buesching | Multimodal oestrus 597 advertisement in a small nocturnal prosimian, Microcebus murinus[END_REF]447 Goerlitz & Siemers, 2007;[START_REF] Piep | The sensory basis of prey 746 detection in captive-born grey mouse lemurs, Microcebus murinus[END_REF][START_REF] Siemers | Sensory basis of food detection in wild 832 Microcebus murinus[END_REF][START_REF] Zimmermann | The complex acoustic design of an advertisement call in male mouse 880 lemurs (Microcebus murinus, Prosimii, Primates) and sources of its variation[END_REF]. Whether 448 the detected differences in ear size are responsible for different acoustic perceptions of males and 449 females needs to be investigated in future studies.

450

The slight female-biased sexual size dimorphism was uniform across the whole genus as indicated by its 451 isometric relationship (slope  1). Rensch's rule, according to which the ratio of female to male size is 452 expected to be larger the smaller the body size of the species (slope > 1), was not supported for mouse 453 lemurs. This finding is in line with the absence of Rensch's rule across a larger set of strepsirrhine 454 primates [START_REF] Kappeler | The evolution of sexual size dimorphism in prosimian primates[END_REF] and with the results of a large-scale study in birds [START_REF] Webb | Only half right: Species with female-biased sexual size dimorphism 862 consistently break Rensch's rule[END_REF]. 455

This deviation from Rensch's rule in mouse lemurs rather suggests an equal selection pressure on male 456 and female body size within the genus, as already proposed by [START_REF] Schmid | Fluctuating sexual dimorphism and differential hibernation by sex in 806 a primate, the gray mouse lemur (Microcebus murinus)[END_REF].

465

In mouse lemurs, climatic niche separation has been shown for a few taxa [START_REF] Kamilar | Ecological niche modeling of mouse lemurs 670 (Microcebus spp.) and its implications for their species diversity and biogeography[END_REF]. We 466 found statistical support for an influence of climate on morphology: tail length in mouse lemurs followed 467 the predictions of Allen's rule. Animals living in warmer and drier environments (i.e., higher 468 temperatures during the coldest months, lower precipitation in the driest and wettest months) had 469 longer tails than those living in habitats of opposite characteristics. Prevention of heat loss via shorter 470 tails is a likely explanation for this observation [START_REF] Serrat | Environmental temperature impact on bone and cartilage growth[END_REF][START_REF] Sgarlata | Genetic and morphological diversity of mouse lemurs (Microcebus spp.) in northern 823 Madagascar: The discovery of a putative new species?[END_REF]. As more than 68% of 471 the variation in tail length was explained by climate, this adaptation appears to have a close evolutionary 472 link to the surrounding environment.

473

Ear size could also be explained by climatic variables but to a much lesser degree than tail length. 474

Temperature-related variables, however, were not included in the best performing models. Instead, we 475 found that large-eared mouse lemurs were more likely to occur in areas of low amounts of rainfall during 476 the dry season. This relationship most likely echoes the divide between the western dry deciduous and 477 the eastern rain forests of Madagascar. These two macro-habitats differ in many aspects: dry forests are 478 subject to stronger seasonal changes in climatic conditions as well as resource availability and differ in 479 vegetation structure and composition from rain forests [START_REF] Blanco | Unpredictable environments, opportunistic 593 responses: Reproduction and population turnover in two wild mouse lemur species (Microcebus rufus 594 and M. griseorufus) from eastern and western Madagascar[END_REF][START_REF] Génin | Life in unpredictable environments: First investigation of the natural history of 634 Microcebus griseorufus[END_REF]. Acoustic 480 communication may be particularly constrained in rain forest habitats where high precipitation and high 481 vegetation density may lead to sound attenuation and diffusion [START_REF] Luther | Sources of background noise and their influence on vertebrate acoustic 713 communication[END_REF][START_REF] Rasoloharijaona | Does nonnutritive tree gouging in a 788 rainforest-dwelling lemur convey resource ownership as does loud calling in a dry forest-dwelling lemur? 789[END_REF]Taylor et al., 2022). Whether mouse lemur ear sizes are causally linked to the relevance of 483 intraspecific acoustic communication in different habitat types or show other adaptations, e.g., to 484 different foraging habits, cannot be tested with our dataset but warrants further investigation. The 485 difference between small-and large-eared mouse lemurs may further indicate an ancestral phylogenetic 486 constraint associated with an ancient split between dry and rain forest taxa [START_REF] Yoder | Mouse lemur phylogeography revises a model of ecogeographic 869 constraint in Madagascar[END_REF]487 Vences et al., 2009) and may not necessarily be driven by or highly interlinked with climatic adaptations.

