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Article

Assessing Natural Variation as a Baseline for Biodiversity Monitoring: The Case of an East Mediterranean Canyon

by
Meir Finkel
1,*,
Ariel Leib Leonid Friedman
1,
Hagar Leschner
2,
Ben Cohen
1,
Hoshen Inbar
1,
Shai Gelbert
1,
Agam Rozen
1,
Eitan Barak
1,
Ido Livne
1,
Ittai Renan
1,
Gilad Ben-Zvi
1 and
Orr Comay
1
1
The Steinhardt Museum of Natural History, Tel Aviv University, 12 Klausner Street, Tel Aviv 6997801, Israel
2
National Natural History Collections, Edmond J. Safra Campus, Giva’at Ram, The Hebrew University of Jerusalem, Jerusalem 9190401, Israel
*
Author to whom correspondence should be addressed.
Ecologies 2025, 6(1), 24; https://doi.org/10.3390/ecologies6010024
Submission received: 1 February 2025 / Revised: 28 February 2025 / Accepted: 4 March 2025 / Published: 11 March 2025
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Abstract

:
Accurately assessing the natural variation in biodiversity is crucial as a baseline for monitoring trends and attributing them to natural or anthropogenic drivers. To assess this baseline, we estimated the species richness, composition and abundance of plants, beetles and ants in Evolution Canyon II (Israel), a protected reserve in the Eastern Mediterranean that is known both for its heterogeneity and for faster-than-average climate change. Consecutive sampling over 24 months in three divergent microhabitats of the canyon (south-facing xeric and north-facing mesic slopes and the valley bottom) during 2019–2021 was conducted using the same methods employed at the same site during 1998–2000, enabling us to also study seasonal and inter-annual variation. Altogether, 459 beetle species, 349 plant species and 47 ant species were found. These taxa exhibit substantial and persistent divergence between canyon slopes. Despite substantial species turnover rates between periods in all the taxa, almost no change was found regarding the biogeographical origins of plant and beetle species composition. In addition, species richness differences between microhabitats persisted between study periods, and year-round sampling revealed many dominant winter-peaking beetle species. These findings reflect the importance of thoroughly surveying diverse taxa, microhabitats, seasons and annual weather patterns when characterizing the natural baseline of a monitoring program.

Graphical Abstract

1. Introduction

Monitoring plant and insect abundance and species richness enables the creation of databases necessary for modeling and assessing the impact of global warming and direct human intervention (urbanization, deforestation, grazing, etc.) on biodiversity, which has rapidly developed in recent years (for reviews and methodology concerns, see [1,2,3,4]). It builds upon extensive research on the natural drivers of richness and abundance, such as colonization, extinction mechanisms and habitat heterogeneity.
Several drivers have been suggested for the claimed insect declines, including habitat loss (due to agricultural encroachment, vegetation clearance, logging and urbanization), pollution (mainly by synthetic pesticides and fertilizers), biological factors (pathogens and introduced species), and climate change [1,5,6,7,8]. Among these drivers, long-term climate change was seen to shift the distribution of insect species, change their phenology and, through this, alter their interactions with other species [9]. In addition, due to its global extent, even protected habitats are affected by climate change, invalidating a key conservation measure.
However, while monitoring can help estimate trends, it is not straightforward to assess the drivers of such trends, especially since multiple stressors act simultaneously on the same ecosystem. Often, complex statistical modeling is required to point out substantial drivers of biodiversity trends [10,11], many times due to limitations in the monitoring design and methods. Likewise, a solid natural baseline for monitoring is required in order to estimate natural fluctuations in population sizes, phenology, etc., while assessing the extent of the ecological change that has occurred so far (e.g., how many and which species have become extinct), thus differentiating between actual trends and stochastic changes.
Moreover, Weisser et al. [4] stress that detecting trends alone is insufficient for insect conservation, and that well-designed experiments are required to understand the causes of these trends. The challenge of differentiating between natural (such as habitat and climate), indirect anthropogenic (such as climate change) and direct anthropogenic (such as deforestation, cultivation, pesticides) causes underlying insect biodiversity trends raises methodological difficulties regarding the ability to discriminate between their importance. Our research, conducted in a nature reserve within a small area—for which we have extensive knowledge of the biodiversity of its microhabitats—enables us to focus on natural causes. These differences should be taken as a baseline when attempting to decipher anthropogenic effects.
Plant and insect biodiversity research and monitoring in the East Mediterranean region is quite limited. The Levant–East Mediterranean is known for its biological heterogeneity compared with the neighboring temperate and desert regions [12,13,14,15,16,17]. This is mainly due to the fact it is located at the junction of Africa, Asia and Europe, which creates a species-rich biogeographic unit. It includes three primary climate zones: Mediterranean, grassland and desert, which hold diverse habitats and different phytogeographical and zoogeographical zones. Our research was conducted in northern Israel, a country with a profound south (Negev Desert region)–north (Upper Galilee Mediterranean region) climatic and phyto/zoological gradient [18] and rapid demographic and economic growth, both drivers of biodiversity declines. For instance, between 2000–2023, Israel’s population grew by 57%, with an average yearly growth of 2.0%, the Gross Domestic Product (GDP) grew by an annual mean of 4.1% (2.1% per capita) in 2012–2022 [19], and built up areas reached 18.2% of the total area north of Beersheba (i.e., the Mediterranean part of Israel; [20]). The Israeli case exemplifies the overall phenomena of higher biodiversity sensitivity of Mediterranean (and tropical) habitats to land-use and climate changes compared with other geographic regions of the world [21]. The Middle East and Eastern Mediterranean grew warmer by 0.45 °C per decade in 1981–2019, faster than the global trend (0.27 °C per decade) and faster than most inhabited regions of the world [22].
However, the Levant suffers from a lack of in-depth plant and arthropod biodiversity research ([23] and references therein), which also hinders long-term monitoring programs in this region [24,25,26]. Although our research is focused on the identification of patterns and not trends, it should be stated that in Israel, where the few biodiversity monitoring programs in the region are concentrated, arthropod monitoring is in its very early stages [20,27]. Hence, they offer few ecological insights, and detailed ecological information on local biodiversity is only available from focused studies. The unique characteristics of a Mediterranean canyon with opposing slopes enable us to gain an understanding of biodiversity issues related not only to the site location but also to the climatic gradient described above. By reusing the same surveying methods applied to our study site 20 years ago for another two years, we enhance the ecological knowledge about the site.
Microscale natural evolutionary laboratories, such as local microgeographic sites subdivided by sharp ecological contrasts, reveal dramatic inter-slope divergence from genes and genomes through to populations and species, ecosystems and biota [28,29]. In Israel, such research project sites are “Evolution Canyons” (ECs) with opposing south- and north-facing slopes that receive substantially different amounts of solar radiation (see below). The EC I microsite, located at Mount Carmel, has been studied since 1991, while the “Evolution Canyon” II (ECII) microsite, where our research is conducted, is located at the Western Upper Galilee and has been studied since 1998. These projects enable generalizations across life and organizational levels in an attempt to highlight unresolved issues of biological evolution [30,31,32].
The aim of the research is to quantify the impact of natural variations in microhabitats, seasons and annual weather patterns on the biodiversity of plants, beetles and ants at ECII as a case study of the ecological baseline for biodiversity monitoring. We present two 24-month periods of continuous monitoring with a 20-year interval in between, thus also covering annual variations in the weather.

