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Context: Spatial occupancy and local abundance of species often positively covary, but the mechanisms driving this widespread relationship are poorly understood. Resource dynamics and habitat changes have been suggested as potential drivers, but long-term studies relating them to abundance and occupancy are rare. In this 34-year study of acorn woodpeckers (Melanerpes formicivorus), a cooperatively breeding species, we observed a paradoxical response to changes in habitat composition: despite a reduction in the availability of high-quality breeding habitat, the population increased considerably. Objectives: We investigated the role of annual variation in food availability and long-term changes in habitat composition as predictors of population dynamics. Methods: Using model selection, we contrasted competing hypotheses on the effects of changing resource availability on occupancy and social group size across three spatial scales: territory, neighborhood, and landscape. Results: The increase in abundance was largely determined by the formation of new social groups, driven by a landscape-level expansion of canopy cover and its interaction with neighborhood-level acorn abundance, indicative of long-term increases in overall acorn productivity. Group size increased with neighborhood acorn crop two years earlier but groups were smaller in territories with more canopy cover. Conclusions: Our results indicate that scale-dependent processes can result in paradoxical relationships in systems with spatial and temporal resource heterogeneity. Moreover, the findings support the role of resources in driving changes in abundance and occupancy at a landscape scale, suggesting that colonization of marginal habitat drives the positive occupancy-abundance relationship in this cooperatively breeding species. [ABSTRACT FROM AUTHOR]

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Species distribution models are the tool of choice for large‐scale population monitoring, environmental association studies and predictions of range shifts under future environmental conditions. Available data and familiarity of the tools rather than the underlying population dynamics often dictate the choice of specific method – especially for the case of presence–absence data. Yet, for predictive purposes, the relationship between occupancy and abundance embodied in the models should reflect the actual population dynamics of the modelled species. To understand the relationship of occupancy and abundance in a heterogeneous landscape at the scale of local populations, we built a spatio‐temporal regression model of populations of the Glanville fritillary butterfly Melitaea cinxia in a Baltic Sea archipelago. Our data comprised nineteen years of habitat surveys and snapshot data of land use in the region. We used variance partitioning to quantify relative contributions of land use, habitat quality and metapopulation covariates. The model revealed a consistent and positive, but noisy relationship between average occupancy and mean abundance in local populations. Patterns of abundance were highly variable across years, with large uncorrelated random variation and strong local population stochasticity. In contrast, the spatio‐temporal random effect, habitat quality, population connectivity and patch size explained variation in occupancy, vindicating metapopulation theory as the basis for modelling occupancy patterns in fragmented landscapes. Previous abundance was an important predictor in the occupancy model, which points to a spillover of abundance into occupancy dynamics. While occupancy models can successfully model large‐scale population structure and average occupancy, extinction probability estimates for local populations derived from occupancy‐only models are overconfident, as extinction risk is dependent on actual, not average, abundance. [ABSTRACT FROM AUTHOR]

Niche conservatism (NC) describes the scenario in which species retain similar characteristics or traits over time and space, and thus has potentially important implications for understanding their biogeographic distributions. Evidence consistent with NC includes similar niche properties across geographically distant regions. We investigated whether NC was evident in stream diatom morphospecies by modeling species responses to environmental and climatic variables in a set of calibration sites (from the US) and then evaluated the models with test sets (from France, Finland, New Zealand, Antilles and La Réunion). We also examined whether diatom species showed congruency in environmental niche optima and niche breadths between the study regions, and whether species occupancy and functional traits influenced the observed patterns. We used boosted regression tree models with local environmental variables and climatic variables as predictors. We detected low NC in both environmental and climate models and a lack of consistent differences in NC between widely distributed and regionally rare species and among functional groups. For all species, diatom environmental and climatic optima varied clearly between the regions but showed some positive relationships especially for pH and total phosphorus. Diatom niche breadths were only weakly correlated between the US and the other regions. We demonstrated that diatoms showed overall relatively little NC globally, and NC was especially low for climatic variables. Collectively, these findings suggest that there may exist locally adapted lineages within the diatom morphospecies or diatoms possess some adaptation potential for differences in temperature. We argue that in diatoms, environmental and especially climate models may not be transferrable in space globally but need regional diatom data for calibration because species niches seem to differ among geographical regions. [ABSTRACT FROM AUTHOR]

Amazonia is an environmentally heterogeneous and biologically megadiverse region, and its biodiversity varies considerably over space. However, existing knowledge on Amazonian biodiversity and its environmental determinants stems almost exclusively from studies of macroscopic above‐ground organisms, notably vertebrates and trees. In contrast, diversity patterns of most other organisms remain elusive, although some of them, for instance microorganisms, constitute the overwhelming majority of taxa in any given location, both in terms of diversity and abundance. Here, we use DNA metabarcoding to estimate prokaryote and eukaryote diversity in environmental soil and litter samples from 39 survey plots in a longitudinal transect across Brazilian Amazonia using 16S and 18S gene sequences, respectively. We characterize richness and community composition based on operational taxonomic units (OTUs) and test their correlation with longitude and habitat. We find that prokaryote and eukaryote OTU richness and community composition differ significantly among localities and habitats, and that prokaryotes are more strongly structured by locality and habitat type than eukaryotes. Our results 1) provide a first large‐scale mapping of Amazonian soil biodiversity, suggesting that OTU richness patterns might follow substantially different patterns from those observed for macro‐organisms; and 2) indicate that locality and habitat factors interact in determining OTU richness patterns and community composition. This study shows the potential of DNA metabarcoding in unveiling Amazonia's outstanding diversity, despite the lack of complete reference sequence databases for the organisms sequenced. [ABSTRACT FROM AUTHOR]

Studies on elevation gradients in Panama and Costa Rica have shown that leaf‐litter ants exhibit a mid‐elevation peak in diversity. This diversity pattern has been observed in other groups and regions, but uncertainty remains as to just how pervasive it is and what might explain it. Here we examine the robustness of the mid‐elevation peak in ant diversity across the entire Middle American corridor, from Veracruz, Mexico, to Costa Rica. We sampled 56 sites distributed throughout Middle America. All were in closed‐canopy evergreen wet forest, spanning 11° latitude, from near sea level to 2600 m elevation. Ants were extracted from 100 litter samples from each site and identified to genus or species. Model selection was performed on richness and diversity variables to test if ant diversity best fits a linear model or one allowing for a mid‐elevation peak. Linear models were also used to examine the relationships among diversity measures and temperature, precipitation, and seasonality. Species richness measures and diversity indices that incorporate relative abundance show a similar relationship to elevation throughout the region: a truncated bell curve with a mode near 400 m. A cubic relationship is statistically favored over quadratic or linear. Temperature is a significant correlate with diversity, but does not predict a bell‐curve. Precipitation and precipitation seasonality fail to explain much of the variability, and no combination of environmental variables predicts a bell curve. Potential causes of the truncated bell curve include lowland biotic attrition, mid‐point attractors, and ecotonal transitions from lowland to montane communities. Analysis of 17 subclades within ants mostly showed the same truncated curve but six clades were anomalous. Distinctive behavioral or historical features potentially explain their patterns. [ABSTRACT FROM AUTHOR]

