Research Article |
Corresponding author: Noelline Tsafack ( noelline.tsafack@gmail.com ) Corresponding author: Xinpu Wang ( wangxinpu@nxu.edu.cn ) Corresponding author: Yingzhong Xie ( yzh.xie@icloud.com ) Corresponding author: Simone Fattorini ( simone.fattorini@univaq.it ) Academic editor: John Spence
© 2021 Noelline Tsafack, Xinpu Wang, Yingzhong Xie, Simone Fattorini.
This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Citation:
Tsafack N, Wang X, Xie Y, Fattorini S (2021) Niche overlap and species co-occurrence patterns in carabid communities of the northern Chinese steppes. In: Spence J, Casale A, Assmann T, Liebherr JК, Penev L (Eds) Systematic Zoology and Biodiversity Science: A tribute to Terry Erwin (1940-2020). ZooKeys 1044: 929-949. https://doi.org/10.3897/zookeys.1044.62478
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Understanding how species sort themselves into communities is essential to explain the mechanisms that maintain biodiversity. Important insights into potential mechanisms of coexistence may be obtained from observation of non-random patterns in community assembly. The spatial niche overlap (Pianka index) and co-occurrence (c-score) patterns in carabid species in three types of steppes (desert steppe, typical steppe, and meadow steppe) in China was investigated. Non randomness was tested using null models. Niche overlap values were significantly higher than expected by chance in the desert steppe, where vegetation cover is less abundant and less uniformly distributed, which possibly forces species to concentrate in certain places. In the typical and meadow steppes, results were influenced by the scale of the analysis. At a broad scale, niche separation was found as a result of species segregation among different sectors (habitats) within these steppes, but when the analysis was conducted at a finer scale, species appeared to be no more segregated than expected by chance. The high co-occurrence averages found in the meadow and typical steppes indicate that the distributions of the species found in a site may be negatively affected by the presence of other species, which suggests that some species tend to exclude (or reduce the abundance of) others. The very low c-score average observed in the desert steppe suggests that competition is not involved there. Thus, in more homogeneous landscapes (such as the typical and meadow steppes), competition might play some role in community structure, whereas spatial variation in the abundances of species is more driven by the uneven spatial distribution of vegetation in the landscape where productivity is lower and less uniformly distributed.
Carabidae, community organization, competition, co-occurrence, c-score, niche overlap, niche segregation, null models
The niche concept formalized by
Niche overlap describes the situation in which co-occurring species share parts of their niche space with each other. High niche overlap may lead to conflictual interactions (such as competition and exclusion) for some species (
Low niche overlap, which implies differential utilization of resources, is considered essential for the coexistence of syntopic species and hence to promote persistent diversity. In particular, interspecific overlap in habitat use has been invoked as a key factor in shaping ecological communities from the small scale (e.g., close-range interspecific interactions;
The role of interspecific competition in determining insect community organization and diversity is highly debated (
In this paper, we investigated niche overlap in carabid beetle communities in Chinese steppes using null model approaches to disclose potential mechanisms of co-existence. In Chinese steppes, carabids are among the most abundant ground dwelling insects (
Using intensive sampling by pitfall trapping in each type of steppe, we tested if patterns of species co-existence deviated from random as defined by using randomization processes that disrupt the original structure in the data according to more or less restrictive rules. For example, one can choose that places unoccupied by a given species in the field data are constrained to be also empty in the simulated null-assemblage, or one may adopt a more liberal approach, in which such sites may receive those species in the null-assemblages. Such null-assemblages generated by randomization represent what one can expect if no biological mechanisms regulate species coexistence. Significant deviation of observed patterns from assemblages predicted by the null-models provides evidence that some structure occurs and hence existence of some biological mechanism is implied.
We used null model approaches based on two different sets of rules to assess if spatial niche breadth and overlap patterns in the carabid communities of these steppes can be explained by chance alone, or if there is some spatial partitioning. Under the null-hypothesis that patterns are not structured by biological mechanisms (H0), i.e., if species abundances are randomly distributed within a given ecosystem, we expect no difference between observed and random values of niche overlap. This null-hypothesis can be falsified either by significantly higher (H1) or lower (H2) values of niche overlap compared to those obtained by random models. Niche overlap values higher than those expected under a null model in which species are allowed to occupy any of the places available, even those actually empty (but similar to those obtained for null-assemblages in which species were not allowed to occupy empty places), indicate that the presence of highly unsuitable places play a major role in community structure by forcing species to coexist. In contrast, niche overlap values lower than those expected under a null model in which the zero-structure is preserved (but similar to those obtained when the structure of zero values in the matrix is destroyed) are consistent with an inference that the community is structured by species segregation processes mediated more by species interactions than habitat heterogeneity. Based on the habitat structure of the three ecosystems, we predicted that H1 would be verified in the desert steppe, where resources are more fragmented (relatively isolated patches of vegetation which drive species coexistence), whereas H2 would be verified in the typical and meadow steppes (where vegetation is more uniformly distributed and hence niches are expected to be more influenced by species interactions).