488

In our test of Bergmann's rule, we found a relationship between body mass and length and climatic 489 variables, with larger/heavier individuals being more likely to live in warmer and wetter environments. 490

This finding contrasts the prediction of Bergmann's rule that individuals from colder environments 491 should be larger [START_REF] Salewski | Bergmann's rule: A biophysiological rule examined in birds[END_REF]. Although the association between the variables was relatively 492 low (R² ≤ 0.213), its direction agrees with results from other studies on small mammals in tropical 493 environments [START_REF] Maestri | 717 Geographical variation of body size in sigmodontine rodents depends on both environment and 718 phylogenetic composition of communities[END_REF][START_REF] Rodríguez | Bergmann's rule and the geography of 793 mammal body size in the Western Hemisphere[END_REF][START_REF] Sgarlata | The genomic diversity of the Eliurus genus in northern Madagascar with a putative new species[END_REF]. In all cases, body size 494 appeared to increase in warmer environments (e.g., at lower altitudes) probably due to an increase in 495 resource availability, suggesting that Bergmann's rule may only apply to animals in colder regions but not 496 in the tropics [START_REF] Rodríguez | Bergmann's rule and the geography of 793 mammal body size in the Western Hemisphere[END_REF]. This also conforms to general patterns of ecogeographic size 497 variation across lemurs of all sizes, both living and recently extinct [START_REF] Albrecht | Ecogeographic size variation among the living and 565 subfossil prosimians of Madagascar[END_REF] confidence intervals. The differences in all variables except inter-and intraorbital distances, lower leg 913 length, third toe length, and body mass were statistically significant (P ≤ 0.029; Table S3).

  rule: Female-biased sexual size dimorphism increases with decreasing body size 324

  rule: The size of body appendices decreases in colder climates 336

  400

  in primates is most likely driven by sexual selection, by natural selection of niche 414

898 899Figure 3 : 906 Figure 4 :Figure 5 :

 898390645 Quality assessment following the five data filtering steps indicated by different color code in B-900 D. A: Completeness of the dataset refers to the number of available values per variable compared to the 901 number of individuals in the raw data (n = 3,073). B: 1-W' measure of normality, C: absolute skewness, D: 902 absolute kurtosis. Values closer to zero in the ridge plots B-D indicate increased conformity to a normal 903 distribution. For skewness (C) and kurtosis (D), the range of acceptable values is indicated with the 904 vertical line at 2 units from 0. 905 Inter-observer reproducibility quantified as coefficient of variation (CV) across the 13 different 907 morphometric parameters. Only data after quality filtering was included. The horizontal line indicates 908 the upper limit of the CV values interpreted as very good reproducibility (CV=0.0-0.1, Aronhime et al.Sexual size dimorphism across all Microcebus taxa displayed via model estimates with 95% 912

914 915Figure 6 :Figure 7 : 924 Figure 8 :

 914679248 Relationship between female and male body length (A) and body mass (B). Rensch's rule is not 916 supported in both cases (P ≥ 0.705). Red line: linear model for observed data (including 95% confidence 917 interval), dashed line: slope = 1. Note: model estimates slightly differ between the standardized major 918 axis and the linear model; the former was used for hypothesis testing and the latter for visualization. 919 920 Partial regression plots for the linear model best explaining the variation in tail length (A-D, R² = 921 0.683) and ear size (E-F, R² = 0.450) across mouse lemur taxa. Note: the y-axis represents the marginal 922 impact of the response variable on the explanatory variable.923 Partial regression plots for the linear models best explaining the variation in body mass (A-B, R² 925 = 0.239) and body length (C-D, R² = 0.206) across mouse lemur taxa. Note: the y-axis represents the 926 marginal impact of the response variable on the explanatory variable.