2. Materials and Methods

The methods employed during the 2019–2021 period replicated those employed in the 1998–2000 research study, with minimal changes. In both periods, the fieldwork was led by the main author [33].

2.1. The Study Area—“Evolution Canyon” II, Lower Nahal Keziv, and Results from the 1998–2000 Period

“Evolution Canyon” II (ECII), located at Lower Nahal Keziv, Western Upper Galilee, Israel (33.033° N; 35.183° E) (Figure 1) is a Plio-Pleistocene canyon carved in Upper Cenomanian limestone [34] and is geologically identical on both slopes. The slopes in ECII are separated by 350 m at the top and only 50 m at the bottom. As with other canyons north of the equator, the opposite slopes display remarkable biotic contrasts and divergence due to differences in the amount of solar radiation, which is about 600% higher on the SFS than on the NFS [35,36,37]. In ECII, the soil moisture content was higher on the NFS than the SFS and was affected by seasonality, with lower soil moisture content in the summer, with the soil moisture content in the NFS twice that under trees in the SFS and three times higher than in open spaces in the SFS [38]. The average annual rainfall, measured at a meteorological station ~3 km away, is 784 mm (Israel Meteorological Service website: https://ims.data.gov.il, accessed on 21 June 2023).
The SFS is therefore warmer, drier and micro-climatically more heterogenous and fluctuating than the NFS. Spatiotemporally, the SFS represents a “broader niche” [39], with Mediterranean Maquis patches with the tree species of Quercus coccifera and Ceratonia siliqua, garrigue (shrub plant community) with the shrub species of Calicotome villosa and Salvia fruticosa and dry “savanna-like” grassland biota in its upper part with open patches hosting Gramineae (grasses) such as Hyparrhenia hirta, Andropogon distachyos, and Pennisetum ciliare (Appendix A.3: Figure A1); and subdivisions in which small variations in aridity amplify biotic divergence, both in space and over time. By contrast, the milder, cooler and more homogeneous NFS consists of lush and dense forest with no grassland openings. In the NFS and VB the dominant tree species were Acer obtusifolium ssp. syriacus, Laurus nobilis and Quercus coccifera.
ECII is located on the western part of the Keziv Stream Nature Reserve (declared in 1977) and has not been affected by cultivation, herding, the use of pesticides or urbanization. Therefore, climate change (and differences in the El Niño–La Niña cycle; [40]) and the overall increase in vegetation density in the Galilee [20] are the most likely drivers of any temporal differences in biodiversity between the two research periods. Another notable change in ECII is its accessibility: In the 1998–2000 research period, a vehicle gate was located ~200 m west of ECII, enabling visitors to walk into the nature reserve along a path by the stream. In 2012, two new vehicle gates were located on the road to the reserve ~1 km west of the original gate, therefore enhancing the protection of wildlife within ECII.
The research conducted between 1998–2000 revealed sharp inter-slope divergence with significantly higher species richness on the SFS compared with the NFS: in microfungi (SFS: 149 vs. NFS: 78) [38], plants (SFS: 205 vs. NFS: 53) [41], beetles (SFS: 307 vs. NFS: 198) [42] and ants (SFS: 19 vs. NFS: 12) [43]. Arachnid composition was found to be substantially different between slopes (SFS: 95 vs. NFS: 58) in the current research period of 2019–2021 [23].

2.2. Topography and Sampling Rows

The SFS dips 20–40° and has three sampling rows—1, 2 and 3 at altitudes of 200 m, 170 m and 140 m above sea level (ASL), respectively. Row 4 is at the valley bottom (VB) at 110 m ASL. The NFS dips 30–40° and has three sampling rows—5, 6 and 7—at equivalent altitudes to the SFS. All these rows lie perpendicular to the seasonal summer-dry riverbed on the valley bottom (Figure 2). Each row is 100 m long with an interval of 10 m between traps. The distance between row 1 and row 7 is 350 m.

2.3. Plant Survey and Documentation

Data collection: Plant species surveys were performed twice a year during 2020 and 2021 (in early-mid April and late May) to include all the annuals blooming in spring. Surveyors recorded all species (but did not quantify their abundance) along each sampling row for 40 min to 1.5 h, depending on the species richness. Species appearing from mid-summer to the end of the winter were recorded during the insect collections (see below) and added to the lists. The method employed was the Whittaker plant sampling design [44], the same that was used in the 1998–2000 sampling period [41].
Species identification and comparison to the 1998–2000 research period: Species identification was conducted in the field. Plants recorded during the 2019–2021 period were compared to the full species list from the 1998–2000 period (sums and statistics were published by Finkel et al. [41]). A total of 366 plant species from both research periods were recorded. A flood event in the winter of 2019–2020 dramatically changed the local stream bed, creating a new terrace on its northern bank. In the spring of 2020, 17 species new to the VB were recorded. These species were not found in the subsequent year, 2021. These 17 species were therefore excluded from the count, and 349 species were included in further analyses. Taxon nomenclature and global distribution patterns were described according to Danin and Fragman-Sapir [45]. Global distribution patterns were divided into either “northern”, “southern” or “other”, as detailed in Appendix A.1.

2.4. Beetle and Ant Collection and Identification

Material collection: All insects were collected based on recommended methods [46,47], which were identical to those used in the 1998–2000 sampling period. Insects were collected using two methods: (1) Ground-dwelling beetles and ants were collected with pitfall traps. Ten traps were placed per sampling row, with an interval of 10 m between traps (Appendix A.3: Figure A1). The traps were open during a 24-month period from 4 September 2019 to 29 August 2021 and were emptied roughly every 3 weeks (altogether, 34 collection events). All specimens were collected, counted and analyzed. (2) Collection with a sweeping net was conducted every 10 days during 2020–2021 from March to mid-July, (14 collection events each year). The sweeping duration was equal in all seven rows. This method is effective for the collection of diurnal flying beetle and arboreal ant species. All species were recorded, and analysis was based on presence–absence data (see Appendix A.3 for rationale).
Species identification and comparison to the 1998–2000 research period:
Beetles: Identification was conducted using the International Commission on Zoological Nomenclature, and all collected specimens were deposited in the Steinhardt Museum of Natural History, Tel Aviv University (SMNHTAU). Specimens collected during the 2019–2021 research period were compared to the specimens from the 1998–2000 research period kept in the national Coleoptera collection (except for a few species whose specimens were sent abroad and not returned). The number of specimens in each row was compared to the data in Finkel et al. [42] (in Apionidae and Curculionidae, the number of specimens was counted from the collection itself). Due to identification challenges, some of the specimens were identified only to the genus level (mainly in the Melyridae) or as either one of two similar species. We focused our identification effort on 12 superfamilies/families with the highest number of species (Appendix A.2). In total, 459 beetle species from both research periods were analyzed. These species were classified according to their global distribution pattern as either “northern”, “southern” or “other”, as detailed in Appendix A.1.
Ants: Identification was conducted at SMNHTAU, where all the specimens were deposited. Unfortunately, the specimens from the 1998–2000 research period were not found, and the data presented in Finkel et al. [43] was minimal (presence/absence on the SFS, NFS and VB). Therefore, in our comparison, we had to stick to this level of information. In those cases in which species identification in the 1998–2000 research period was in doubt due to changes in nomenclature during the intervening years, or in cases where very similar species were suspected to be wrongly identified, we erred on the side of caution and grouped species to avoid spuriously inflating the species richness. In total, 47 ant species from both research periods were analyzed.