Understanding how environmental factors interact to determine the abundance and distribution of animals is a primary goal of ecology, and fundamental to the conservation of wildlife populations. Studies of these relationships, however, often assume static environmental conditions, and rarely consider effects of competition with ecologically similar species. In many parts of their shared ranges, grizzly bears Ursus arctos and American black bears U. americanus have nearly complete dietary overlap and share similar life history traits. We therefore tested the hypothesis that density patterns of both bear species would reflect seasonal variation in available resources, with areas of higher primary productivity supporting higher densities of both species. We also hypothesized that interspecific competition would influence seasonal density patterns. Specifically, we predicted that grizzly bear density would be locally reduced due to the ability of black bears to more efficiently exploit patchy food resources such as seasonally abundant fruits. To test our hypotheses, we used detections of 309 grizzly and 597 black bears from two independent genetic sampling methods in spatially‐explicit capture–recapture (SECR) models. Our results suggest grizzly bear density was lower in areas of high black bear density during spring and summer, although intraspecific densities were also important, particularly during the breeding season. Black bears had lower densities in areas of high grizzly bear density in spring; however, density of black bears in early and late summer was best explained by primary productivity. Our results are consistent with the hypothesis that smaller‐bodied, more abundant black bears may influence the density patterns of behaviorally‐dominant grizzly bears through exploitative competition. We also suggest that seasonal variation in resource availability be considered in efforts to relate environmental conditions to animal density. [ABSTRACT FROM AUTHOR]

Highlights • Salamander occupancy, richness, and abundance increased with increasing SMZ width. • Occupancy of uncommon species was highest at sites with wide SMZs (>55 m). • Richness was lowest in SMZs dominated by pine and surrounded by mid-aged stands. • Implementation of SMZs ≥55 m on each side of the stream should continue in key areas. • SMZs in intensively managed forests can support robust salamander communities. Abstract Streamside management zones (SMZs), buffers of forest retained along streams when adjacent areas are harvested, are a primary tool for maintaining ecological functions in managed forests. However, few studies have examined the influence of SMZs on salamanders, and none have examined variation in salamander occupancy, species richness, and abundance across a gradient of SMZ widths at a large spatial scale. Further, because previous studies had limited ability to detect responses of uncommon species, little is known about interspecific variation in salamander responses or whether SMZs serve as refuges for terrestrial species during harvesting. To evaluate the role of SMZs in maintaining salamander species, we conducted four replicate surveys at 102 headwater stream sites, spanning a gradient of SMZ widths and adjacent forest stand ages, within the Ouachita Mountains, Arkansas, USA. We used a hierarchical Bayesian community occupancy model to estimate salamander species richness and species-specific occupancy responses to SMZ and overstory characteristics, while accounting for variation in occupancy and detection attributable to site and sampling covariates. Additionally, we used Bayesian binomial mixture models to examine the influence of SMZ width and surrounding stand age on abundance of two stream-associated salamanders [Ouachita dusky salamander (Desmognathus brimleyorum) and many-ribbed salamander (Eurycea multiplicata)] and three terrestrial salamanders [red-backed salamander (Plethodon serratus), slimy salamander (P. glutinosus complex), and Caddo Mountain salamander (P. caddoensis)]. Occupancy, species richness, and abundance increased with increasing SMZ width, but the strength of responses varied among species. Occupancy of uncommon species was highest at sites with wide SMZs, likely because of the need for specific habitat elements (e.g., wetlands, rocky talus). Richness was lowest in SMZs dominated by pine overstory, and lowest in mid-aged stands, consistent with a lagged decrease in richness within SMZs following harvest. Finally, we documented patterns of abundance consistent with the evacuation hypothesis, indicating that salamanders may leave recently harvested areas seeking more suitable habitat and microclimate within SMZs. Collectively, results indicate that SMZs in intensively managed forests can support robust salamander communities, provided managers continue to retain wide SMZs at some key sites across the landscape (i.e., ≥55 m on each side of the stream), a value larger than current Forestry BMP guidelines. [ABSTRACT FROM AUTHOR]

The degree to which species ecological and biological traits determine their distribution and abundance has intrigued ecologists for a long time, and it has seen a revival in recent years. This topic is important because it provides information about the determinants of species rarity and their conservation implications. We examined the effects of niche breadth, niche position, biological traits and taxonomic relatedness on the interspecific occupancy–abundance relationship, as well as on occupancy and abundance, in lake littoral macroinvertebrates. We sampled 48 lakes in a boreal lake district, found altogether 155 species, and calculated regional occupancy (as the proportion of sites occupied) and local abundance (as mean abundance at occupied sites) for each species. We determined niche position and niche breadth for each species using the outlying mean index analysis. Also, we calculated trait vectors and taxonomic vectors describing species trait similarity and taxonomic relatedness, respectively, using principal coordinates analysis. We found a strong positive occupancy–abundance relationship that was mostly explained by among‐species variation in niche position, followed by niche breadth. Instead, trait vectors and taxonomic vectors tended to be less important in affecting occupancy and abundance than the niche features. Our results strongly suggest that niche position, a measure of habitat availability for littoral macroinvertebrates, is the chief determinant of their occupancy and abundance. This finding has important implications for ecology and conservation of species, as species with marginal niche position, a reflection of low habitat availability, are both regionally rare and locally uncommon. Such species may face double jeopardy if environmental conditions change and affect their preferred marginal habitat types. [ABSTRACT FROM AUTHOR]

Environmental DNA (eDNA) has improved detection probabilities of aquatic invasive species but lab-based analytical platforms for eDNA analyses slow opportunities for rapid response. Effective approaches that address this analytical bottleneck and improve capacity for rapid response are urgently needed. We tested the sensitivity of a portable, field-based eDNA platform relative to widely used lab-based eDNA approaches for detecting invasive northern pike (Esox lucius) in eight lakes on Alaska's Kenai Peninsula. The portable, field-based platform takes ~ 1 hr from sample collection to final results and uses a field-based DNA extraction kit, a shelf-stable assay, and a portable real-time PCR thermocycler. Lab-based approaches take days to weeks to months for final results and use lab-based DNA extraction kits, lab-bound assays, and benchtop real-time thermocyclers. We found that the portable, field-based approach was less sensitive than lab-based approaches and was more prone to inhibition, thus increasing potential for false-negatives. Until sensitivity and inhibition issues can be resolved, this portable, field-based approach is best viewed as a complement to rather than a replacement of standard eDNA lab-based approaches. [ABSTRACT FROM AUTHOR]

Biological systems provide examples of differential success among taxa, from ecosystems with a few dominant species (ecological success) to clades that possess far more species than sister clades (macroevolutionary success). Macroecological success, the occupation by a species or clade of an unusually high number of areas, has received less attention. If macroecological success reflects heritable traits, then successful species should be related. Genera composed of species possessing those traits should occupy more areas than genera with comparable species richness that lack such traits. Alternatively, if macroecological success reflects autapomorphic traits, then generic occupancy should be a by-product of species richness among genera and occupancy of constituent species. We test this using Phanerozoic marine invertebrates. Although temporal patterns of species and generic occupancy are strongly correlated, inequality in generic occupancy typically is greater than expected. Genus-level patterns cannot be explained solely with species-level patterns. Within individual intervals, deviations between the observed and expected generic occupancy correlate with the number of lithological units (stratigraphic formations), particularly after con- trolling for geographic range and species richness. However, elevated generic occupancy is unrelated to or negatively associated with either generic geographic ranges or within-genus species richness. Our results suggest that shared traits among congeneric species encourage short-term macroecological success without generating short-term macro-evolutionary success. A broad niche may confer high occupancy but does not necessarily promote speciation. [ABSTRACT FROM AUTHOR]

