The Niche-Center Relationship in Ecological Niche Modeling

The Niche-Center Relationship in Ecological Niche Modeling | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 22 December 2025 V2 Latest version Share on The Niche-Center Relationship in Ecological Niche Modeling Authors : Paanwaris Paansri 0000-0001-9992-098X , Huijie Qiao 0000-0002-5345-6234 , and Luis Escobar 0000-0001-5735-2750 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.176544375.53399389/v2 955 views 283 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract The niche-center relationship posits that organisms’ fitness is highest at the center of their fundamental ecological niche, an assumption often implicit in ecological niche modeling. Nevertheless, empirical support for the niche-center relationship at the species level is often inconsistent, potentially due to confounding factors such as biotic interactions, dispersal limitations, habitat degradation, and sampling biases. Confounding factors could obscure the signals of the niche-center relationship between abiotic conditions and population performance. To address these challenges, we present a supra-specific approach, hypothesizing that analyzing the niche-center relationship at a higher taxonomic level can better approximate the fundamental ecological niche of a lineage and reveal more robust macroecological patterns. We illustrate this approach using a case study on the North American rodent genus Peromyscus, leveraging a comprehensive dataset from the Global Biodiversity Information Facility and the National Ecological Observatory Network. We defined environmental space using principal component analysis of bioclimatic variables. We assessed fitness based on Peromyscus occupancy. The niche-center relationship was not supported by individual species; a strong and statistically significant negative relationship emerged at the genus level. Occupancy frequency was highest in environmental conditions closest to the genus’s niche centroid. This ecological niche supra-specific modeling framework effectively filters species-level realized niche noise to estimate better proxies of fundamental ecological niches of lineages, assess niche-center relationships, and better understand organisms’ response to global change, dispersal limitations, and evolution. Introduction The concept of the fundamental ecological niche, introduced by Grinnell (Grinnell 1917) and mathematized by Hutchinson (Hutchinson 1957), remains foundational in ecology and biogeography. Hutchinson, expanding ideas from Grinnell (Colwell and Rangel 2009, Soberón and Nakamura 2009), conceptualized the niche as an n -dimensional hypervolume encompassing the range of environmental conditions where a species can maintain a stable population without the need for immigration (Peterson et al. 2011). The Hutchinsonian ecological niche framework distinguished the fundamental niche N F from the realized niche N R , where N F is defined as abiotic tolerances and N R is a subset of N F constrained by biotic interactions (Peterson et al. 2011). N F is critical for understanding species’ physiological tolerances, offering a baseline to predict environmentally suitable geographic areas for an organism (Soberón and Peterson 2020). Soberón (2007) provided a formal definition of the Grinnellian N F as the subset of environmental space ( E ) for which the intrinsic, density-independent growth rate, thereby placing fitness, expressed as demographic viability, at the core of niche definition. In practice, however, fitness is rarely measured directly. Instead, proxies such as population abundance, occupancy, or genetic diversity are often used in empirical studies to infer fitness-related patterns along environmental gradients. Over time, N F approximations have become central to ecological niche modeling (ENM) in ecology and evolutionary biology (Qazi et al. 2022, Rathore and Sharma 2023, Doser et al. 2024). Despite their utility, ENM has faced criticism for oversimplifying species distributions, particularly by assuming that N F regions, the full range of environmental conditions, correspond directly to a species’ biological fitness. Recent studies have emphasized the need for a better understanding of N F structure, focusing on the context of its manifestation in E (Qiao et al. 2016, Qiao 2025) Hutchinson proposed to measure the structure of N F in units of fitness. The relationship between the core region of the N F (i.e., the niche center) and species fitness has been proposed to be non-linear (Osorio‐Olvera et al. 2020) with higher fitness expected closer to the niche center (Maguire, 1973). Niche-center relationship studies in E , however, have been largely found to be