The study was conducted in the Ningxia region (northern China), between 36°N and 38°N and between 105°E and 108°E, in three types of grassland ecosystems: desert, typical and meadow steppes (
We selected three main sampling areas representing these three types of steppes. To reflect within-ecosystem variability of the typical and meadow steppe habitats, on the basis of vegetation characteristics, we identified three habitat types (that we refer to as sectors 1, 2, and 3) within the typical steppe, and two habitat types (sectors 1 and 2) in meadow steppe. Data were gathered from 90 sampling sites distributed as follows: 15 sites in the desert steppe, 45 sites in the typical steppe (15 sites in each sector), and 30 sites in the meadow steppe (again 15 sites in each sector). In each sector, sites were selected haphazardly (i.e., without any regular spatial arrangement) and separated by at least 150 m to avoid, or at least reduce, possible autocorrelation.
At each sampling site, five pitfall traps (separated by at least five meters from each other) were installed. Pitfall traps consisted of 7.15 cm-diameter plastic cups, sunk in the ground with the cup-lip level with the soil surface, and filled with 60 ml of a mixture of tap water and vinegar (8%), sugar (4%), and 70% alcohol (4%). Sampling was done from May to September 2017. During the sampling period, pitfall traps were placed in the sites once a month in mid-month, and left in the field for 72 h prior to collection. Traps were composed of two buckets, with the smaller inserted into the larger. At each sampling session, the smallest were extracted to collect the trapped beetles and then paced again in the largest, which were left dug into the soil. This ensured that trap position remained exactly the same over the sampling period and disturbance reduced to minimum. Thus, we collected 25 samples (5 pitfall traps × 5 sampling dates) for each site, for a total of 2,250 samples. This spatially intensive sampling allowed us to obtain extremely detailed data about species distribution at fine scale. Further details about study area and data collection can be found in
We calculated the Pianka index (
where Ojk is Pianka’s index of niche overlap between species j and k, pij is the proportion of the ith resource used by species j, pik is the proportion of the ith resource used by species k, and n is the total number of resources. In our case, the resource is the habitat space (sampling sites), the use of which is assumed to be expressed by species abundances. The Ojk index is a symmetrical modification of the asymmetric index proposed by
Following widespread criticism of quantifying competitive interactions based on overlap indices (e.g.,
To assess if the mean values of niche overlap differed from those expected by chance, we compared the observed average values of spatial niche overlap (i.e., the averages of the niche overlap values between each species pair) with the expected averages obtained from simulated null-assemblages (pseudo-communities) constructed with two alterative sets of rules (see below). In each case, we calculated 10,000 null-assemblages, a number of permutations considered enough to avoid algorithm biases in calculations (
Null-assemblages were simulated using Monte Carlo randomization algorithms that assign resource use values (in our case, number of individuals from different sampling sites) to each species. The choice of an appropriate model to construct null-assemblages is a critical issue.