Abstract: A frequent assumption in ecology is that biotic interactions are more important than abiotic factors in determining lower elevational range limits (i.e., the “warm edge” of a species distribution). However, for species with narrow environmental tolerances, theory suggests the presence of a strong environmental gradient can lead to persistence, even in the presence of competition. The relative importance of biotic and abiotic factors is rarely considered together, although understanding when one exerts a dominant influence on controlling range limits may be crucial to predicting extinction risk under future climate conditions. We sampled multiple transects spanning the elevational range limit of Plethodon shenandoah and site and climate covariates were recorded. A two‐species conditional occupancy model, accommodating heterogeneity in detection probability, was used to relate variation in occupancy with environmental and habitat conditions. Regional climate data were combined with datalogger observations to estimate the cloud base heights and to project future climate change impacts on cloud elevations across the survey area. By simultaneously accounting for species’ interactions and habitat variables, we find that elevation, not competition, is strongly correlated with the lower elevation range boundary, which had been presumed to be restricted mainly as a result of competitive interactions with a congener. Because the lower elevational range limit is sensitive to climate variables, projected climate change across its high‐elevation habitats will directly affect the species’ distribution. Testing assumptions of factors that set species range limits should use models which accommodate detection biases. [ABSTRACT FROM AUTHOR]

Abstract: Dispersal distances are commonly inferred from occupancy data but have rarely been validated. Estimating dispersal from occupancy data is further complicated by imperfect detection and the presence of unsurveyed patches. We compared dispersal distances inferred from seven years of occupancy data for 212 wetlands in a metapopulation of the secretive and threatened California black rail (Laterallus jamaicensis coturniculus) to distances between parent‐offspring dyads identified with 16 microsatellites. We used a novel autoregressive multi‐season occupancy model that accounted for both unsurveyed patches and imperfect detection to quantify patch isolation using buffer radius (BRM) and incidence function (IFM) connectivity measures at 15 scales (1–10, 15, 20, 25, and 30 km). Connectivity measures were then fit as colonization covariates in occupancy models to estimate a model‐averaged dispersal distance. As predicted, colonization was more strongly related to connectivity at small spatial scales (<10 km). AIC weights were greatest at 7 km for BRM and at 4 km for IFM. Model‐averaged dispersal distances (BRM = 7.46 km; IFM = 5.48 km) showed good agreement with the mean M(±SE) dispersal distance from 23 parent‐offspring dyads (5.58 ± 1.92 km), indicating reasonably accurate mean dispersal distances can be inferred from occupancy data when isolation strongly affects colonization. [ABSTRACT FROM AUTHOR]

Abstract: A species’ distribution and abundance are determined by abiotic conditions and biotic interactions with other species in the community. Most species distribution models correlate the occurrence of a single species with environmental variables only, and leave out biotic interactions. To test the importance of biotic interactions on occurrence and abundance, we compared a multivariate spatiotemporal model of the joint abundance of two invasive insects that share a host plant, hemlock woolly adelgid (HWA; Adelges tsugae) and elongate hemlock scale (EHS; Fiorina externa), to independent models that do not account for dependence among co‐occurring species. The joint model revealed that HWA responded more strongly to abiotic conditions than EHS. Additionally, HWA appeared to predispose stands to subsequent increase of EHS, but HWA abundance was not strongly dependent on EHS abundance. This study demonstrates how incorporating spatial and temporal dependence into a species distribution model can reveal the dependence of a species’ abundance on other species in the community. Accounting for dependence among co‐occurring species with a joint distribution model can also improve estimation of the abiotic niche for species affected by interspecific interactions. [ABSTRACT FROM AUTHOR]

Abstract: The Lower Keys marsh rabbit (Sylvilagus palustris hefneri) is one of many endangered endemic species of the Florida Keys. The main threats are habitat loss and fragmentation from sea‐level rise, development, and habitat succession. Exotic predators such as free‐ranging domestic cats (Felis catus) pose an additional threat to these endangered small mammals. Management strategies have focused on habitat restoration and exotic predator control. However, the effectiveness of predator removal and the effects of anthropogenic habitat modifications and restoration have not been evaluated. Between 2013 and 2015, we used camera traps to survey marsh rabbits and free‐ranging cats at 84 sites in the National Key Deer Refuge, Big Pine Key, Florida, USA. We used dynamic occupancy models to determine factors associated with marsh rabbit occurrence, colonization, extinction, and the co‐occurrence of marsh rabbits and cats during a period of predator removal. Rabbit occurrence was positively related to freshwater habitat and patch size, but was negatively related to the number of individual cats detected at each site. Furthermore, marsh rabbit colonization was negatively associated with relative increases in the number of individual cats at each site between survey years. Cat occurrence was negatively associated with increasing distance from human developments. The probability of cat site extinction was positively related to a 2‐year trapping effort, indicating that predator removal reduced the cat population. Dynamic co‐occurrence models suggested that cats and marsh rabbits co‐occur less frequently than expected under random conditions, whereas co‐detections were site and survey‐specific. Rabbit site extinction and colonization were not strongly conditional on cat presence, but corresponded with a negative association. Our results suggest that while rabbits can colonize and persist at sites where cats occur, it is the number of individual cats at a site that more strongly influences rabbit occupancy and colonization. These findings indicate that continued predator management would likely benefit endangered small mammals as they recolonize restored habitats. [ABSTRACT FROM AUTHOR]

Abstract: Occupancy models that account for detection probability are important analytical tools in conservation monitoring. Traditionally, occupancy models relied on detection/non‐detection data generated from temporal replicates for estimating detectability. Due to logistical challenges and financial costs involved, many large‐scale field studies instead use spatial replication as a surrogate. The efficacy of the two approaches and their statistical validity has generally sought support from simulation‐based inferences rather than empirical data. Using the sloth bear Melursus ursinus as an example, we compared estimates of occupancy and detection probabilities obtained from temporal and spatial sampling designs. We carried out temporally replicated camera trap surveys and spatially replicated sign surveys across a 754‐km2 area around Bhadra Tiger Reserve in the Western Ghats of India. We sampled along forest/coffee plantation roads in 58 grid cells of 13 km2 each, treating these cells as independent sites. We used the standard single‐season model for the camera trap survey data, and the single‐season correlated detections model (with Markovian dependence) for the sign survey data, and incorporated ecological covariates that likely influenced occupancy and detection probabilities. Occupancy estimates from the two surveys and corresponding modelling approaches were similar [ ψ ^ c ( S E ^ ) = 0.58 (0.03) for camera trap surveys; ψ ^ s ( S E ^ ) = 0.56 (0.03) for sign surveys]. In both cases, the influence of covariates corroborated our a priori predictions. Site‐level estimates of occupancy from the two methods were highly correlated (r = .78). We generated a combined estimate of sloth bear occupancy in the region as an inverse‐variance weighted average of the two estimates [ ψ ^ ( S E ^ ) = 0.57 (0.02)]. Synthesis and applications. Studies that aim to evaluate occupancy models should account for spatial variation in occupancy/detection probabilities, particularly when making inferences on species–habitat relationships. We show that spatial replication can serve as a good surrogate for temporal replication in occupancy studies, which may be useful for distribution assessments of species when field resources are limited or logistical challenges preclude traditional survey approaches that yield temporally replicated data. Our results therefore provide a basis for efficient targeting of funds and field resources, particularly for practitioners involved in monitoring species at large landscape scales. [ABSTRACT FROM AUTHOR]