significant by some research groups (Yañez‐Arenas et al. 2012, Martínez-Meyer et al. 2013, Manthey et al. 2015, Martínez‐Gutiérrez et al. 2018, Osorio‐Olvera et al. 2020), but not others (Sagarin and Gaines 2002, Dallas et al. 2017, Santini et al. 2019, Dallas and Santini 2020) (Table 1). Such discrepancies have been largely mediated by flawed study designs and interpretations, which have obscured modern niche theory. Niche breadth and its estimation are critical to understanding a species’ ecological performance and adaptability (Carscadden et al. 2020). As such, traditional niche estimation has focused on species-level tolerances (Romero et al. 2014, Smith et al. 2019), neglecting information of sister taxa to determine if species close along the phylogenetic tree and with recent divergence retain the similar niche characteristics or structure (e.g., species in the same genus with comparable niche position, niche breath, and niche center). The niche-center relationship has been poorly addressed in the context of species with broad vs. narrow niche breadth, during short temporal spans, and by ignoring dispersal limitations (Smith et al. 2019, Osorio‐Olvera et al. 2020), which often generate misinterpretations of the effect of heterogeneous patterns of fitness across a species N F . The niche center is theoretically the host of optimal environmental conditions for a species, characterized by abiotic factors (e.g., optimum temperature or salinity) where a species could express its highest fitness. An erroneous interpretation of niche-center relationships, however, is to assume that the niche center has resource availability, absence of stressors, unlimited dispersal, presence of positive abiotic factors (e.g., prey for depredators or hosts for parasites), and absence of negative abiotic factors (e.g., pathogens, competitors). To overcome species divergence times, we propose that the biological principle of niche conservatism provides a powerful justification for testing the niche-center relationship at a supra-specific level. Niche conservatism is the tendency of lineages to retain ancestral ecological traits over evolutionary time (Wiens and Graham 2005). For the phylogenetic relationship, which has undergone relatively recent radiation across the spatially heterogeneous environments (Smith et al. 2019), its constituent species are expected to retain a significant degree of their ancestral physiological tolerances. Sharing an evolutionary history means that aggregating species occurrence data is not merely a statistical tool to create a broad, well-centered “aggregate niche”; it is a method to reconstruct the broader, conserved environmental tolerances of the entire lineage. The supra-specific level allows for a more complete approximation of N F than can be achieved from the truncated or constrained N R of individual species (Carscadden et al. 2020, Pennington et al. 2021). The reliance on species taxonomies, groupings of biological organisms based on their shared characteristics , within N F , may have influenced the misidentification of real niche centers. That is, incomplete taxonomic identification of taxa could generate N F estimates based on field data from a subset of the populations that may not truly resemble the abiotic range tolerable by a lineage. Broader taxonomic scales may help address incomplete N F estimations from field data. For example, using data from the genus, instead of the subspecies or species level, could give a biologically sound proxy of abiotic tolerances under the assumption of niche conservatism, which refers to the tendency of lineages to retain ancestral ecological traits (Wiens and Graham 2005). Niche conservatism shapes the dynamic interactions of phylogenetic lineages, influencing patterns of speciation, extinction, and community assembly (Wiens and Graham 2005, Peterson 2011). At higher taxonomic levels, such as genus, metrics like environmental tolerances remain underutilized despite their potential to reveal tolerances and adaptive capacities of lineages. Grouping species that share an evolutionary history offers an innovative way to expand the data availability for N F estimations and niche center identifications. Ecological niche modeling (ENM) from field data at high taxonomic levels is expected to capture physiological, ecological, and evolutionary signals of a clade. Genus-level ENM may reveal unoccupied portions of N F , which could be missed by using data from a lower taxonomical level, e.g., subspecies, underestimating the range of tolerance to biotic conditions under fluctuating environmental conditions or due to dispersal constraints derived from geographic barriers of biotic interactions, such as competitors (Fig 1).In the context of anthropogenic climate change, N F itself is relatively static in E and is not expected to evolve or shift rapidly (Levin 2005, Pearman et al. 2008, Chevalier et al. 2024). Instead, climate change alters the geographic landscape, causing the locations that match a species’ optimal conditions to decline. As this occurs, populations in those areas face decline and potential extirpation. The primary advantage of using the niche-center relationship, therefore, is its power to forecast which populations are most vulnerable to occurring in sites far from the environmental optimum. Analyzing the niche in E is a robust method for identifying the full range of conditions under which a species is expected to thrive. For many species, incomplete occurrence blocks our ability to confidently approximate the N F . Comprehensive overview of the niche-center relationship Initially, the niche-center relationship suggested that species perform best at the center of their N F , with fitness decreasing toward the periphery. Increasing evidence, however, indicates that field data generally fail to provide information to determine N F and, in turn, its center. Erroneous center estimations occur when models use biased field data largely influenced by anthropogenic factors (Dallas and Santini 2020). An important criticism of standard niche-center relationship studies is their excessive reliance on abundance-based measures as a proxy of fitness to determine optimal conditions for a species. Species with broad environmental tolerances, especially ecological generalists, may experience only moderate yet sufficient fitness across a wide range of conditions, making it difficult to reveal clear trends along distances to the niche center (Osorio‐Olvera et al. 2020). On the other hand, field data may be incapable of providing information to reveal the center of N F . Similarly, species abundance can be influenced by human activities (Wiens and Graham 2005), resulting in scenarios where humans provide subsidized resources unevenly for a species across distances to the niche center. Due to these data quality concerns, environmental gradient experiments would be more reliable to capture the abiotic tolerances of organisms when resources are constant. The role of niche conservatism further challenges traditional niche-center models. Organisms are not expected to evolve fast to adjust to changing environmental conditions (Wiens and Graham 2005). The complex evolutionary processes required for the delimitation of N F refute the idea that species evolve to relocate their niche center to track abiotic conditions (e.g., climate change). Instead, the position of a species’ ecological niche center is constrained by phylogenetic and evolutionary factors (Peterson 2011). Challenges in testing the niche-center relationship at the species level Beyond niche conservatism, source-sink population dynamics and dispersal asymmetries further complicate the robustness of demographic data for niche-center interpretations (Feng and Qiao 2022). For example, when populations in less favorable (marginal to the niche) conditions are periodically replenished by migrants from high-quality habitats, these sink populations can maintain substantial numbers even in suboptimal environmental conditions in a system referred to as a metapopulation (Dallas et al. 2017, Feng and Qiao 2022). Source-sink metapopulation dynamics distort the expected correlation between population abundance and distances to the environmental optimum, highlighting the need to incorporate long-term values of demography for more accurate signals of fitness of organisms along environmental gradients (Holt 2009, Wilson et al. 2016). Moreover, asymmetric dispersal patterns can hinder species from accessing their optimal environmental conditions at the center of N F , resulting in a disparity between predicted and observed fitness (Dallas and Santini 2020). In fragmented landscapes, species may not reach their niche centroid due to barriers that limit their movement, the absence of food resources, high densities of predators, or other biotic factors acting at a fine spatial scale (Drakou et al. 2009, Gido et al. 2015). While the niche-center relationship theory remains valuable for understanding species-environment interactions, its practical application requires critical revision and careful study design. Relying solely on species field occurrence reports to reconstruct N F and abundance data as a proxy of fitness oversimplifies the ecological responses of species to environmental gradients (Rathore and Sharma 2023). Future research should prioritize integrative modeling that considers phenotypic plasticity and evolutionary