  Refaly Ernest 6 , Mamy Rina Evasoa 7 , Alida Hasiniaina 7,8 , Daniel Hending 9 , Fabien 11 Jan 4 , Barbara le Pors 4 , Alex Miller 10 , Gillian Olivieri 7 , Ando N. Rakotonanahary 11 , Solofomalala Jacques 12 Rakotondranary 12 , Romule Rakotondravony 13 , Tantely Ralantoharijaona 13 , Veronarindra 13 Ramananjato 14,15 , Blanchard Randrianambinina 13 , Nancia N. Raoelinjanakolona 15 , Emilienne 14 Rasoazanabary

	1		downstream analyses	it is possible to use the whole dataset (e.g., for univariate analyses) or one that omits all Morphological variability or inter-observer bias?
	2	individuals containing NA values (i.e., for multivariate analyses). In the latter case, it should A methodological toolkit to improve data quality of multi-researcher datasets for
	3			be assessed whether remaining variables are still the analysis of morphological variation
				sufficient for testing the question under study.
	4
	5	Running title: Quality filtering of morphological data
	6
	7	Authors:
	8	Dominik Schüßler 1# ,
	9	Marina B. Blanco 2 , Nicola K. Guthrie 3 , Gabriele M. Sgarlata 4 ,
	10	Melanie Dammhahn 5 , 11 , Jordi
	17	Salmona 23 , Ute Radespiel 7#
	18	5	Exclude systematic	If there are several researchers who contributed to measurements of the same unit of investigation	For example, certain researchers measured mouse lemur hind foot
	19	deviations #: Corresponding authors: (i.e., species or population), variable-wise	length consistently different from
			among	comparisons may help to identify systematic	others across different
	20	researchers Dominik Schüßler, dominik.schuessler@posteo.de measurement errors. For this, we recommend	populations, which may indicate a
				visualizing the distributions of specific variables	different application of calipers
	21	across all units of investigation via boxplots, with Ute Radespiel, ute.radespiel@tiho-hannover.de	(Figure 2A). We applied the
	22			researchers represented as color-coded boxes.	following rules to exclude such inter-observer biases from our
	23	Affiliation:		dataset: (1) If < 3 researchers contributed measurements, it is
	24	1: Institute of Biology and Chemistry, University of Hildesheim, Germany	not possible to assess which measurements were more
	25	2: Duke Lemur Center, Durham, NC, USA	accurate; (2) if ≥ 3 researchers
					contributed measurements, boxes
	26	that do not overlap with each 3: Department of Anthropology and Archaeology, University of Calgary, Canada
					other and represent the smaller
	27	4: Instituto Gulbenkian de Ciência, Oeiras, Portugal	number of researchers (e.g., one
	28	5: Behavioural Biology, University of Münster, Germany	deviating from the others) indicate a researcher whose
	29				values for this variable should be set to NA. Examples are illustrated
					in Figure 2A.
		6	Define the	By replacing filtered values with NA (see previous
			dataset for	steps), we aimed at retaining as much information
				as possible. Depending on the analysis of interest,

16 , Rodin M. Rasoloarison 17 , David W. Rasolofoson 18 , Solofonirina Rasoloharijaona 13 , 15 Emmanuel Rasolondraibe 13 , Sam Hyde Roberts 19 , Helena Teixeira 7,20,21 , Tobias van Elst 7 , 16 Steig E. Johnson 3 , Jörg U. Ganzhorn 22 , Lounès Chikhi 4,23 , Peter M. Kappeler 17,24 , Edward E. Louis Jr. 6: QIT Madagascar Minerals, Fort-Dauphin, Madagascar 30 7: Institute of Zoology, University of Veterinary Medicine Hannover, Germany 31 8: School for International Training, Antananarivo, Madagascar

  3) Conformity with Allen's rule (i.e., body appendages are smaller in colder climates) was tested for ear 267 size (= ear width * ear length) and tail length by taking the mean of the respective variable for each 268 population and extracting six different bioclimatic variables (i.e., mean annual temperature, minimum 269 temperature of the coldest month and maximum temperature of the warmest month, mean annual 270 precipitation, precipitation of wettest and driest month; CHELSA 2.1, Karger et al., 2017). Means per 271 population for each bioclimatic variable were calculated to reduce spatial autocorrelation. We followed 272 the proposed methodology by Alhajeri et al. (2020) who conducted a global test for Allen's rule in 273 rodents. As temperature and precipitation are known to covary across latitudes, including both allows to 274 disentangle purely thermoregulatory aspects from other climatic factors. Mean annual temperature and 275 annual precipitation were not used due to high correlations with other variables (r ≥ 0.84). Linear models 276