Abstract: Population size is widely used as a unit of ecological analysis, yet to estimate population size requires accounting for observed and latent heterogeneity influencing dispersion of individuals across landscapes. In newly established populations, such as when animals are translocated for conservation, dispersal and availability of resources influence patterns of abundance. We developed a process to estimate population size using N‐mixture models and spatial models for newly established and dispersing populations. We used our approach to estimate the population size of critically endangered St. Croix ground lizards (Ameiva polops) five years after translocation of 57 individuals to Buck Island, an offshore island of St. Croix, United States Virgin Islands. Estimates of population size incorporated abiotic variables, dispersal limits, and operative environmental temperature available to the lizards to account for low species detection. Operative environmental temperature and distance from the translocation site were always important in fitting the N‐mixture model indicating effects of dispersal and species biology on estimates of population size. We found that the population is increasing its range across the island by 5–10% every six months. We spatially interpolated site‐specific abundance from the N‐mixture model to the entire island, and we estimated 1,473 (95% CI, 940–1,802) St. Croix ground lizards on Buck Island in 2013 corresponding to survey results. This represents a 26‐fold increase since the translocation. We predicted the future dispersal of the lizards to all habitats on Buck Island, with the potential for the population to increase by another five times in the future. Incorporating biologically relevant covariates as explicit parameters in population models can improve predictions of population size and the future spread of species introduced to new localities. [ABSTRACT FROM AUTHOR]

The regional occupancy and local abundance of species are thought to be strongly correlated to their body size, niche breadth and niche position. The strength of the relationships among these variables can also differ between different organismal groups. Here, we analyzed data on stream diatoms and insects from a high-latitude drainage basin to investigate these relationships. To generate measures of niche position and niche breadth for each species, we used sets of local environmental and catchment variables separately, applying the outlying mean index analysis. Beta regression and negative binomial generalized linear models were run to predict regional occupancy and mean local abundance, respectively. We found a positive occupancy-abundance relationship in both diatoms and insects, and that niche-based variables were the main predictors of variation in regional occupancy and local abundance. This finding was mainly due to local environmental niche position, whereas the effects of niche breadth on regional occupancy and local abundance were less important. We also found a relationship between body size and local abundance or regional occupancy of diatoms. Our results thus add to current macroecological research by emphasizing the strong importance of niche position rather than niche breadth and body size for regional occupancy and local abundance in rarely studied organisms (e.g., diatoms and insects) and ecosystems (i.e., wilderness streams). [ABSTRACT FROM AUTHOR]

Occupancy-abundance ( OA) relationships are a foundational ecological phenomenon and field of study, and occupancy models are increasingly used to track population trends and understand ecological interactions. However, these two fields of ecological inquiry remain largely isolated, despite growing appreciation of the importance of integration. For example, using occupancy models to infer trends in abundance is predicated on positive OA relationships. Many occupancy studies collect data that violate geographical closure assumptions due to the choice of sampling scales and application to mobile organisms, which may change how occupancy and abundance are related. Little research, however, has explored how different occupancy sampling designs affect OA relationships. We develop a conceptual framework for understanding how sampling scales affect the definition of occupancy for mobile organisms, which drives OA relationships. We explore how spatial and temporal sampling scales, and the choice of sampling unit (areal vs. point sampling), affect OA relationships. We develop predictions using simulations, and test them using empirical occupancy data from remote cameras on 11 medium-large mammals. Surprisingly, our simulations demonstrate that when using point sampling, OA relationships are unaffected by spatial sampling grain (i.e., cell size). In contrast, when using areal sampling (e.g., species atlas data), OA relationships are affected by spatial grain. Furthermore, OA relationships are also affected by temporal sampling scales, where the curvature of the OA relationship increases with temporal sampling duration. Our empirical results support these predictions, showing that at any given abundance, the spatial grain of point sampling does not affect occupancy estimates, but longer surveys do increase occupancy estimates. For rare species (low occupancy), estimates of occupancy will quickly increase with longer surveys, even while abundance remains constant. Our results also clearly demonstrate that occupancy for mobile species without geographical closure is not true occupancy. The independence of occupancy estimates from spatial sampling grain depends on the sampling unit. Point-sampling surveys can, however, provide unbiased estimates of occupancy for multiple species simultaneously, irrespective of home-range size. The use of occupancy for trend monitoring needs to explicitly articulate how the chosen sampling scales define occupancy and affect the occupancy-abundance relationship. [ABSTRACT FROM AUTHOR]

Species distribution and abundance are critical population characteristics for efficient management, conservation, and ecological insight. Point process models are a powerful tool for modelling distribution and abundance, and can incorporate many data types, including count data, presence-absence data, and presence-only data. Aerial photographic images are a natural tool for collecting data to fit point process models, but aerial images do not always capture all animals that are present at a site. Methods for estimating detection probability for aerial surveys usually include collecting auxiliary data to estimate the proportion of time animals are available to be detected., We developed an approach for fitting point process models using an N-mixture model framework to estimate detection probability for aerial occupancy and abundance surveys. Our method uses multiple aerial images taken of animals at the same spatial location to provide temporal replication of sample sites. The intersection of the images provide multiple counts of individuals at different times. We examined this approach using both simulated and real data of sea otters ( Enhydra lutris kenyoni) in Glacier Bay National Park, southeastern Alaska., Using our proposed methods, we estimated detection probability of sea otters to be 0.76, the same as visual aerial surveys that have been used in the past. Further, simulations demonstrated that our approach is a promising tool for estimating occupancy, abundance, and detection probability from aerial photographic surveys., Our methods can be readily extended to data collected using unmanned aerial vehicles, as technology and regulations permit. The generality of our methods for other aerial surveys depends on how well surveys can be designed to meet the assumptions of N-mixture models. [ABSTRACT FROM AUTHOR]