2.5. Statistical Analysis

Based on the presence–absence data, we conducted the following analyses for the plant, ant and beetle species: species richness and Venn diagrams to compare overlaps between the research periods for each slope, model-based ordinations for species composition, and multivariate species-specific responses to the environmental predictors (see Appendix A.4). All statistical analyses were conducted in R version 4.3.0. GLMs were fitted using the glmmTMB [48] R package, while Venn diagrams were drawn using the ggvenn [49] and ggplot2 [50] R packages. Data from the Venn diagrams are summarized in Table 1. The species overlap percentage between slopes was defined per study period (either 1998–2000 or 2019–2021) as the number of species occurring on both the SFS and NFS divided by the total number of species. The species richness ratio was calculated by dividing the species richness on the SFS by the species richness on the NFS. The turnover rate was defined per slope (either NFS or SFS) as the number of species occurring either in 1998–2000 or in 2019–2021, but not in both, divided by the total number of species.
Model-based ordinations were generated via the R package ecoCopula [51]. An ecoCopula ordination is a computationally fast model-based alternative to ecological distance-based ordinations of species composition that accounts for inter-specific correlations [52]. Multivariate species-specific responses to environmental predictors were fitted via the R package gllvm (Generalized Linear Latent Variable Models) [53,54]. In all the model selection processes, we selected the model with the lowest Akaike Information Criterion (AIC; [55]) among the nested models that included all possible combinations of the predictors (see Appendix A.4 for specifications per analysis). Whenever the AIC differences between nested models were lower than 2, we preferentially chose the simpler model (with fewer parameters, predictors and interactions).
Seasonality of beetle species was analyzed for the 10 most common beetle species. We selected the model via the R package MuMIn [56]. Afterward, we plotted the predicted and observed daily abundance by the chosen model to visually examine each species’ seasonality.
For further details regarding the statistical analysis, please see Appendix A.4.

2.6. Environmental Changes Between Periods

NDVI: For analyzing trends in the NDVI, we used LANDSAT [57,58] values per transect per year for 1984–2022, using mean values for 15 August to 30 September to capture the summer NDVI annual minimum. Data were analyzed using ENVI version 5.6.3. (Exelis Visual Information Solutions, Boulder, CO, USA).
Weather: Homogenized temperature for 1975–2022 and rainfall data for 1950–2021 at the nearest meteorological station (33.065° N, 35.217° E), situated 3 km north of ECII, were downloaded from the Israeli Meteorological Service website (https://ims.gov.il/he/node/1980, accessed on 21 June 2023). Rainfall data were available as the total daily rainfall in mm. Temperature data were available as the daily minimum and maximum in degrees Celsius (henceforth °C). We analyzed the trends in total annual rainfall, start and end of the rainfall season, and mean daily temperature.

3. Results

3.1. Climate Measures

Rainfall: The mean annual rainfall in 1950–2020 was 783.9 mm. We found no significant trend in total annual rainfall in either 1950–2021 or in 1995–2021 (p = 0.598 and p = 0.638, respectively). Likewise, we found no significant trend in the date of the first and last rains of the season in both periods (1950–2021: first rains, p = 0.417; last rains, p = 0.786. 1995–2021: first rains, p = 0.165; last rains, p = 0.872) (Tables S1 and S2). It should be noted that there was a considerable difference between the two research periods (1998–1999: 530 mm; 1999–2000: 700 mm; 2019–2020; 870 mm; 2020–2021: 800 mm), but the amount of rain does not affect the onset of plant blooming, only the duration of their bloom.
Temperature: The mean temperature for the four study years (September 1998–August 2000 and September 2019–August 2021) was 20.3 °C (sd = 5.2 °C). We found a significant warming trend for 1975–2022 (p < 0.001) and for 1999–2021 (p < 0.001) at a rate of 0.04 °C per annum for both periods (Tables S3 and S4). For a seasonal analysis, see Appendix B.1; Figure A2.

3.2. Plants

3.2.1. General Findings

Combining surveys from all the microhabitats and research periods, we found a total of 349 plant species. The life-form composition of ECII did not differ between the study periods (Table S5). This clearly illustrates the distinct inter-slope differences presented in Section 2.1.

3.2.2. NDVI Results

Mean NDVI values in 1998–2000 were 0.44, 0.52 and 0.52 for the SFS, VB and NFS, respectively, while in 2019–2021, the corresponding values were 0.63, 0.73 and 0.76. We found significant differences between the slopes (p-value ≤ 0.027), while the difference between the NFS and VB is insignificant (p = 0.233) (Appendix B.2: Figure A3). Additionally, we found a significant positive trend (p-value < 0.001), which varied in pace between slopes: between 1999–2021, the best-fitting model predicted 19.8%, 30.2% and 25.4% increases in the NDVI on the NFS, VB and SFS, respectively (Table S6).

3.2.3. Detailed Comparison

Table 1 shows persistency in the inter-slope divergence pattern of very low species overlap. A 40.2% species turnover was detected in VB. The change was mostly in annuals (out of the 87 non-overlapping species, 60 are annuals and none are trees); hence, the overall vegetation structure was unchanged.
Table 1. Plant, beetle and ant species richness per slope and study period: species overlap between slopes (species found on both the SFS and NFS/(species found on the SFS + species found on the NFS − species found on both the SFS and NFS)); species richness ratio between slopes (species number on the SFS/species number on the NFS); turnover rates between study periods per slope (species found only in the 1998–2000 period + species found only in 2019–2021)/(total number of species found on the slope).
Table 1. Plant, beetle and ant species richness per slope and study period: species overlap between slopes (species found on both the SFS and NFS/(species found on the SFS + species found on the NFS − species found on both the SFS and NFS)); species richness ratio between slopes (species number on the SFS/species number on the NFS); turnover rates between study periods per slope (species found only in the 1998–2000 period + species found only in 2019–2021)/(total number of species found on the slope).
TaxonResearch PeriodSFSOverlap Between SlopesNFSSpecies Richness Ratio
Plants1998–2000 205 28 (12.2%) 53 3.87
2019–2021 212 31 (13%) 57 3.72
Turnover87/252—34.5% 20/65—30.8%
Beetles (pitfall traps)1998–20007349 (50.5%)731
2019–20215226 (37.1%)441.18
Turnover63/94—67% 59/88—67%
Beetles (net)1998–200010519 (16.5%)293.62
2019–20218513 (12.5%)322.66
Turnover134/162—82.7% 43/52—82.7%
Ants1998–2000189 (47.4%)101.8
2019–20213017 (45.9%)241.25
Turnover20/34—58.8% 20/27—74.1%
Persistency was also observed in the inter-slope global distribution composition of plant species, which was found to be similar in both periods (Figure 3). Plant species richness (Table S5) was found to be slightly higher on the NFS, SFS and VB during 2019–2021 compared with 1998–2000. Total species richness was significantly higher in 2019–2021 compared with 1998–2000 (p = 0.037). The differences between the NFS, VB and SFS are statistically significant (p < 0.001) in both periods.
Plant community composition was found to be stable on the NFS, and it differs most between the study periods on rows 1 and 2 (SFS) and the VB (Figure 4).