The RA3 algorithm relies on the equiprobability assumption that each species will use the resource in a site with the same probability or each site will have the same probability to host a species (in other words, differences between rows (species) or columns (sites) are not preserved). The RA3 algorithm tends to overestimate niche overlap if the equiprobability assumption is not met, because more abundant resources will be used by all species even if niche segregation occurs. Thus, for comparative purposes, we also used the RA2 algorithm, which tests for structure in the generalist-specialist nature of the resource utilization matrix by conserving guild structure (zero states are retained, thus preventing species that did not use a certain resource in the field from doing so in simulations), but relaxes niche breadth (i.e., it allows niche breadth to vary, thus assuming a random equiprobable specialization) (
We also calculated species segregation by using the C-score (
Cjk=(Rj–SS)(Rk–SS)
where Rj is the row total for species j, Rk is the row total for species k, and SS is the number of samples that contain both j and k. Thus, for any particular species pair, the C-score is a numerical index that ranges from a minimum of 0 (maximally aggregated) to a maximum of RjRk (maximally segregated with no shared samples). The matrix-wide C-score is an average of all the pairwise values of C-score for different species, so it reflects both positively and negatively associated species pairs. To establish whether the matrix had an average C-score significantly different from what can be expected from a null model, we compared the observed values with the expected averages obtained from 10,000 simulated null-assemblages using the fixed row-fixed column (FF) algorithm, which preserved row and column totals (
While niche overlap analysis uses a data matrix with species- and site-specific observed abundances, the C-score searches for non-random structure in the species assemblage data by using a presence/absence data matrix (
We collected a total of 25 species of carabid beetles (Table
Species distribution of carabid beetles in three types of Central Asian steppes. Number of sites (N. sites) in which each species was found and number of collected individuals (N. ind.) are also given.
Desert steppe | Typical steppe 1 | Typical steppe 2 | Typical steppe 3 | Meadow steppe 1 | Meadow steppe 2 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
N. sites | N. ind. | N. sites | N. ind. | N. sites | N. ind. | N. sites | N. ind. | N. sites | N. ind. | N. sites | N. ind. | |
Amara dux Tschitscherine, 1894 | 3 | 6 | 8 | 11 | 6 | 10 | 9 | 31 | 0 | 0 | 6 | 9 |
Amara harpaloides Dejean, 1828 | 1 | 7 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | 0 | 1 | 1 |
Amara helva Tschitscherine, 1898 | 3 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Amara sp. | 0 | 0 | 2 | 3 | 4 | 6 | 0 | 0 | 1 | 1 | 4 | 5 |
Broscus kozlovi Kryzhanovskij, 1995 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 1 | 1 | 3 | 5 |
Calosoma anthrax Semenov, 1900 | 0 | 0 | 9 | 17 | 7 | 11 | 5 | 6 | 0 | 0 | 5 | 7 |
Calosoma chinense Kirby, 1819 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |
Calosoma lugens Chaudoir, 1869 | 0 | 0 | 4 | 6 | 2 | 3 | 0 | 0 | 0 | 0 | 2 | 2 |
Carabus anchocephalus Reitter, 1896 | 0 | 0 | 1 | 1 | 3 | 3 | 11 | 25 | 0 | 0 | 14 | 56 |
Carabus crassesculptus Kraatz, 1881 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 295 | 11 | 44 |
Carabus gigoloides Cavazzuti, 2000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 266 | 1 | 1 |
Carabus glyptoterus Fischer Von Waldheim, 1827 | 15 | 252 | 14 | 66 | 14 | 194 | 15 | 327 | 0 | 0 | 14 | 47 |
Carabus modestulus Semenov, 1887 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 15 | 82 | 1 | 2 |
Carabus sculptipennis Chaudoir, 1877 | 0 | 0 | 15 | 247 | 14 | 140 | 6 | 14 | 2 | 2 | 1 | 1 |
Carabus vladimirskyi Dejean, 1830 | 1 | 3 | 15 | 560 | 15 | 639 | 6 | 13 | 15 | 808 | 10 | 16 |
Corsyra fusula (Fischer Von Waldheim, 1820) | 1 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Cymindis binotata Fischer Von Waldheim, 1820 | 7 | 19 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Dolichus halensis (Schaller, 1783) | 0 | 0 | 0 | 0 | 1 | 2 | 1 | 1 | 0 | 0 | 0 | 0 |
Harpalus lumbaris Mannerheim, 1825 | 4 | 11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Poecilus fortipes (Chaudoir, 1850) | 0 | 0 | 15 | 227 | 14 | 103 | 6 | 8 | 13 | 120 | 15 | 94 |
Poecilus gebleri (Dejean, 1828) | 0 | 0 | 15 | 391 | 15 | 608 | 15 | 135 | 2 | 2 | 7 | 9 |
Pseudotaphoxenus mongolicus (Jedlicka, 1953) | 11 | 23 | 3 | 4 | 10 | 39 | 4 | 11 | 0 | 0 | 0 | 0 |
Pseudotaphoxenus rugupennis (Faldermann, 1836) | 3 | 3 | 15 | 85 | 15 | 133 | 15 | 39 | 6 | 11 | 12 | 39 |
Reflexisphodrus reflexipennis (Semenov, 1889) | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | 14 | 332 | 10 | 34 |
Zabrus potanini Semenov, 1889 | 1 | 1 | 10 | 25 | 11 | 54 | 0 | 0 | 7 | 16 | 10 | 20 |
Overall mean pairwise niche overlap was 24% in the desert steppe, 25% in the typical steppe and 21% in the meadow steppe when these ecosystems were considered as a whole (Table
Histograms of expected values (blue bars) for niche overlap in carabid beetle communities of Central Asian steppes using the RA3 algorithm to generate 10,000 null matrices. Investigated ecosystems were a desert steppe (a), a typical steppe (b), a meadow steppe (c), three sectors within the typical steppe (d–f), and two sectors within the meadow steppe (g, h). In each graph, the vertical red line indicates the observed value, long-dash lines indicate the one-tailed 95% limits, and the short-dash lines the two-tailed 95% limits.