processes. The inconsistent nature of niche-center relationship findings at the species level is well-documented in the literature. A path forward: The supra-specific framework Our synthesis of recent niche-center relationship studies reveals that of 34 studies summarized, nearly half (14) offer support for the relationship between fitness and distance to the niche center, which uses Euclidean distance and Mahalanobis distance (Table 1). The remainder of the literature is inconclusive or non-supportive. This widespread inconsistency suggests that while abiotic factors provide a baseline, the realized performance of individual species is often overwhelmingly influenced by localized factors such as biotic interactions, dispersal limitations, and unique demographic histories, which obscure a clear signal. It is precisely this species-level variability that underscores the potential of analyzing the niche-center relationship at a higher taxonomic level. By aggregating data to the genus, we can buffer against the idiosyncratic noise of individual species’ N R to better approximate the conserved N F of a lineage. Furthermore, while many studies rely on population abundance, its reliability can be compromised by sampling biases inherent in large biodiversity datasets (Troia and McManamay 2016). To address this, alternative fitness metrics that complement temporally corrected metrics, such as the occupancy frequency index (equation 1), could provide less biased information about demography and are more robust to uneven sampling efforts. \(OFI_{i,G}=\frac{\left|M_{\det,\ i,G}\right|}{T_M}\)(equation 1) Where: • \(OFI_{i,G}\) is the occupancy frequency index for a specific grid cell, i , for the chosen taxonomic group, G . • \(G\) is the taxonomic group of interest. This is the flexible part of the equation. • \(M_{i,G}\) is the set of unique months in which any member of group G was detected in grid cell i . • \(\left|\ldots\right|\ \) denotes the cardinality, or the total count of unique months in that set. • \(T_M\) is the total number of months in the entire survey period across the whole dataset (i.e., max(month) - min(month) + 1). This value remains constant regardless of the group being analyzed. A key limitation of the occupancy frequency index is its inability to completely remove spatial sampling bias. While the index effectively normalizes for temporal variations in survey effort, it remains susceptible to geographic patterns of non-random sampling. Specifically, geographic areas with high accessibility or proximity to intensive research areas are often subject to disproportionately intense and repeated survey efforts. Consequently, in such well-surveyed locations, the occupancy frequency index may become artificially inflated. This potential for bias requires careful interpretation; however, the index remains a valuable tool for estimating occupancy performance across large geographic areas. Its advantage lies in providing a substantial measure of temporal persistence, which is less affected by short-term sampling issues that often distort the immediate abundance of data. Shifting efforts to model N F and estimate the niche center at higher taxonomic levels could facilitate a comprehensive analytical framework for understanding persistence and adaptation of lineage across environmental gradients derived from distance to the niche center (see case study in Box 1). Recognizing the complexity of niche structure may allow future models to move beyond static assumptions and create robust, evolutionary-grounded ecological theories that accurately reflect real-world species interactions and environmental changes. Conclusions This article advocates for supra-specific approaches for the study of the niche-center relationship in ecology. The niche-center relationship states that the populations near the center of the species’ N F exhibit higher fitness-related attributes. We illustrate how a genus-level ecological niche model provides a more robust reconstruction of the environmental conditions of the Peromyscus genus’s ecological niche, which predicts the areas of highest population persistence. The divergence between the genus-level and species-level results is particularly illuminating. The lack of support for the hypothesis in most individual species suggests that N R of a single species is shaped by more than just its abiotic tolerances. Factors such as interspecific competition, predation, and historical biogeographic events likely constrain species to occupy specific regions of the broader N F (Soberón 2007, Holt 2009), not all of which may be centered on their own abiotic optimum. For instance, a competitively inferior species might be relegated to a