Effective wildlife habitat management and conservation requires understanding the factors influencing distribution and abundance of plant species. Field studies, however, have documented observation errors in visually estimated plant cover including measurements which differ from the true value (measurement error) and not observing a species that is present within a plot (detection error). Unlike the rapid expansion of occupancy and N-mixture models for analysing wildlife surveys, development of statistical models accounting for observation error in plants has not progressed quickly. Our work informs development of a monitoring protocol for managed wetlands within the National Wildlife Refuge System., Zero-augmented beta (ZAB) regression is the most suitable method for analysing areal plant cover recorded as a continuous proportion but assumes no observation errors. We present a model extension that explicitly includes the observation process thereby accounting for both measurement and detection errors. Using simulations, we compare our approach to a ZAB regression that ignores observation errors (naïve model) and an 'ad hoc' approach using a composite of multiple observations per plot within the naïve model. We explore how sample size and within-season revisit design affect the ability to detect a change in mean plant cover between 2 years using our model., Explicitly modelling the observation process within our framework produced unbiased estimates and nominal coverage of model parameters. The naïve and 'ad hoc' approaches resulted in underestimation of occurrence and overestimation of mean cover. The degree of bias was primarily driven by imperfect detection and its relationship with cover within a plot. Conversely, measurement error had minimal impacts on inferences. We found >30 plots with at least three within-season revisits achieved reasonable posterior probabilities for assessing change in mean plant cover., For rapid adoption and application, code for Bayesian estimation of our single-species ZAB with errors model is included. Practitioners utilizing our R-based simulation code can explore trade-offs among different survey efforts and parameter values, as we did, but tuned to their own investigation. Less abundant plant species of high ecological interest may warrant the additional cost of gathering multiple independent observations in order to guard against erroneous conclusions. [ABSTRACT FROM AUTHOR]

Dynamic occupancy models are popular for estimating dynamic distribution rates (colonization and extinction) from repeated presence/absence surveys of unmarked animals. This approach assumes closure among repeated samples within primary periods, allowing estimation of dynamic rates between these periods. However, the impact of temporary emigration (TE; reversible changes in sampling availability) on dynamic rate estimates has not been tested., Using simulated data, we investigated the degree to which TE could mislead researchers interested in quantifying dynamics. We then compared results from three avian point count datasets to evaluate the likelihood that TE confounds estimates of dynamics for 19 species under a popular sampling protocol., Simulated experiments indicated that when secondary periods were open to TE, presence of dynamics was correctly identified ≥95.1% of the time, and dynamic rate estimates were accurate. However, dynamic rate estimates were biased when secondary periods were closed to TE. In empirical datasets, dynamic occupancy models had greater support than closed models for all species when secondary sampling periods occurred in immediate succession (i.e. 3 samples within 10 min); however, our results suggest that this is because dynamic estimates were heavily influenced by TE. When counts within a primary period were separated by 24-48 hr, we found evidence of dynamics for less than half of these species. We recommend an alternative sampling approach that allows accurate estimation of dynamic rates when TE is of no interest, and introduce a novel model for estimating both processes simultaneously in rare cases where they are both of biological interest., Concern for violating the occupancy modelling closure assumption has led to widespread recommendations that samples within primary periods be conducted extremely close in time. However, this may not be the best approach when interest is in quantifying dynamic rates. While dynamic occupancy models provide estimates of 'colonization' and 'extinction,' these values do not inherently represent dynamics unless TE has been explicitly modelled or accounted for with sampling design. Naiveté to this fact can result in incorrect conclusions about biological processes. [ABSTRACT FROM AUTHOR]

Occupancy models are widely used to describe the distribution of rare and cryptic species-those that occur on only a small portion of the landscape and cannot be detected reliably during a single survey. However, model estimates of occupancy (ψ) and detection probabilities ( p) are often least accurate under these circumstances., Available sampling designs for occupancy surveys include standard design, wherein each of S sites is visited K times, and removal design, wherein S sites are visited K times each or until the species of interest is detected. We propose a new conditional design, wherein each of S sites is visited one time, and sites where the species of interest is encountered during the first survey are visited an additional ( K−1) times to better estimate detection probability. We used large sample properties of maximum-likelihood estimators and Markov chain Monte Carlo (MCMC) simulations to characterise our proposed conditional design and compare it to standard and removal designs across a wide range of true occupancy and detection probabilities (ψ, p = 0.1 to 0.9 by 0.1 increments), maximum visits ( K) and total sampling effort ( E, the number of surveys accrued across all sites)., The conditional design provided more accurate estimates (lower standard or root mean squared error) of occupancy than standard or removal designs in our calculations and simulations when species were rare (ψ ≤ 0.3) as well as more accurate estimates of detection probability over most combinations of ψ and p. These low-occupancy improvements are achieved by expending a greater proportion of effort at occupied sites, improving estimates of p and thus ψ. When species are common (ψ ≥ 0.5) the removal design generally provided the most accurate occupancy estimates, whereas the standard design performed best when ψ was intermediate and during MCMC simulations when p and K were low., We recommend the conditional design for surveys of rare species and pilot studies. For multi-species surveys that include mixtures of rare and common species, a hybrid standard-conditional design with 2-3 replicates at all sites and additional replicates at sites where rare species are detected improves occupancy estimates of rare species. [ABSTRACT FROM AUTHOR]

The generally positive relationship between the number of sites a species occupies and its average abundance within those sites provides an important link between population processes occurring at different spatial scales. Although such abundance-occupancy relationships (AORs) have been documented across a very wide range of taxa and in many different environments, little is known of such patterns in Earth's largest ecosystem, the deep sea. Wood falls - derived from natural or anthropogenic inputs of wood into the oceans - constitute an important deep-sea habitat, habouring their own unique communities ultimately entirely dependent on the wood for chemical energy. In this study we take advantage of the unique features of an experimental wood fall deployment to examine AORs for the first time in deep-sea invertebrates. The study design combines advantages of both experimental (tractability, control of key environmental parameters) and observational (natural colonisation by taxonomically diverse communities) studies. We show that the interspecific AOR is strongly positive across the 48 species occurring over 32 wood fall communities. The precise form of the AOR is mediated by both species-level life history (body size) and by the colonisation stage at which communities were harvested, but not by environmental energy (wood fall size). Temporal dynamics within species are also generally consistent with positive intraspecific AORs. This support for positive AORs in the deep sea is an important extension of a macroecological generality into a new environment offering considerable potential for further testing and developing mechanistic macroecological theories. [ABSTRACT FROM AUTHOR]

In Nepal, the red panda (Ailurus fulgens) has been sparsely studied, although its range covers a wide area. The present study was carried out in the previously untapped Chitwan-Annapurna Landscape (CHAL) situated in central Nepal with an aim to explore current distributional status and identify key habitat use. Extensive field surveys conducted in 10 red panda range districts were used to estimate species distribution by presence-absence occupancy modeling and to predict distribution by presence-only modeling. The presence of red pandas was recorded in five districts: Rasuwa, Nuwakot, Myagdi, Baglung and Dhading. The predictive distribution model indicated that 1,904.44 km2 of potential red panda habitat is available in CHAL with the protected area covering nearly 41% of the total habitat. The habitat suitability analysis based on the probability of occurrence showed only 16.58% (A = 315.81 km2) of the total potential habitat is highly suitable. Red Panda occupancy was estimated to be around 0.0667, indicating nearly 7% (218 km2) of the total habitat is occupied with an average detection probability of 0.4482±0.377. Based on the habitat use analysis, altogether eight variables including elevation, slope, aspect, proximity to water sources, bamboo abundance, height, cover, and seasonal precipitation were observed to have significant roles in the distribution of red pandas. In addition, 25 tree species were documented from red panda sign plots out of 165 species recorded in the survey area. Most common was Betula utilis followed by Rhododendron spp. and Abies spectabilis. The extirpation of red pandas in previously reported areas indicates a need for immediate action for the long-term conservation of this species in CHAL. [ABSTRACT FROM AUTHOR]