Results of null-model species-niche overlap for desert, typical, and meadow steppes: Pianka index estimates and variances of observed values and expected values. Expected values are obtained with RA3 and RA2 algorithms. Significant values are in bold.
Steppe | Observed | Expected (RA3) (10,000 iterations) | Expected (RA2) (10,000 iterations) | |||||||
---|---|---|---|---|---|---|---|---|---|---|
estimate | variance | estimate | variance | Lower-tail | Upper-tail | estimate | variance | Lower-tail | Upper-tail | |
P = (Obs < exp) | P = (Obs > exp) | P = (Obs < exp) | P = (Obs > exp) | |||||||
Desert steppe | 0.24 | 0.081 | 0.17 | <0.001 | 0.9 | <0.05 | 0.22 | <0.001 | 0.8 | 0.2 |
Typical steppe | 0.25 | 0.048 | 0.23 | <0.001 | 0.9 | <0.05 | 0.27 | <0.001 | <0.01 | 0.9 |
Meadow steppe | 0.21 | 0.046 | 0.21 | <0.001 | 0.6 | 0.4 | 0.26 | <0.001 | <0.001 | 1.0 |
Typical steppe 1 | 0.37 | 0.080 | 0.35 | <0.001 | 0.9 | 0.1 | 0.35 | <0.001 | 0.9 | 0.1 |
Typical steppe 2 | 0.41 | 0.058 | 0.40 | <0.001 | 0.8 | 0.1 | 0.41 | <0.001 | 0.5 | 0.5 |
Typical steppe 3 | 0.36 | 0.051 | 0.35 | <0.001 | 0.7 | 0.3 | 0.39 | <0.001 | 0.1 | 0.9 |
Meadow steppe 1 | 0.28 | 0.055 | 0.30 | <0.001 | 0.2 | 0.8 | 0.38 | <0.001 | <0.00001 | 1.0 |
Meadow steppe 2 | 0.30 | 0.063 | 0.29 | <0.001 | 0.7 | 0.3 | 0.28 | <0.001 | 0.9 | 0.8 |
Mean overall niche overlap in the typical steppe was also significantly higher than in the simulated assemblages constructed using the RA3 randomization algorithm (P = 0.02; Table
Histograms of expected values (blue bars) for niche overlap in carabid beetle communities of Central Asian steppes using the RA2 algorithm to generate 10,000 null matrices. Investigated ecosystems were a desert steppe (a), a typical steppe (b), a meadow steppe (c), three sectors within the typical steppe (d–f), and two sectors within the meadow steppe (g, h). In each graph, the vertical red line indicates the observed value, long-dash lines indicate the one-tailed 95% limits, and the short-dash lines the two-tailed 95% limits.
The mean niche overlap in the meadow steppe was significantly lower than expected under RA2 (P < 0.001), but not under RA3 (P = 0.444) (Table
Expected C-scores were very large in the typical and meadow steppe; however, they were low with high variance in the desert steppe (Table
Histograms of expected values (blue bars) for species co-occurrence (c-scores) in carabid beetle communities of Central Asian steppes using the fixed-fixed algorithm to generate 10,000 null matrices. Investigated ecosystems were a desert steppe (a), a typical steppe (b), a meadow steppe (c), three sectors within the typical steppe (d–f), and two sectors within the meadow steppe (g, h). In each graph, the vertical red line indicates the observed value, long-dash lines indicate the one-tailed 95% limits, and the short-dash lines the two-tailed 95% limits.