3.3. Beetles

Combining surveys from all the microhabitats and research periods, we found a total of 459 beetle species, with high overlap and turnover rates (Table 1). The following key observations can be made:
Species richness (Table S7) pattern: The species richness ratio (numbers), and species global distribution composition between the slopes and the VB were similar in both periods in ground-dwelling and net-collected beetles, although the numbers were lower from the NFS, SFS and VB during 2019–2021 compared with 1998–2000 (Figure 5). (1) Pitfall traps: The final GLM included only the period as a predictor. Species richness was significantly (p < 0.001) lower in 2019–2021 compared with 1998–2000 in all localities within the research area (NFS, VB, SFS). (2) Net collection: The final GLM included both period and slope as significant predictors, indicating a significant difference in species richness between the slopes (p < 0.001), as well as between the research periods (p = 0.014).
When looking at the entire beetle fauna from both the pitfall and net collections (Figure 6A), the picture that emerges shows a similarity in the clustering of rows within the slopes and a similarity in the NFS species composition between periods, while for the SFS, we found a difference in the species composition, which is smaller than the dramatic difference between study periods in the VB species composition. This is similar to the findings for plants (for ecoCopula results of net-collected beetle species only, see Appendix B.3: Figure A4).
In the pitfall trap collection, which was standardized and thus included abundance, we conducted an additional ecoCopula ordination analysis to include only abundant species (with at least 10 individuals collected; see Figure 6B). The species composition of the SFS and NFS in the 2019–2021 period is more similar than the composition of the SFS and NFS during the 1998–2000 period, testifying to an “averaging” of the inter-slope difference.
Comparison of the abundance of ground-dwelling beetles between slopes was conducted according to the best-fitting GLLVM for ground-dwelling beetles (Figure 7). A total of 20 out of 65 species exhibited significantly higher numbers of specimens on the NFS compared to the SFS, while 15 species had the opposite pattern (30 species had no significant pattern between the SFS and the NFS). Of the species that had a significantly higher abundance on the NFS, six were species of northern origin compared with only two species of southern origin; in contrast, of the species that had a significantly higher abundance on the SFS, only three were species of northern origin compared with seven species with southern origins.
For the 2019–2021 period, seasonality was statistically significant and was included in the chosen model in all of the 10 most common beetle species caught by pitfall traps (Table S8). Of these species, six (Orthomus longior, Ocypus mus, Opatrum libani, Nimbus harpagonis, Calatus syriacus, Carabus sidonius) peaked during the winter (December–February). Figure 8 illustrates the winter seasonality of O. longior and O. mus, the first and third most common pitfall beetle species (For the other eight species, see Appendix B.4: Figure A5, Figure A6, Figure A7, Figure A8, Figure A9, Figure A10, Figure A11 and Figure A12). Table S9 details the number of beetles caught in pitfall traps in 1998–2000.

3.4. Ants

There is a similar overlap pattern in the species lists between the slopes in both periods (47.4–45.9%) and a difference in turnover between the research periods—SFS: 58.8%; NFS: 74.1% (Table 1); and 64.5% at the VB—attesting to a substantial change in species composition, mainly due to the rise in species richness presented below.
Species richness (Figure 9; Table S10) was found to be significantly (p < 0.001) and substantially higher in the 2019–2021 period than in the 1998–2000 period (Figure 9), although the species ratio pattern between the slopes remained similar. No significant differences in species richness were found between the slopes.

4. Discussion

4.1. Persistence of Inter-Slope Differences Despite a High Species Turnover

Despite significant warming (+0.8 °C) and maquis encroachment (+0.2 NDVI) trends and a considerable variation in annual precipitation (+220 mm) in 20 years, we found similar rates of overlap between the slopes in species composition and richness in the two research periods (Table 1), although no physical barrier exists between the slopes. These results are evidence for the continuous and strong impact of solar radiation, which affects soil moisture, resulting in differences in biodiversity. The species turnover between the research periods is lowest in plants (~35%), higher in ants (~65%) and ground-dwelling beetles (~67%), and highest in beetles collected by nets (~83%). Likewise, the community structure ordinations point out that samples from the same slope are often more similar than those from the same study period (Figure 4 and Figure 6), despite very high species turnover rates within each slope (Table 1). The stability of composition in broad terms is NFS > SFS > VB. The NFS is a unified stable habitat, while the SFS contains various habitats.
Our interpretation of the findings is that stability of solar radiation and the resulting inter-slope microhabitat differences overrule inter-period differences. As human disturbance in ECII is minimal due to the protection of the entire area within the Keziv Stream Nature Reserve, these patterns should be considered the natural baseline for future assessments of biodiversity patterns and can likely be explained by natural stochastic dynamics.
Our findings of inter-slope divergence persistency, despite several changes during the 20-year interval, attest to the fundamental importance of habitat availability. We maintain that the overall vegetation structure (and hence microhabitat) was consistent within each slope (Section 3.2.1) and, therefore, the microhabitat divergence between slopes was likewise persistent. Similarly, research in Britain of the arthropod range shift over 40 years during a period of 0.8 °C warming showed that habitat availability was a more important factor than climate change [59]. Research on insects in Switzerland demonstrated that habitat types (agricultural, unmanaged (open and forested) and managed forest habitats) were the most important predictors of insect richness, abundance and biomass compared with temperature, precipitation, vegetation index and elevation [11]. We assert that differences between natural habitats should be studied as a basis for understanding the complex relations between them and other natural and anthropogenic factors affecting biodiversity.