Since the 1970s, California Spotted Owls ( Strix occidentalis occidentalis [CSO]) have been documented on private forest lands currently owned by Sierra Pacific Industries (SPI) in the Sierra Nevada of California. In 2012, SPI began an occupancy study on a portion of the CSO population known to occur on or near its managed forests in 5 watershed study areas averaging 110 km2 each. These watersheds occur from the southern end of the Cascade Range to just north of Yosemite National Park. We concluded that 57 historical CSO sites existed in these areas at the beginning of the study. During 2012 through 2016, the survey effort increased the total number of known CSO sites in the study areas to 65. During the same period, the yearly occupancy of the CSO sites within the study areas ranged from 70 to 86%, with 98% of the sites occupied during at least 1 y. Crude densities during the study period were calculated to be 0.117 CSO-occupied sites km−2. Compared to other studies in the Sierra Nevada, the CSO populations on our 5-yr study areas showed relatively high occupancy rates and a wide range of crude densities. [ABSTRACT FROM AUTHOR]

Many coastal dune ecosystems have been degraded by non-native dune vegetation, but these systems might still provide valuable habitat for some taxa, including amphibians. Because restoration of degraded dune systems is occurring and likely to continue, we examined the occurrence of amphibians in drainages associated with a coastal dune ecosystem degraded by invasive plants (European Beachgrass, Ammophila arenaria, and Iceplant, Carpobrotus edulis). We found that occupancy of 3 amphibian species (California Red-legged Frog, Rana draytonii; Sierran Treefrog, Hyliola sierra; and Rough-skinned Newt, Taricha granulosa) among 21 coastal-dune drainages was high, with most coastal-dune drainages occupied by all 3 species. Furthermore, reproduction of Sierran Treefrogs and California Red-legged Frogs was estimated to occur in approximately ½ and 1/3 of the drainages, respectively. The probability of occurrence of Rough-skinned Newts and pre-metamorphic life stages of both anurans decreased during the study, perhaps because of ongoing drought in California or precipitation-induced changes in phenology during the final year of the study. Maintaining structural cover and moist features during dune restoration will likely benefit native amphibian populations inhabiting coastal-dune ecosystems. [ABSTRACT FROM AUTHOR]

Both economical locomotion and physical fighting are important performance traits to many species because of their direct influence on components of Darwinian fitness. Locomotion represents a substantial portion of the total daily energy budget of many animals. Fighting performance often determines individual reproductive fitness through the means of resource control, social dominance and access to mates. However, phenotypic traits that improve either locomotor economy or fighting ability may diminish performance in the other. Here, we tested for a predicted disparity between locomotor economy and competitive ability in wild-derived house mice (Mus musculus). We used 8 week social competition trials in semi-natural enclosures to directly measure male competitive ability through territorial control and female occupancy within territories. We also measured oxygen consumption during locomotion for each mouse using running trials in an enclosed treadmill and open-flow respirometry. Our results show that territory-holding males have higher absolute and mass-specific oxygen consumption when running (i.e. reduced locomotor economy) compared with males that do not control territories. This relationship was present both before and after 8 week competition trials in seminatural enclosures. This disparity between physical competitive ability and economical locomotion may impose viability costs on males in species for which competition over mates is common and may constrain the evolution of behavioral and phenotypic diversity, particularly in natural settings with environmental and resource variability. [ABSTRACT FROM AUTHOR]

ABSTRACT Ecological relationships and processes vary across species' geographic distributions, life stages and spatial, and temporal scales. Montane landscapes are characterized by low wetland densities, rugged topographies, and cold climates. Consequently, aquatic-dependent and low-vagility ectothermic species (e.g., pool-breeding amphibians) may exhibit unique ecological associations in montane landscapes. We evaluated the relative importance of breeding- and landscape-scale features associated with spotted salamander ( Ambystoma maculatum) and wood frog ( Lithobates sylvaticus) wetland occupancy in Maine's Upper Montane-Alpine Zone ecoregion, and we determined whether models performed better when the inclusive landscape-scale covariates were estimated with topography-weighted or circular buffers. We surveyed 135 potential breeding sites during May 2013-June 2014 and evaluated environmental relationships with multi-season implicit dynamics occupancy models. Breeding site occupancy by both species was influenced solely by breeding-scale habitat features. Spotted salamander occupancy probabilities increased with previous or current beaver ( Castor canadensis) presence, and models generally were better supported when the inclusive landscape-scale covariates were estimated with topography-weighted rather than circular buffers. Wood frog occupancy probabilities increased with site area and percent shallows, but neither buffer type was better supported than the other. Model rank order and support varied between buffer types, but model inferences did not. Our results suggest pool-breeding amphibian conservation in montane Maine include measures to maintain beaver populations and large wetlands with proportionally large areas of shallows ≤1-m deep. Inconsistencies between our study and previous studies substantiate the value of region-specific research for augmenting species' conservation management plans and suggest the application of out-of-region inferences may promote ineffective conservation. © 2016 The Wildlife Society. [ABSTRACT FROM AUTHOR]

Occupancy modeling is a widely used analytical technique for assessing species distributions and range dynamics. However, occupancy analyses frequently ignore variation in abundance of occupied sites, even though site abundances affect many of the parameters being estimated (e.g., extinction, colonization, detection probability). We introduce a new model ('dynamic N-occupancy') capable of providing accurate estimates of local abundance, population gains (reproduction/immigration), and apparent survival probabilities while accounting for imperfect detection using only detection/nondetection data. Our model utilizes heterogeneity in detection based on variations in site abundances to estimate latent demographic rates via a dynamic N-mixture modeling framework. We validate our model using simulations across a wide range of values and examine the data requirements, including the number of years and survey sites needed, for unbiased and precise estimation of parameters. We apply our model to estimate spatiotemporal heterogeneity in abundances of barred owls ( Strix varia) within a recently invaded region in Oregon (USA). Estimates of apparent survival and population gains are consistent with those from a nearby radio-tracking study and elucidate how barred owl abundances have increased dramatically over time. The dynamic N-occupancy model greatly improves inferences on individual-level population processes from occupancy data by explicitly modeling the latent population structure. [ABSTRACT FROM AUTHOR]

An enduring challenge in ecology is to understand what drives spatial variation in the size and structure of communities. The ability to count the number of species present at a location is hindered by the fact that not all species are equally detectable, and invariably some go completely undetected. This makes comparing species richness across distinct spatial units (or regions) problematic as sources of error are usually unaccounted for in simple enumerations of species., Multi-species occupancy models explicitly incorporate a model for this observation uncertainty and provide a framework for estimating community size when detection is imperfect. Currently, however, the model is restricted to estimating the number of species at only a single region of interest. In this paper we extend the multi-species occupancy model to accommodate data collected across multiple regions of interest (e.g., reserves or biomes)., We report improved model performance of the joint multiregion approach when compared to the more traditional two-stage approach of modelling spatial variation in species richness using simulations. Then, applying the model to data collected from eight avian communities in northern Italy, we demonstrate how species richness can be modeled as a spatially varying function of habitat complexity., Extending the multi-species occupancy model to accommodate data collected across multiple regions of interest (e.g., reserves or biomes) allows for joint estimation of region-specific community size and permits species richness to be modeled as a function of region-specific covariates. Our approach provides a mechanism for testing hypotheses about why and how species richness varies across space. [ABSTRACT FROM AUTHOR]