peripheral, less optimal portion of its N F by a dominant competitor, causing its highest abundance to be displaced from its own centroid. This process can be articulated in two steps: Species-level displacement creates: For any single species, its observed distribution represents its N R , which is often a small and constrained subset of its full N F . While theory predicts that a species should exhibit its highest fitness at the abiotic center of its N F , this is rarely observed in nature (Dallas et al. 2017, Santini et al. 2019). Localized biotic interactions act as influential confounding factors. These forces often push a species’ peak abundance far from its true physiological optimum. In essence, individual species often operate where ecological circumstances allow them to persist, rather than where they are abiotically most suited. Genus-level aggregation filters the noise to reveal N F : When we aggregate occurrence data to the genus level, the analysis acts as a statistical filter. This process of “evolutionary group averaging” smooths out the idiosyncratic, localized noise that displaces the niche centers of individual species. By filtering N R noise from the species-level ecological niche to reveal ancestral abiotic constraints ( N F ), the underlying, conserved physiological optimum shared across the lineage emerges (Wiens and Graham 2005, Smith et al. 2019). The fact that the niche-center relationship becomes so strong at the genus level confirms that once these localized interferences are statistically removed, the shared, ancestral abiotic tolerance of the entire lineage is the primary determinant of where peak performance occurs. The robustness of this finding, using a temporally corrected occupancy frequency metric, strengthens the theory that the center of N F represents an optimal E where fitness is expected to be maximized. 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Table Overview of studies evaluating the relationship between distance to the niche centroid and fitness-related proxies. Studies are categorized by niche type, centroid estimation method, and biological taxon, with key findings summarizing the direction and strength of the niche-center relationship. Summary of classifications: 14 supportive, 10 mixed, and 10 non-supportive. *Abbreviations: ED=Euclidean Distance; MD=Mahalanobis Distance; MVE=Minimum-Volume Ellipsoid; CH=Convex Hull; KDE=Kernell Density Estimation; SR=Species’ range; E=Environmental-Space; G=Geographic-Space; NCR=The Niche-Center Relationship. Sagarin and Gaines (2002) N F ED (G) Marine invertebrates & birds Global / Various Population abundance Mixed supporting NCR. Only 39% of 145 tests supported NCR; most studies had limited spatial coverage. Feldhamer et al. (2012) N R SR (G) Golden mice ( Ochrotomys nuttalli ) Southeastern United States Relative abundance, occupancy Not supporting NCR. Vaupel and Matthies (2012) N F ED (E) Alpine Thistle ( Carduus Defloratus ) Central Europe Population size, density, and seed production Support NCR. Yañez‐Arenas et al. (2012) N F ED (E) White-tailed deer ( Odocoileus virginianus ) Mexico Population abundance Support NCR. Dixon et al. (2013) N R SR (G) Four plant species Central Basin of Tennessee, USA Abundance, density, reproduction, and genetic diversity Mixed supporting NCR. Support the concept of species range limits. Martínez-Meyer et al. (2013) N F ED (E&G) Multiple taxa Western Hemisphere Population abundance Support NCR. Pironon et al. (2015) N F ED (E&G) Plants South Africa & UK Demographic rates, genetic diversity Not supporting NCR. No demographic pattern; positive historical niche-genetic diversity relationship. Aikens and Roach (2014) N R SR (G) Roan Mountain rattlesnake-root ( Prenanthes roanensis ) Southern Appalachians, USA Abundance, population growth rate Not supporting NCR due to demographic variation and local environmental differences. Lira-Noriega and Manthey (2014) N F ED (E&G) Plants Mexico Genetic diversity Support NCR. Manthey et al. (2015) N F ED (E&G) Birds North America Population abundance Support NCR. Dallas et al. (2017) N F CH and ED (E&G) Birds North America Population abundance Not supporting NCR. Weak geographic, moderate climatic correlation. Freeman and Beehler (2018) N R SR (G) 17 bird species Papua New Guinea Relative abundance Not supporting NCR. Martínez‐Gutiérrez et al. (2018) N R ED (E) Mammals (Peccary) The Americas Population abundance Support NCR. Santini et al. (2019) N R MD, ED (E&G) Birds, mammals Global Population abundance Not supporting NCR. Yañez et al. (2020) N F MVE and MD (E) Virtual species (Mussels) Virtual Species Population abundance Supports NCR when NF is fully characterized; violates it when M-region