4.2. Implications for Monitoring Anthropogenic Effects on Biodiversity

As described in the introduction, biodiversity research aiming at better understanding the factors involved in plant and insect composition and richness change and stability suffer from the multitude of possible factors, both anthropogenic (direct and indirect) and natural, and the complex interactions between them. The advantages of the “Evolution Canyon” research method—and specifically, the location of ECII within a well-protected nature reserve, with its contrasting slopes with no physical barrier in between and differentiated dramatically by solar radiation, thereby creating very different micro habitats—are exemplified by the results presented in this paper. Namely, the opposing slopes with their distinct microhabitats host very different flora and arthropod fauna compositions, despite the environmental changes over the last 20 years, including continuous rising temperatures and NDVI and differences in precipitation between the research periods. Our methodological suggestion is that when trying to monitor anthropogenic effects on biodiversity, natural sites containing various habitats should be monitored simultaneously as a baseline or control group.
Furthermore, we call for the monitoring of a broad range of taxa and ecological indices with varying stability within the same site. Namely, woody plants (and hence overall microhabitat structure) demonstrated high stability between study periods, while beetles and ants had high turnover rates (Table 1). Similarly, beetles showed greater differences in species composition between the study periods compared with plants (Figure 4, Figure 6 and Figure 7). Another example is species richness vs. species composition: while plant species richness was high in both the VB and the SFS (making these micro-habitats difficult to differentiate based on this index), species composition analysis via ordination clearly demonstrated the difference between these microhabitats (Figure 3 and Figure 4). Furthermore, ground beetle species-specific abundance significantly differed between the microhabitats (Figure 7), suggesting that this index might contain ecologically important information not provided by other indices such as species lists or richness. This conclusion echoes that of Belmaker [60], stressing the greater sensitivity of behavior and abundance over species occurrence alone.
The continuous sampling effort over 24 consecutive months enabled the understanding that many of the most abundant ground-dwelling beetle species within ECII peak during the winter (Figure 8), a season usually not surveyed in entomological monitoring programs in the northern hemisphere [61]. This was also found for arachnids in ECII [23]. One implication is that if sampling is seasonal (e.g., only in the spring and summer), species richness or lists could be better indicators than abundance, since the sampling of even a single specimen of a species peaking outside the sampling season would lead to their inclusion in the data, while their abundance would be underestimated. Another is that even if long-term monitoring programs rely on spring and summer sampling, dedicating even one year of a full 12 months of sampling will yield important information regarding the possibility of dominant “winter species”. Our findings are in line with other works conducted around the Mediterranean with its marked seasonality, such as research on the Carabidae and Saproxylic beetles conducted in the Israeli Upper Galilee, where 10 out of 34 species were found to be “winter species” [62]; Saproxylic beetles in western Spain, where four families exhibited a winter preference [63]; and the dung beetle in Tunisia, where Aphodiidae dwellers showed a winter preference [64]. The authors of those papers all gave recommendations similar to ours, which echo and expand those of Montgomery et al. [47], who recommended that monitoring take place over the entire activity season of the taxon of interest; in warm climates, this could often mean surveying throughout the year [27].
The “Evolution Canyon” method is a complementary method to others employed in the Palearctic region for monitoring biodiversity along a climatic gradient (for example, [10]) or altitudinal gradients of mountain slopes (for example, [65,66]). If this continues to be used at the same site in the next few decades, it could aid at better differentiating between anthropogenic and natural causes for biodiversity change. Our observations also exemplify the need to protect such sites in the Palearctic region, as contrasting slopes, while within a close distance to each other, are, in practice, hosting very different complexes of flora and arthropod fauna.

4.2.1. Effects of Rainfall Variation

The effect of differences in the yearly amount of rainfall within a Mediterranean region (characterized by 450–800 mm) results in two major differences in vegetation: the length/heights of annuals or growth of perennials (=biomass) and the length of the flowering season. Even in the case of the winter of 1998–1999, with only 530 mm of rain, all the annuals germinated, grew and bloomed and were therefore counted in the survey. As flying beetles are dependent on plants flowering, their appearance in that year should be similar to other years within the above-mentioned precipitation range although harder to detect due to their shorter life span. It should be noted that from the 20-year perspective, the rainfall amount differences are fluctuations, as the current data from the adjacent meteorological station shows no significant trend, and the predicted long-term trend is the opposite, with a decrease in precipitation anticipated [67]. One possible implication for biodiversity monitoring in areas with fluctuating rainfall is to survey several years with variable weather (e.g., El Niño vs. La Niña years) to establish a baseline for assessing trends.

4.2.2. Effects of Natural Stochastic Dynamics/Autonomous Turnovers

Changes in arthropod community composition due to natural stochastic dynamics are well documented in the literature. They usually show a typical pattern in which there is a mixture of declines and increases, resulting in overall stable species richness, while species compositions change due to high species turnover. Such patterns were documented in Carabid beetles in England [68], in tropical ant communities in Ecuador [69] and Panama [70] and in arthropod communities in Puerto Rico [71] and the Arctic [72].
In modeling species turnover, O’Sullivan et al. [73] found that species richness attains an equilibrium despite continuous species turnover, and local communities, on average, “turn over faster than the regional metacommunity of which they form” due to invader influx compensating for diversity loss. Later, O’Sullivan et al. [74] termed the intrinsic ecological dynamics “autonomous turnover”. They claimed that species composition turnover exists due to “ecological structural instability—the mechanism that also limits local biodiversity” without a need for an external force such as environmental change or the invasion of new species.
We suggest that within the slopes, what we found is “autonomous turnover”, while between-slope persistency that does not “cross” the inter-slope “border” is kept stable. “Autonomous turnover” in natural habitats should be considered as another aspect of the baseline for biodiversity monitoring.

5. Conclusions

Studying biodiversity trends and attributing them to anthropogenic impacts requires a solid baseline of the natural biodiversity in the monitored area. Characterizing this baseline requires surveying varied taxa, microhabitats, seasons and annual weather patterns in natural environments over several years, as natural processes can lead to varying monitoring results over short timescales and small spatial scales, especially in sensitive indices such as species-specific abundance and phenology.

Supplementary Materials

The following supporting information can be downloaded at: https://doi.org/10.5281/zenodo.14974671. Table S1: Homogenized precipitation data (Eilon 1950–2017.csv) and homogenized rainfall data (corrected for differences in measuring devices, exact station location, etc.) (Eilon rain data 2018–2021.csv) for the period 1950–2017. The data for these earlier years are homogenized to match those of later years (available in Table S2) to allow for a direct comparison. Table S2: Eilon rain data 2018–2021.csv: Rainfall data per day in 2018–2021. Dates with no rainfall are missing from the file. As noted above, data for earlier years were homogenized to match those of these years. Table S3: Temperature_comb_stations.csv: Homogenized temperature data (corrected for differences in measuring devices, exact station location etc.) in degrees Celsius. TN = minimum temperature. TX = maximum temperature. Table S4: Eilon temperature data 2018–2022.csv: Temperature data per day in 2018–2022. Same two meteorological stations. Table S5: Plants data.csv: Presence–absence data of plant species per line (L1 to L7) and period combination (1998–2000, indicated as 2000, vs. 2019–2021, indicated as 2020). Table S6: NDVI.xlsx: NDVI values for August 15th to September 30th (summer minimum) from LANDSAT and MODIS (not analyzed in the paper) for 1984–2022. Columns 1 to 7 relate to the line numbers at the site. Table S7: Net and pitfall combined.csv: Beetle species presence–absence in 1998–2000 and 2019–2021. All combinations of beetle species, slope (south-facing, north-facing or valley bottom, abbreviated as SFS, NFS or VB, respectively), line number (1–3 for SFS; 4 for VB; 5–7 for NFS), research period and either their presence (1) or absence (0) are noted. Species trapped by pitfall traps and by nets are given together unseparated. Table S8: Beetles 2019–2021.xlsx: The raw data and per line summary (see row.summary sheet) of the beetle species trapped either by pitfall traps (the trap number is specified in column G in the sheet raw.data) or by net (where the trap number is unspecified) for the study period of 2019–2021. The number of individuals is only meaningful for the pitfall traps, since only a few voucher specimens were collected per species in the net trapping. The row.summary sheet summarizes the total number of individual beetles per research period, sampling row and collection method (pitfall traps or sweeping nets) and also provides the species’ global distribution pattern. The sheet raw.data details the exact collection dates and trap numbers (for pitfalls only). Table S9: Beetles 1998–2000.xlsx: The number of individuals trapped by pitfall traps in the 1998–2000 research period for the four most common species only (Ocypus mus, Maldera syriaca, Dendarus plicatulus and Orthomus longior). The original data per survey date was not recovered, but a monthly total was available. Hence, the number of survey dates (either one or two) is indicated per calendar month, along with the mean date (which is the exact date when only one survey was conducted at a given month). The sheet of sampling dates details all 30 survey dates for the 1998–2000 pitfall trappings. Table S10: Ants.xlsx: Columns include the species names of all the ant taxa found in the study and research period and the total number of individuals caught in pitfall traps in the north-facing slope (N), south-facing slope (S) and the valley bed (VB). File S11: Keziv Stream Coleoptera code for publication.R—The R code used for the analysis described in the paper. Table S12: Trap microhabitats 2019–2021.csv: The microhabitat of all pitfall traps, defined as either “open” (pitfall trap was not positioned near any plant), “shrub” (pitfall trap positioned near a shrub) or “tree” (pitfall trap positioned near a tree). SFS = south-facing slope; NFS = north-facing slope; VB = valley bed.