Many tropical mammals with important functional roles in forest ecosystems are threatened with extinction, yet how they respond to increasingly prevalent habitat disturbance, such as selective logging, is not well understood. We deployed 43 motion-triggered camera traps in 2013 and 2014 in unlogged forest and in a forest logged three decades previously in Malaysian Borneo. We used camera trap photographs to assess whether selective logging influenced the local abundance of medium to large-bodied ground-dwelling mammals. We focused our study on six locally common species: sambar deer, ( Rusa unicolor ), yellow muntjac ( Muntiacus atherodes ), chevrotains ( Tragulus spp.), banded civet ( Hemigalus derbyanus ), Malay civet ( Viverra tangalunga ), and pig-tailed macaque (Macaca nemestrina ). Occupancy models were used to estimate local abundance. None of the mammals in our study exhibited decreased abundance in logged forest, although yellow muntjac and pig-tailed macaque were associated with canopy height. Contiguous forest connects these two areas, allowing animal movement between them, potentially explaining the lack of responses. The elapsed time since logging may have further influenced our findings. The effects of logging on mammal local abundance may no longer detectable after 30 years. Overall, our results demonstrate the importance of regenerating forest as habitat for medium- to large-sized mammal species. [ABSTRACT FROM AUTHOR]

We provide the first estimates of density and population size of two sympatric species of armadillos ( Euphractus sexcinctus and Dasypus novemcinctus ) for the Vale Natural Reserve, evaluating how poaching would be affecting their habitat use (i.e. detectability and occupancy probability) and also describing their activity patterns. Abundance and density were estimated using a distance sampling technique. Occupancy, detectability, and activity patterns were assessed using camera-trapping during a one-year period. Estimated population densities were 0.06 individuals per hectare (ind/ha) and 0.05 ind/ha for D. novemcinctus and E. sexcinctus , respectively. Occupancy probability of both species was affected by the distance to forest edge. D. novemcinctus showed a positive relationship with increasing distance from the edge, whereas E. sexcinctus showed an opposite relationship. D. novemcinctus also showed higher occupancy rates in sites closer to a water resource. Additionally, occupancy and detectability of both species were negatively affected by historical poaching intensity in the reserve. Activity patterns varied between species, with D. novemcinctus being mostly nocturnal and E. sexcinctus being diurnal. We conclude that despite the two armadillo species sharing several niche dimensions, they showed spatial and temporal segregation. These differences may be an adaptation to reduce interspecific competition but may also be affected by the need to avoid poaching pressures. This study contributes to increase the knowledge and conservation of these two little-studied species. [ABSTRACT FROM AUTHOR]

Models accounting for imperfect detection are important. Single-visit ( SV) methods have been proposed as an alternative to multiple-visit methods to relax the assumption of closed population. Knape & Korner-Nievergelt (Methods in Ecology and Evolution, 2015) showed that under certain models of probability of detection, SV methods are statistically non-identifiable leading to biased population estimates., There is a close relationship between estimation of the resource selection probability function ( RSPF) using weighted distributions and SV methods for occupancy and abundance estimation. We explain the precise mathematical conditions needed for RSPF estimation as stated in Lele & Keim (Ecology, 87, 2006, 3021). The identical conditions that remained unstated in our papers on SV methodology are needed for SV methodology to work. We show that the class of admissible models is quite broad and does not excessively restrict the application of the RSPF or the SV methodology., To complement the work by Knape and Korner-Nievergelt, we study the performance of multiple-visit methods under the scaled logistic detection function and a much wider set of situations. In general, under the scaled logistic detection function, multiple-visit methods also lead to biased estimates., As a solution to this problem, we extend the SV methodology to a class of models that allows use of scaled probability function. We propose a multinomial extension of SV methodology that can be used to check whether the detection function satisfies the RSPF condition or not. Furthermore, we show that if the scaling factor depends on covariates, then it can also be estimated., We argue that the instances where the RSPF condition is not satisfied are rare in practice. Hence, we disagree with the implication in Knape & Korner-Nievergelt (Methods in Ecology and Evolution, 2015) that the need for RSPF condition makes SV methodology irrelevant in practice. [ABSTRACT FROM AUTHOR]

Regional monitoring strategies frequently employ a nested sampling design where a finite set of study areas from throughout a region are selected and intensive sampling occurs within a subset of sites within the individual study areas. This sampling protocol naturally lends itself to a hierarchical analysis to account for dependence among subsamples. Implementing such an analysis using a classic likelihood framework is computationally challenging when accounting for detection errors in species occurrence models. Bayesian methods offer an alternative approach for fitting models that readily allows for spatial structure to be incorporated. We demonstrate a general approach for estimating occupancy when data come from a nested sampling design. We analyzed data from a regional monitoring program of wood frogs ( Lithobates sylvaticus) and spotted salamanders ( Ambystoma maculatum) in vernal pools using static and dynamic occupancy models. We analyzed observations from 2004 to 2013 that were collected within 14 protected areas located throughout the northeast United States. We use the data set to estimate trends in occupancy at both the regional and individual protected area levels. We show that occupancy at the regional level was relatively stable for both species. However, substantial variation occurred among study areas, with some populations declining and some increasing for both species. In addition, When the hierarchical study design is not accounted for, one would conclude stronger support for latitudinal gradient in trends than when using our approach that accounts for the nested design. In contrast to the model that does not account for nesting, the nested model did not include an effect of latitude in the 95% credible interval. These results shed light on the range-level population status of these pond-breeding amphibians, and our approach provides a framework that can be used to examine drivers of local and regional occurrence dynamics. [ABSTRACT FROM AUTHOR]

There is interest in large-scale and unbiased monitoring of biodiversity status and trend, but there are few published examples of such monitoring being implemented. The New Zealand Department of Conservation is implementing a monitoring program that involves sampling selected biota at the vertices of an 8-km grid superimposed over the 8.6 million hectares of public conservation land that it manages. The introduced brushtail possum (Trichosurus Vulpecula) is a major threat to some biota and is one taxon that they wish to monitor and report on. A pilot study revealed that the traditional method of monitoring possums using leg-hold traps set for two nights, termed the Trap Catch Index, was a constraint on the cost and logistical feasibility of the monitoring program. A phased implementation of the monitoring program was therefore conducted to collect data for evaluating the trade-off between possum occupancy–abundance estimates and the costs of sampling for one night rather than two nights. Reducing trapping effort from two nights to one night along four trap-lines reduced the estimated costs of monitoring by 5.8% due to savings in labour, food and allowances; it had a negligible effect on estimated national possum occupancy but resulted in slightly higher and less precise estimates of relative possum abundance. Monitoring possums for one night rather than two nights would provide an annual saving of NZ$72,400, with 271 fewer field days required for sampling. Possums occupied 60% (95% credible interval; 53–68) of sampling locations on New Zealand’s public conservation land, with a mean relative abundance (Trap Catch Index) of 2.7% (2.0–3.5). Possum occupancy and abundance were higher in forest than in non-forest habitats. Our case study illustrates the need to evaluate relationships between sampling design, cost, and occupancy–abundance estimates when designing and implementing large-scale occupancy–abundance monitoring programs. [ABSTRACT FROM AUTHOR]