constraints apply. Osorio‐Olvera et al. (2020) N F CH and ED, MVE and MD (E) Birds North America Population abundance Support NCR. Dallas and Santini (2020) N F ED (E&G) Mammals North America Population abundance Not supporting NCR, influenced by stochasticity. Altamiranda-Saavedra et al. (2020) N F MVE and MD (E) Insects (Chagas vectors) Colombia Population abundance Support NCR. Ángeles-González et al. (2021) N F MVE and MD (E) Mollusca (Octopus) Mexico (Yucatan) Catch per unit effort Support NCR. Chevalier et al. (2021) N F CH and KDE (G) & CH and MVE (E&G) Multiple taxa. The Americas Abundance Mixed supporting NCR. 33% of species support an abundant-distance relationship, suggesting either low predictability of abundances from locations or a poor signal-to-noise ratio. Wen et al. (2021) N R SR (G) Five small mammal species Central and Southwest China Relative abundance Mixed supporting NCR Xu et al. (2021) N R SR (G) Chinese Emmenopterys ( Emmenopterys henryi ) Subtropical China Genetic diversity, differentiation, gene flow Mixed support NCR. The species’ core matches its highest habitat suitability and genetic diversity, but not differentiation, which is lower at the edges. Historically, migration from the core to the edge was higher than from the edge to the core, aligning with NCR. Ochoa-Zavala et al. (2022) N F MVE and MD (E) Birds North & Central America Genetic diversity Support NCR. Singhal et al. (2022) N R SR (G), ED (G), MVE and MD (E) 25 species-level taxa of Australian scincid lizards in the genera Ctenotus and Lerista Australia Genetic diversity Mixed supporting NCR, decreasing genetic diversity from the range edge to the core. Fristoe et al. (2023) N F ED (E&G) North American passerine birds North America Population abundance Support NCR. 92% of passerine species supported the abundant-core expectation in E. 87% of passerine species supported the abundant-core expectation in G. Fuica-Carrasco et al. (2023) N F MD (E) Plants Chile Metabolite diversity Support NCR. Higher metabolite diversity at the niche periphery. Moutouama and Gaoue (2023) N F CH and ED (G) & MVE and MD (E) Atacora bell ( Thunbergia atacorensis ) West Africa Vital rates, population growth rate. Not supporting NCR, but more resilient demographically to perturbation than peripheral ones. Martin et al. (2024) N R SR (G), an area-weighted mean centroid 109 North American bird species North America Relative abundance Mixed supporting NCR. 65% of species support NCR, but there is no support for NCR when points are near oceanic range edges, as most species peak away from their narrow range centers. Reyes‐Ortiz et al. (2024) N F MD (E) Plants (Montane Forest) Mexico Leaf functional traits Support NCR. Leaf traits variation increases away from the niche centroid. Antúnez and Ricker (2025) N R SR (G) 12 tree species Mexico Probability of occurrence Mixed supporting NCR. Léandri‐Breton et al. (2025) N F KDE (G) Black-legged kittiwake ( Rissa tridactyla ) North Atlantic Reproductive success, energy expenditure Not supporting NCR. Weak relationship and no pattern. Panter et al. (2025) N R SR (G) 3660 multiple taxa Global Population abundance Mixed support NCR. Hypothesis support varies by taxonomic group: more pronounced for plants, nonsignificant when summarized across all animals. Wang et al. (2025) N R SR (G), ED (G) Trees ( Liquidambar ) Subtropical China Population genetic diversity, differentiation Mixed support NCR. Population genetic diversity decreased, and differentiation increased from the center to the margin in L. acalycina , but not in L. formosana , likely reflecting different demographic histories and the contributions of geography. Zink et al. (2025) N R KDE (G), SR 64 freshwater fish species Continental US Abundance Not supporting NCR. The relationship is not well-suited for freshwater fishes. Box 1: The niche of Peromyscus and the niche-center relationship To illustrate the niche-center relationship, we conducted a case study on the North American rodent genus Peromyscus . Peromyscus was selected as a focal group due to its broad ecological adaptability and widespread occurrence across diverse environments, making it an ideal system for testing genus-level ENMs. Furthermore, frequent misclassification among Peromyscus species due to morphological similarities, particularly between P. maniculatus and P. leucopus (Perry et al. 1997). It presents an opportunity to assess whether genus-level reconstructions can mitigate such errors by generalizing environmental tolerances. Our analysis utilized a comprehensive dataset of 47 species, with occurrence records compiled from the Global Biodiversity Information Facility (GBIF; 12,448 records) and the National Ecological