Author Contributions

Conceptualization, M.F. and O.C.; methodology, M.F., G.B.-Z. and O.C.; validation, A.L.L.F. and H.L.; formal analysis, O.C.; investigation, M.F., B.C., H.I., S.G., A.R., E.B. and I.L.; resources, M.F. and G.B.-Z.; writing—original draft preparation, M.F., G.B-Z. and O.C; writing—review and editing, M.F., A.L.L.F., H.L., I.R., G.B-Z. and O.C.; visualization, O.C. and M.F.; supervision, M.F.; project administration, M.F. and G.B-Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Code (File S11) and data are available from https://doi.org/10.5281/zenodo.14974671.

Acknowledgments

The authors wish to thank the “Tefen High School Biodiversity Research Project”. Tefen High School is located in Western Upper Galilee, Israel. The multiyear research project is aimed at monitoring and gaining a better understanding of the flora and invertebrate fauna biodiversity in the Upper Galilee nature reserves. Under the supervision of Meir Finkel and in collaboration with Israel’s top researchers in those fields, pupils work and prepare their high school theses. This study was conducted under Israel Nature and Parks Authority (INPA) permit numbers 2019/42320 and 2020/42545 to Meir Finkel. We thank the Israel Nature and Parks Authority for their permission to work in the Keziv Nature Reserve. We thank Armin Ionescu, collection manager of ants (Formicidae) and Aculeata (Hymenoptera) at the Steinhardt Museum for ant identification. We wish to thank Igor Armiach Steinpress from the National Natural History Collections, The Hebrew University of Jerusalem, for the preparation of Figure 1 and Figure 2.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Extended Methods

Appendix A.1. Definitions of Global Distribution Patterns

Plants: “Northern”—includes northern latitude/humid regions and their combinations (Mediterranean; Euro-Siberian; Euro-Siberian/Mediterranean; Euro-Siberian/Mediterranean/Irano-Turanian; Euro-Siberian/Western-Irano-Turanian; Mediterranean/Euro-Siberian; Western Euro-Siberian/Eastern Mediterranean); “southern”—includes southern latitude/arid regions and their combinations (Eastern Mediterranean; Southern Mediterranean; Irano-Turanian; Saharo-Arabian; Mediterranean/Irano-Turanian; Mediterranean/Western Irano-Turanian; Eastern Mediterranean/Western Irano-Turanian; Southern + Eastern Mediterranean; Mediterranean/Irano-Turanian/Saharo-Arabian; Saharo-Arabian/Eastern Mediterranean/Western Irano-Turanian); “other”—includes species whose distribution could not be attributed to the north or south.
Beetles: “Northern”—includes northern latitude/humid regions (Palearctic; West Palearctic; Northeastern Mediterranean; European Mediterranean; Holarctic); ”southern”—includes southern latitude/arid regions (Mediterranean-Central Asian; Irano-Turanian; Southeastern Mediterranean; Saharo-Arabian; Arabian); “other”—includes species whose distribution could not be attributed either to the north or south (Levantine; Circum-Mediterranean; Eastern Mediterranean, Cosmopolitan); “unknown”—specimens identified only to the genus level or with a degree of doubt between two similar species.

Appendix A.2. Beetle Families on Whom the Taxonomic Identification Effort Was Focused (Listed Alphabetically)

Bruchidae; Buprestidae; Carabidae; Cerambycidae; Chrysomelidae; Coccinellidae; Curculionoidea (including Anthribidae, Apionidae, Brachyceridae, Curculionidae and Rhynchitidae); Dermestidae; Melyridae; Scarabaeoidea (including Glaphyridae, Ochodaeidae and Scarabaeidae); Staphylinidae; Tenebrionidae

Appendix A.3. Extended Descriptions of Methods

Pitfalls: The pitfalls had an inner diameter of 10 cm and a depth of 10 cm. They were filled to a 1 cm depth with a preservation liquid composed of ethylene glycol (50%), water (30%) and ethanol (20%). In contrast with the 1998–2000 period, ethanol was added to better preserve arachnoids, which were collected too.
The number of ant individuals was recorded, but this does not represent actual abundance, because sometimes dozens of ants belonging to one colony fell into a pitfall trap while in other cases, only one or two fell in. The number of falling occurrences does represent abundance but indirectly. It is important to note that, unlike flying beetles, where the find of a rare specimen may be attributed to an occasional windstorm, in the case of ants, even the collection of a single worker attests to the presence of a nearby nest.
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Figure A1. Schematic representation of the microhabitat (under tree, shrub, or in the open patch) of pitfall traps in the study. On the left of each row in the scheme, the slope and the number the row is written. Each cell represents a pitfall trap. Green cells represent pitfall traps installed under trees, orange cells represent pitfall traps installed under shrubs, and yellow cells represent pitfall traps installed in the open patches. This information is also available in Table S12.
Figure A1. Schematic representation of the microhabitat (under tree, shrub, or in the open patch) of pitfall traps in the study. On the left of each row in the scheme, the slope and the number the row is written. Each cell represents a pitfall trap. Green cells represent pitfall traps installed under trees, orange cells represent pitfall traps installed under shrubs, and yellow cells represent pitfall traps installed in the open patches. This information is also available in Table S12.
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Sweeping nets: The abundance of flying beetles and arboreal ants is very difficult to measure reliably due to the fact that in the Mediterranean spring, single flowers can host dozens of specimens, making comparison between rows very problematic, as even adding one sweep on certain flowers can make a substantial difference and result in biased data. Due to that reason, in each collection, we caught only 1–2 specimens of a species in each row. Therefore, species collected by sweeping nets were analyzed only as present–absent.
Weather data: We used homogenized rather than raw weather data. Homogenized weather data include corrections for break points in the dataset, which usually represent spurious trends resulting from changes in the exact station location or measuring device. The use of homogenized data is crucial for detecting true climate change [75].