Results of null-model co-cooccurrence species for desert, typical, and meadow steppes: c-score estimates and variances of observed values and expected values. Expected values are obtained with Sim9 (fixed-fixed) algorithm. Significant values are in bold.
Steppe | Observed | expected (Sim9 (Row Sums = Fixed; Col Sums = Fixed)) (10,000 iterations) | ||||
---|---|---|---|---|---|---|
estimate | variance | estimate | variance | Lower-tail | Upper-tail | |
P = (Obs < exp) | P = (Obs > exp) | |||||
Desert steppe | 2.40 | 11.596 | 2.32 | 2.318 | 0.8 | 0.3 |
Typical steppe | 27.07 | 2066.180 | 26.10 | 0.119 | 0.9 | <0.01 |
Meadow steppe | 25.24 | 1247.348 | 22.34 | 0.059 | 1.0 | <0.0001 |
Typical steppe 1 | 1.67 | 14.980 | 1.65 | 0.005 | 0.6 | 0.4 |
Typical steppe 2 | 3.47 | 28.867 | 3.20 | 0.013 | 0.9 | <0.05 |
Typical steppe 3 | 5.56 | 59.436 | 5.46 | 0.013 | 0.8 | 0.2 |
Meadow steppe 1 | 1.27 | 8.448 | 4.56 | 0.012 | 0.1 | 0.9 |
Meadow steppe 2 | 4.43 | 32.235 | 1.44 | 0.017 | 0.1 | 0.9 |
The role of competition in carabid beetles is debated. Interspecific competition in carabid beetle communities was reviewed in detail by
Laboratory studies have produced contrasting results. For example, laboratory research showed that mortality rates observed when two species of Pterostichus were reared together did not differ from those observed when they were reared separately (
Field studies have produced contradictory results. Field research performed by
It has been suggested that carabid species with overlapping trophic niches can co-occur if they have different circadian rhythms. For example, Notiophilus biguttatus (Fabricius, 1779) and Nebria brevicollis (Fabricius, 1792) not only have their activity peaks in different seasons (in spring and in autumn, respectively), but also show different circadian patterns, being diurnal and nocturnal, respectively (
Based on the high frequency of co-occurrence of congeneric species, some authors (
Niche complementarities (i.e., niche differences between species) have long been considered to be important key drivers of species coexistence (e.g.
The RA3 algorithm, which is based on the assumption of retaining niche breadth, revealed that the spatial niches of carabid species overlapped significantly and more than expected in both the desert and the typical steppe. These results were obtained when niche breadths were preserved but species were allowed to be modelled as present in sites where they were not found in the observed data set. This indicates that some places are unsuitable for particular species, and that existence of such places likely forces spatial niche overlap in places that are suitable. This result is consistent with the idea that in these ecosystems resources are scarce and structured spatially, so that unsuitable parts of the environment (here, for example, sandy areas without vegetation) may restrict species from finding areas of suitable habitat. This result seems to reflect that mobility of carabids is strongly constrained in desert steppe, as we found in a previous paper (
Our analysis was strictly about spatial distribution, and we did not consider explicitly the distribution of vegetation or productivity. However, the desert steppe supports low vegetative productivity that is not uniformly distributed, so we think that spatial segregation patterns likely reflect uneven resource distribution. Thus, that the species aggregation observed in our data is driven by beetle concentration in places where resources are more abundant. This is in agreement with
The carabids of the desert community also showed the highest variance in niche values, i.e., some species pairs show high niche overlap and others show low niche overlap. Thus, we argue that spatial resource partitioning is due to the presence of sites that are favorable to certain species but not to others, which supports our prediction H1. The assumption of resource partitioning can also be supported by the high variance observed in carabid functional diversity in desert steppe (
The carabid community of the meadow steppe did not deviate significantly from randomness under the RA3 assumptions, which means that the environment is used in a more uniform way, with no or few spaces unavailable to carabids. This is consistent with the greater landscape homogeneity of this ecosystem as postulated by our prediction H2. A previous study showed that carabid activity-density in the meadow steppe was significantly correlated with vegetation indices at a relatively large scale (buffers zones of 1450 m and 1500 m diameter), thus suggesting that carabid species in this ecosystem are more mobile and can exploit larger areas of suitable habitat (
Modelled with the RA2 algorithm (which retains the zero-structure of the matrix, but allows niche breadths to vary), niche overlaps in the beetle assemblage of the desert steppe did not vary significantly from randomness. This indicates that some places are unavailable to some species, but there are no other constraints on space utilization. Although the carabids of this ecosystem divide the shared spatial resource in a random way, some places are apparently unsuitable for particular species. For data from the typical steppe, however, niche overlap values from the RA2 algorithm were lower than expected if determined by chance, suggesting that the environment is spatially structured. In other words, presence of zeroes in the matrix of habitat use are important, and if they are removed niche overlap will increase significantly. Thus, the unsuitability of some sites for particular species contributes to the pattern of spatial utilization among species. Similarly, in the meadow steppe, use of the RA2 algorithm leads to lower niche overlap than expected by chance; however, if the influence of zeroes is removed (RA3), niche overlap does not increase to become significantly higher than by chance. This suggests that places unavailable to particular species are less important to assemblage structure in the meadow steppe than in the other two ecosystems. This interpretation should be offered with caution however, because use of RA2 algorithm is prone to Type I error (false positive) (