  With our generalized suggestions for future morphological studies, we aim to 521 improve data collection procedures and overall quality in these kind of studies. Using this pipeline, we 522 filtered the largest currently available dataset of morphometric measurements for the species-rich genus 523 of mouse lemurs and generated a consolidated dataset with which four biologically meaningful 524 hypotheses were tested. 525 Morphometric variation was partly explained by sexual size dimorphism and by various bioclimatic 526 variables which were partly in agreement with the predictions of Allen's rule (tail length) but not with 527 those of Rensch's or Bergmann's rule. The remaining unexplained variation could be due to yearly and 528 monthly variations in resource availability, possible differences in resource use across lineages and 529 between the sexes, variation in intraspecific acoustic communication patterns and sociality, as well as 530 phylogenetic constraints. Our genus-wide analysis could therefore outline general patterns of 531 morphological variation in mouse lemurs. We further generated hypotheses for later testing in order to 532 fully understand the drivers of morphological variation within a genus of nocturnal, cryptic and small-Distribution of Microcebus spp. populations sampled across Madagascar. Overlapping symbols 889 were rarified to improve display. Minimum temperature of the coldest month according to Karger et al. 890 (2017) is shown in shadings from white to black. Illustration of steps 4 and 5 of the generalized data filtering pipeline. A: Boxplots per lineage 893 and researcher for one variable (hind foot length), boxes encircled in red indicate examples outlined in 894 Table 1. B and C: Histograms for ear length and interorbital distance, respectively, with truncation 895 threshold (median + 3 * median absolute deviation) introduced as vertical dashed red line. Truncation 896 (i.e., the exclusion of measurements for this variable for the respective researcher) takes place in B but 897 not in C.

	Figure captions
	887
	888 Figure 1: 891
	2022) can cope with thermoregulatory challenges under low temperatures using torpor or even hibernation (Blanco et al. 2018; Salewski & Watt 2016). However, more than 78% of the variation in body mass and body length could not be explained by climatic factors. It is known that resource availability, seasonality and most likely also phylogeny are additional drivers of mouse lemur body mass variation (Atsalis 1999; Génin 2008; Randrianambinina et al. 2003). Phylogenetic constraints on body size of mouse lemurs are indicated by the observation that several smaller taxa (M. berthae, M. myoxinus, M. lehilahytsara, M. rufus) form a distinctive clade of small-bodied mouse lemur species (Louis & Lei 2016) that can occur not only in sympatry with other larger species but also across a variety of environments in eastern and western Madagascar. These include the highly seasonal western dry deciduous forests with their clear separation into a 4-6 month hot rainy season and a 6-8-month cooler dry season lacking precipitations almost entirely, the central highlands that undergo substantial seasonal temperature fluctuations, and the eastern lowland rainforests that exhibit the least climatic variations across the year (Andriambeloson et al. 2020; Blanco et al. 2015, Evasoa et al. 2018). A comprehensive evaluation of the relative impact of environmental variables and phylogenetic constraints on the morphology of mouse lemurs is currently hampered by a lack of a fully resolved phylogeny of all currently described mouse lemur lineages. Conclusions Our new filtering pipeline was developed in order to enable analyzing a large multi-researcher dataset. We detailed on the six different filtering steps and illustrated their usefulness for reducing potential bodied primates. inter-observer biases. 533 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 534 535 892 Figure 2:

).

498

Several mouse lemur species (e.g., M. berthae and M. murinus at Kirindy, Schmid, 2000; M. griseorufus, 499

[START_REF] Génin | Life in unpredictable environments: First investigation of the natural history of 634 Microcebus griseorufus[END_REF] M. lehilahytsara, Randrianambinina et al., 2003, Andriambeloson et al., 2020

; M. 500 ravelobensis,

[START_REF] Thorén | 845 Seasonal changes in feeding ecology and activity patterns of two sympatric mouse lemur species, the 846 gray mouse lemur (Microcebus murinus) and the golden-brown mouse lemur (M. ravelobensis), in 847 northwestern Madagascar[END_REF] M. rufus, Atsalis, 1999, Ramananjato et al.,

Acknowledgements

CONFLICT OF INTEREST STATEMENT

554

The authors declare no conflict of interest. 555 556

557

DATA AVAILABILITY STATEMENT

558

The quality filtering pipeline, the assessment of data quality (R scripts), and all supplementary 559 information are available at: https://doi.org/10.25625/6EINJN 560