Appendix A.4. Statistical Specifications for the Full Models per the Analyzed Index

Here, we describe the full models from which predictors and interactions were gradually removed in a stepwise backward model selection process based on the AIC.
Species richness:
  • Taxa: Plants, beetles (separately for flying and ground-dwelling species) and ants.
  • Dependent variable: Species richness per period and line combination.
  • Model type: Generalized linear model (GLM).
  • Distribution: We used the dispersion parameter to choose between Poisson and negative binomial distributions.
  • Predictors: Period (1998–2000 vs. 2019–2021), the slope (north-facing, south-facing or valley bottom) and their interaction.
  • Software: glmmTMB package [48] for R [76].
NDVI:
  • Taxon: Plants.
  • Dependent variable: Spatial mean of NDVI values from August 15th to September 30th per annum from 1984 (start of data) until 2022 (one year after the end of the second research period).
  • Model type: Generalized linear mixed model (GLMM).
  • Distribution: Gamma.
  • Predictors: Years since 1984 (start of data), the slope (north-facing, south-facing or valley bottom) and their interaction, and the line number in the study site (1 to 7) as a random predictor.
  • Software: glmmTMB package [48] for R [76].
Total rainfall:
  • Dependent variable: Total annual rainfall (mm) from September to August.
  • Model type: Generalized linear model (GLM).
  • Distribution: Gaussian (normal).
  • Predictors: Years since 1950 (start of data)/1995 (a few years before the first research period).
  • Software: glmmTMB package [48] for R [76].
Rainfall season:
  • Dependent variable: No. of days since September 7th (the earliest recorded rainfall event with at least 10 mm, for a rainfall season defined from September to August) until the calendar date of the first/last day of the rainfall season with at least 10 mm of rainfall.
  • Model type: Generalized linear model (GLM).
  • Distribution: Gaussian (normal).
  • Predictors: Years since 1950 (start of data)/1995 (3 years before the first research period).
  • Software: glmmTMB package [48] for R [76].
Temperature:
  • Dependent variable: Mean daily temperature (°C).
  • Model type: Cosinor generalized linear model (cosinor GLM; [77,78]).
  • Predictors: Years since 1975 (start of data), sine and cosine of the radian distance between the calendar date and June 21st (one and two harmonics—i.e., allowing up to two annual peaks), all interactions between the number of years since 1975 and each trigonometric function (but not between the trigonometric functions themselves).
  • Software: glmmTMB package [48] for R [76].
Seasonality:
  • Taxon: Ground-dwelling beetles.
  • Dependent variable: Total number of individuals per line of pitfall traps per date during the 2019–2021 research period, analyzed per species, for the 10 most common ground-dwelling beetle species.
  • Model type: Cosinor generalized linear mixed model (cosinor GLMM).
  • Predictors: Year as a factor (2019, 2020 or 2021), sine and cosine of the radian distance between the calendar date and June 21st (one and two harmonics—i.e., allowing up to two annual peaks), all interactions between the year and each trigonometric function (but not between the trigonometric functions themselves), the slope (north-facing, south-facing or valley bottom), (scaled) percentage of traps beneath the trees per trap line and the trap line number in the study site (1 to 7) as a random predictor.
  • Software: glmmTMB package [48] for R [76].
Species-specific abundance:
  • Taxon: Ground-dwelling beetles.
  • Dependent variable: Total number of individuals per species per line (1 to 7) and period combination. Species with fewer than 10 individuals overall were omitted to avoid spurious results.
  • Distribution: We used diagnostic residual plots to choose between Poisson and negative binomial distributions.
  • Model type: Generalized linear latent variable model (GLLVM)
  • Predictors: Period (1998–2000 vs. 2019–2021), the slope (north-facing, south-facing or valley bottom) and their interaction.
  • Software: gllvm package [53,54,79,80] for R [76].
Ordination by species-specific occurrence (presence–absence) or abundance:
  • Taxa: Plants (occurrence), ground-dwelling beetles (abundance), flying and ground-dwelling beetles combined (occurrence) and ants (occurrence).
  • Dependent variable: Presence–absence/total number of individuals per species per line (1 to 7) and period combination. When analyzing abundance, species with fewer than 10 individuals overall were omitted to avoid spurious results.
  • Distribution: For abundance—negative binomial. For presence–absence—binomial.
  • Model type: ecoCopula.
  • Predictors: None (unconstrained ordination).
  • Software: ecoCopula package [51] for R [76].

Appendix B. Extended Results

Appendix B.1. Temperature

We found a significant seasonal pattern: January 26th was predicted to be the coldest day, and August 18th was predicted to be the hottest day. In addition, we found a slight yet significant (p < 0.001) increasing trend in temperature seasonality, as the chosen model predicted that the difference between the annual minimum and maximum daily temperatures in 1999 would be 14.1 °C (12.1 °C vs. 26.2 °C), compared with an equivalent predicted difference of 14.3 °C in 2021 (13.0 °C vs. 27.3 °C). That is, the model predicted that the summers are warming faster than the winters.
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Figure A2. Observed (circles) and predicted (black line) homogenized mean annual temperatures (°C) for 1975—2021 at the Eilon Meteorological Station, 3 km north of the Evolution Canyon II study site. The gray ribbon is the prediction’s 95% confidence interval.
Figure A2. Observed (circles) and predicted (black line) homogenized mean annual temperatures (°C) for 1975—2021 at the Eilon Meteorological Station, 3 km north of the Evolution Canyon II study site. The gray ribbon is the prediction’s 95% confidence interval.
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Appendix B.2. NDVI

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Figure A3. Change in the NDVI during 1984–2022. Points are spatial means of observed values in the study rows (three each from the south-facing slope (SFS) and north-facing slope (SFS) and one from the valley bottom (VB) for a total of seven per annum). Lines are model predictions. The colored ribbons are the 95% confidence intervals.
Figure A3. Change in the NDVI during 1984–2022. Points are spatial means of observed values in the study rows (three each from the south-facing slope (SFS) and north-facing slope (SFS) and one from the valley bottom (VB) for a total of seven per annum). Lines are model predictions. The colored ribbons are the 95% confidence intervals.
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Appendix B.3. ecoCopula Results of Net-Collected Beetle Species

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Figure A4. ecoCopula ordination of the beetle fauna collected by nets (analysis of presence–absence). Numbers represent rows within the research area.
Figure A4. ecoCopula ordination of the beetle fauna collected by nets (analysis of presence–absence). Numbers represent rows within the research area.
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Appendix B.4. Seasonal Patterns of Common Ground Beetles