Observatory Network (NEON 2025) (64,303 records). To assess fitness, we calculated a robust proxy for persistence, the occupancy frequency index. This was defined as the proportion of total survey months in which a species was detected within a given geographic grid cell, leveraging the full extent of the combined GBIF and NEON datasets (Further details on the methodology are available in the Supporting information). Species-level niche dynamics Given that the relationship between the niche center and species fitness is expected to be non-linear (Osorio‐Olvera et al. 2020), we analyzed the data using a log-transformed regression of the occupancy frequency index against the distance to each species’ niche centroid (Fig IA, IB). This analysis yielded varied results across the Peromyscus genus. We detected a statistically significant negative relationship in only five species (26.3%): P. difficilis , P. eremicus , P. gossypinus , P. keeni , and P. maniculatus . For the majority of species, no significant relationship was found. These inconsistencies are likely attributable to noise and confounding factors, such as biotic interactions, which are known to obscure niche-center relationships (Wiens and Graham 2005, Smith et al. 2019). Genus-level niche dynamics In contrast to the variable patterns observed at the species level, the analysis of the Peromyscus N F provided strong support for the niche-center relationship at the genus level. The results demonstrated that the most persistent populations, those detected most consistently over time, are also located in environments near the center of the genus N F (Fig. IC). Conversely, as environmental conditions become more marginal (i.e., farther from the centroid), the frequency of occupancy declines. Fig. I The contrast between species-level “noise” and genus-level “signal” in the Peromyscus case study. (A) A representative species, P. leucopus , showing no significant relationship (p=0.328), a pattern observed in the majority (14 of 19) of analyzed species. (B) P. keeni , representing the minority of species (5 of 19, 26.3%) that exhibit a significant negative trend. (C) The genus-level analysis filters the species-level variability to reveal a strong, statistically significant negative relationship (p<0.001) for the Peromyscus genus (Results for all species in the Peromyscus genus and virtualizations are available in the Supplementary information Fig. 1 Conceptual illustration of genus-level fundamental niche and its relationship to species-specific distributions in E and G . (Left panel) E : The large green dash-shape represents the genus-level N F as a comprehensive representation of the environmental conditions that support the entire lineage. A black dot indicates the genus-level ecological niche centroid. A transparent black dot indicates the species’ individual niche centroid. Overlaid are N R of three hypothetical species, constructed using real-world data (Sp1, Sp2, Sp3, represented by smaller colored shapes), showing their distinct, often non-overlapping, and sometimes truncated positions within the broader genus ecological niche. The red gradient illustrates an observed abundance gradient, with peak abundance not necessarily aligning with the species’ individual niche centroid. (Right panel) G : The panel maps the niche concepts from E onto a geographic landscape. The black dot shows the geographic location corresponding to the genus-level ecological niche centroid. The colored dashed regions indicate the geographic distributions of the three species. The framework demonstrates how a genus-level N F , reconstructed from collective data, can encompass the diverse and often disjunct N R of individual species. Supplementary Material File (table_1.docx) Download 54.31 KB Information & Authors Information Version history V1 Version 1 11 December 2025 V2 Version 2 22 December 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords fundamental niche macroecology niche conservatism niche-center relationship supra-specific Authors Affiliations Paanwaris Paansri 0000-0001-9992-098X Virginia Tech View all articles by this author Huijie Qiao 0000-0002-5345-6234 Chinese Academy of Sciences View all articles by this author Luis Escobar 0000-0001-5735-2750 [email protected] Virginia Tech View all articles by this author Metrics & Citations Metrics Article Usage 955 views 283 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Paanwaris Paansri, Huijie Qiao, Luis Escobar. The Niche-Center Relationship in Ecological Niche Modeling. Authorea . 22 December 2025. DOI: https://doi.org/10.22541/au.176544375.53399389/v2 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. 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