If the three sectors of the typical steppe are analyzed separately, neither the RA2 nor RA3 approach revealed non-random community structure. This suggests that spatial structure in the overall analysis arose because the three sectors include different assemblages. This is not surprising as the abundance of even the most widely distributed species (C. vladimirskyi) varied widely in this ecosystem, being trapped commonly in two sectors, but rarely in the third. Thus, spatial niche separation likely results from low overlap in patterns of habitat use among these three assemblages more than spatial segregation within each sector. In other words, spatial segregation was greater between species found in different sectors than between species within the same sector. This is an interesting result showing that spatial niche overlap values should be interpreted with consideration of the scale of analysis. At a broad scale, niche separation appears to result from species segregation reflecting their different distributions, but when the analysis is conducted at a finer scale, species appeared to be no more segregated than expected by chance. These results are consistent with
The situation is paralleled in the meadow steppe in the second sector, but although overall niche overlap in the first sector was lower than expected. This suggests that the first sector has a high habitat heterogeneity, that facilitates species segregation. This is consistent with the strong correlations that we found between carabid activity density and vegetation indices only at a larger scale (buffers of 1450 m and 1500 m diameters;
Species that coexist in a certain area are expected to show lower overlap than a randomly assembled set of species (from the same area) if the community is structured by competition because of mutual exclusion. The significant overlap in spatial distribution found for the carabids of the desert and typical steppes using the RA3 algorithm thus suggests that there is little competition (
The high average C-scores found in the meadow and typical steppe suggests that the presence of a species is directly affected by the presence of other species, and hence that competition may have some role in defining species assemblages in these ecosystems, whereas the low value observed in the desert suggests, as above, that competition is not involved here.
Our analyses based on the use of null models constructed under different sets of assumptions reveal that spatial niche overlap values in carabid communities inhabiting Central Asian steppes reflect both habitat structure and species interactions, and that results are scale dependent. Niche overlaps were significantly higher than expected by chance alone in the desert steppe, where resources are highly fragmented, and therefore species tend to be aggregate and to share resources. In the typical and meadow steppes, at a broad scale, we found species segregation, but when the analysis was conducted at a finer scale, species appeared to be not more segregated than expected. This indicates that, in homogenous landscapes, species are not segregated by the habitats and tend to co-occur more randomly. However, high average co-occurrence found in the meadow and typical steppes indicates that the distribution of one species may be negatively affected by the presence of other species, and hence that competition may have some role in defining species assemblages in these ecosystems. By contrast, the very low co-occurrence value observed in the desert suggests that competition cannot be involved there. Thus, in homogeneous landscapes competition may play some role in community structure and biodiversity maintenance, whereas species assemblages are more driven by the spatial distribution of resources in a landscape where they are more fragmented.
Our approach was largely based on the detection of coexisting and not-coexisting species, and variation in their relative abundances. For this, it is essential that patterns of both co-occurrences and co-absences, as well as differences in species’ abundances, reflect true patterns in nature, and are not simply an artifact of inadequate sampling. Terry Erwin’s studies have emphasized the need for sampling procedures that are adequate to obtain robust measures of carabid abundance and diversity, and we think that this was particularly important in our case. For this reason, we are honored to offer this paper as a small tribute to Dr. Erwin’s memory.
We are grateful to Hongbin Liang (Institute of Zoology, CAS) for identifying specimens. We would like to express our gratitude to an anonymous reviewer and the editor J. R. Spence for their very useful comments on a previous version of this paper.