Figure 8 depicts the seasonal patterns of Orthomus longior (most common species) and of Ocypus mus (third most common species). Here we present figures for the 2nd and the 4th to 10th most common species.
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Figure A5. Seasonal pattern of the second most common species, Dailognatha crenata, which peaks in summer (June–August). Numbers are line numbers.
Figure A5. Seasonal pattern of the second most common species, Dailognatha crenata, which peaks in summer (June–August). Numbers are line numbers.
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Figure A6. Seasonal pattern of the fourth most common species, Dendarus plicatulus, which peaks in summer (June–September). Numbers are line numbers.
Figure A6. Seasonal pattern of the fourth most common species, Dendarus plicatulus, which peaks in summer (June–September). Numbers are line numbers.
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Figure A7. The seasonal pattern of the fifth most common species, Tentyria herculeana, which peaks in late-spring/early summer (May–June). Numbers are line numbers.
Figure A7. The seasonal pattern of the fifth most common species, Tentyria herculeana, which peaks in late-spring/early summer (May–June). Numbers are line numbers.
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Figure A8. The seasonal pattern of the sixth most common species, Maladera syriaca, which peaks in late-spring/early summer (May–June). Numbers are line numbers.
Figure A8. The seasonal pattern of the sixth most common species, Maladera syriaca, which peaks in late-spring/early summer (May–June). Numbers are line numbers.
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Figure A9. The seasonal pattern of the seventh most common species, Opatrum libani, which peaks in winter (December–March). Numbers are line numbers.
Figure A9. The seasonal pattern of the seventh most common species, Opatrum libani, which peaks in winter (December–March). Numbers are line numbers.
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Figure A10. The seasonal pattern of the eighth most common species, Nimbus harpagonis, which peaks in winter (December–March). Numbers are line numbers.
Figure A10. The seasonal pattern of the eighth most common species, Nimbus harpagonis, which peaks in winter (December–March). Numbers are line numbers.
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Figure A11. The seasonal pattern of the ninth most common species, Calathus syriacus, which peaks in winter (November–December). Numbers are line numbers.
Figure A11. The seasonal pattern of the ninth most common species, Calathus syriacus, which peaks in winter (November–December). Numbers are line numbers.
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Figure A12. The seasonal pattern of the 10th most common species, Carabus sidonius, which peaks in winter (November–March). Numbers are line numbers.
Figure A12. The seasonal pattern of the 10th most common species, Carabus sidonius, which peaks in winter (November–March). Numbers are line numbers.
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Figure 1. Map of the Eastern Mediterranean region (left) and ECII within Israel (right).
Figure 1. Map of the Eastern Mediterranean region (left) and ECII within Israel (right).
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Figure 2. “Evolution Canyon” II microsite in Nahal Keziv, Western Upper Galilee, Israel. (A) This cross-section, as photographed in 1999 by M.F., shows the NFS on the left and is a mirror image for convenience of comparison to later figures. (B) Orthophoto of ECII (from the Israel official maps website govmap.il: https://www.govmap.gov.il/?c=204000,595000&z=0, accessed on 1 August 2022). Each of the seven sampling rows (1–7) is 100 m long (dashed lines represent the rows; points represent the western end of the rows). Higher solar radiation on the SFS causes xeric garrigue and grassland open-patch habitats versus mesic dense forest on the NFS and VB.
Figure 2. “Evolution Canyon” II microsite in Nahal Keziv, Western Upper Galilee, Israel. (A) This cross-section, as photographed in 1999 by M.F., shows the NFS on the left and is a mirror image for convenience of comparison to later figures. (B) Orthophoto of ECII (from the Israel official maps website govmap.il: https://www.govmap.gov.il/?c=204000,595000&z=0, accessed on 1 August 2022). Each of the seven sampling rows (1–7) is 100 m long (dashed lines represent the rows; points represent the western end of the rows). Higher solar radiation on the SFS causes xeric garrigue and grassland open-patch habitats versus mesic dense forest on the NFS and VB.
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Figure 3. Plant species richness recorded on the NFS, SFS and VB, divided by the global distribution (for details, see Appendix A.1).
Figure 3. Plant species richness recorded on the NFS, SFS and VB, divided by the global distribution (for details, see Appendix A.1).
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Figure 4. ecoCopula ordination of plant composition. Numbers represent rows within the research area.
Figure 4. ecoCopula ordination of plant composition. Numbers represent rows within the research area.
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Figure 5. Beetle species richness by general global distribution. Upper charts: Beetles collected in the pitfall traps. Lower charts: Beetles collected in the net collection. For definitions of “northern”, ”southern” and “unknown”, see Appendix A.1.
Figure 5. Beetle species richness by general global distribution. Upper charts: Beetles collected in the pitfall traps. Lower charts: Beetles collected in the net collection. For definitions of “northern”, ”southern” and “unknown”, see Appendix A.1.
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Figure 6. ecoCopula ordination of (A) beetle fauna collected by pitfall traps and the net (analysis of the presence–absence of all species) and (B) the beetle fauna collected by pitfall traps (the analysis of counts includes only species with at least 10 individuals collected). Numbers represent rows within the research area.
Figure 6. ecoCopula ordination of (A) beetle fauna collected by pitfall traps and the net (analysis of the presence–absence of all species) and (B) the beetle fauna collected by pitfall traps (the analysis of counts includes only species with at least 10 individuals collected). Numbers represent rows within the research area.
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Figure 7. Coefficients of ground-dwelling beetle species with a significant abundance difference between the SFS and NFS (the analysis of counts includes only species with at least 10 individuals collected). Positive coefficients indicate higher abundance on the SFS and vice versa. Colors indicate the species’ general distribution pattern.
Figure 7. Coefficients of ground-dwelling beetle species with a significant abundance difference between the SFS and NFS (the analysis of counts includes only species with at least 10 individuals collected). Positive coefficients indicate higher abundance on the SFS and vice versa. Colors indicate the species’ general distribution pattern.
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Figure 8. Observed (circles), predicted (line) and 95% confidence intervals (shaded areas) of (A) Orthomus longior and (B) Ocypus mus counts per pitfall trap line. These are the first and third most common pitfall beetle species, respectively. These and four other species out of the 10 most common beetle species peak during the winter. Numbers are line numbers.
Figure 8. Observed (circles), predicted (line) and 95% confidence intervals (shaded areas) of (A) Orthomus longior and (B) Ocypus mus counts per pitfall trap line. These are the first and third most common pitfall beetle species, respectively. These and four other species out of the 10 most common beetle species peak during the winter. Numbers are line numbers.
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Figure 9. Ant species richness by slope and period.
Figure 9. Ant species richness by slope and period.
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Finkel, M.; Friedman, A.L.L.; Leschner, H.; Cohen, B.; Inbar, H.; Gelbert, S.; Rozen, A.; Barak, E.; Livne, I.; Renan, I.; et al. Assessing Natural Variation as a Baseline for Biodiversity Monitoring: The Case of an East Mediterranean Canyon. Ecologies 2025, 6, 24. https://doi.org/10.3390/ecologies6010024

AMA Style

Finkel M, Friedman ALL, Leschner H, Cohen B, Inbar H, Gelbert S, Rozen A, Barak E, Livne I, Renan I, et al. Assessing Natural Variation as a Baseline for Biodiversity Monitoring: The Case of an East Mediterranean Canyon. Ecologies. 2025; 6(1):24. https://doi.org/10.3390/ecologies6010024

Chicago/Turabian Style

Finkel, Meir, Ariel Leib Leonid Friedman, Hagar Leschner, Ben Cohen, Hoshen Inbar, Shai Gelbert, Agam Rozen, Eitan Barak, Ido Livne, Ittai Renan, and et al. 2025. "Assessing Natural Variation as a Baseline for Biodiversity Monitoring: The Case of an East Mediterranean Canyon" Ecologies 6, no. 1: 24. https://doi.org/10.3390/ecologies6010024

APA Style

Finkel, M., Friedman, A. L. L., Leschner, H., Cohen, B., Inbar, H., Gelbert, S., Rozen, A., Barak, E., Livne, I., Renan, I., Ben-Zvi, G., & Comay, O. (2025). Assessing Natural Variation as a Baseline for Biodiversity Monitoring: The Case of an East Mediterranean Canyon. Ecologies, 6(1), 24. https://doi.org/10.3390/ecologies6010024

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