As freshwater habitats are among the most endangered, there is an urgent need to identify critical areas for conservation, especially those that are home to endangered species. The Pyrenean desman ( Galemys pyrenaicus ) is a semi-aquatic mammal whose basic ecological requirements are largely unknown, hindering adequate conservation planning even though it is considered as a threatened species. Species distribution modelling is challenging for freshwater species. Indeed, the complexity of aquatic ecosystems (e.g., linear and hierarchical ordering) must be taken into account as well as imperfect sampling. High-quality and relevant hydrological descriptors should also be used. To understand the influence of environmental covariates on the occupancy and detection of the Pyrenean desman, we combine both a robust sign-survey data set (i.e. with genetic validation ensuring true presence information) and a hydrological model to simulate the flow regime across a whole catchment. Markovian site-occupancy analysis, taking into account sign detection and based on spatially adjacent replicates, indicated a positive influence of heterogeneity of substrate and shelters, and a negative influence of flow variability on Pyrenean desman detection. This valuable information should help to improve monitoring programs for this endangered species. Our results also highlighted a spatially clustered distribution and a positive influence of stream flow and number of tributaries on occupancy. Hence, modifications of flow regime (e.g. hydropower production, irrigation, climate change) and habitat fragmentation appear to be major threats for this species, altering the connectivity between tributaries and the mainstream river as well as between adjacent sub-catchments. [ABSTRACT FROM AUTHOR]

The past decade has seen an explosion in the development and application of models aimed at estimating species occurrence and occupancy dynamics while accounting for possible non-detection or species misidentification., We discuss some recent occupancy estimation methods and the biological systems that motivated their development. Collectively, these models offer tremendous flexibility, but simultaneously place added demands on the investigator., Unlike many mark-recapture scenarios, investigators utilizing occupancy models have the ability, and responsibility, to define their sample units (i.e. sites), replicate sampling occasions, time period over which species occurrence is assumed to be static and even the criteria that constitute 'detection' of a target species. Subsequent biological inference and interpretation of model parameters depend on these definitions and the ability to meet model assumptions., We demonstrate the relevance of these definitions by highlighting applications from a single biological system (an amphibian-pathogen system) and discuss situations where the use of occupancy models has been criticized. Finally, we use these applications to suggest future research and model development. [ABSTRACT FROM AUTHOR]

Estimating the abundance of wild carnivores is of foremost importance for conservation and management. However, given their elusive habits, direct observations of these animals are difficult to obtain, so abundance is more commonly estimated from sign surveys or radio-marked individuals. These methods can be costly and difficult, particularly in large areas with heavy forest cover. As an alternative, recent research has suggested that wolf abundance can be estimated from occupancy–abundance curves derived from “virtual” surveys of simulated wolf track networks. Although potentially more cost-effective, the utility of this approach hinges on its robustness to violations of its assumptions. We assessed the sensitivity of the occupancy–abundance approach to four assumptions: variation in wolf movement rates, changes in pack cohesion, presence of lone wolves, and size of survey units. Our simulations showed that occupancy rates and wolf pack abundances were biased high if track surveys were conducted when wolves made long compared to short movements, wolf packs were moving as multiple hunting units as opposed to a cohesive pack, and lone wolves were moving throughout the surveyed landscape. We also found that larger survey units (400 and 576 km2) were more robust to changes in these factors than smaller survey units (36 and 144 km2). However, occupancy rates derived from large survey units rapidly reached an asymptote at 100% occupancy, suggesting that these large units are inappropriate for areas with moderate to high wolf densities (>15 wolves/1,000 km2). Virtually-derived occupancy–abundance relationships can be a useful method for monitoring wolves and other elusive wildlife if applied within certain constraints, in particular biological knowledge of the surveyed species needs to be incorporated into the design of the occupancy surveys. Further, we suggest that the applicability of this method could be extended by directly incorporating some of its assumptions into the modelling framework. [ABSTRACT FROM AUTHOR]

Aim Understanding patterns in a species' occupancy and abundance across multiple scales is important for management purposes, particularly for protecting threatened species. Here, we develop a new quantitative, multiscale model of occupancy and abundance that characterizes seven types of rarity and one of commonness in high-diversity communities of staghorn coral. We examine how rare species influence coral community structure and explore spatial variability in underlying patterns of community structure in the context of optimizing the data needed to protect biodiversity. Location North-west Pacific Ocean. Methods We present categorical abundance data for 87 species of staghorn corals occurring within 100 sites across five reefs in the north-west Pacific Ocean. We develop a new model that combines measures of global distribution, local distribution and local abundance to describe eight mutually exclusive occupancy-abundance patterns, which can be used to prioritize regional species conservation. Traditional and new analytical approaches are compared to explore how rare species influence multidimensional space and community structure. Results We show that five types of occupancy-abundance relationships exist in staghorn coral assemblages, including four patterns of rarity. The overwhelming majority of species (73%) are rare according to local abundance and/or distribution criteria. Occupancy-abundance patterns are spatially variable in staghorn coral communities, and no single underlying distribution fits all assemblages. Our findings suggest that 54 species are at risk of regional extinction, 30 of which are also classified as 'vulnerable' by the IUCN. Main conclusions Our model demonstrates that multiple occupancy-abundance patterns exist in staghorn coral assemblages. We conclude that 66% of the pool of staghorn coral fauna in the north-west Pacific is at risk of regional extinction. At the locations and scales examined here, occupancy-abundance patterns and the underlying distributions of coral communities are spatially variable, suggesting that it may not be appropriate to apply unified ecological theory to communities with a large proportion of threatened species because this may jeopardize biodiversity conservation. [ABSTRACT FROM AUTHOR]

Zero-inflated versions of standard distributions for count data are often required in order to account for excess zeros when modeling the abundance of organisms. Such distributions typically have as parameters X, the mean of the count distribution, and n, the probability of an excess zero. Implementations of zero-inflated models in ecology typically model X using a set of predictor variables, and n is fit either as a constant or with its own separate model. Neither of these approaches makes use of any relationship that might exist between n and X. However, for many species, the rate of occupancy is closely and positively related to its average abundance. Here, this relationship was incorporated into the model for zero inflation by functionally linking n to X, and was demonstrated in a study of snapper (Pagrus auratus) in and around a marine reserve. This approach has several potential practical advantages, including better computational performance and more straightforward model interpretation. It is concluded that, where appropriate, directly linking n to X can produce more ecologically accurate and parsimonious statistical models of species abundance data. [ABSTRACT FROM AUTHOR]

Copyright of Basic & Applied Ecology is the property of Urban & Fischer Verlag and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The relationship between species abundance, the variance of the number of individuals, and species occupancy is a fundamental ecological characteristic of a community. Moreover, this relationship varies across scales, and any model for the variance-occupancy-abundance (VOA) relationship has to address its scale dependency in a consistent way. In this study, point-process theory was used to define a multiscale model that jointly predicts the VOA relationship across scales in a consistent way. This provides a tool to jointly analyze data sets collected at different scales and to give insights into the biological processes underlying the VOA relationship. This model can also account for different types of individual spatial pattern (clustered, random, or regular). Three stand-mapping data sets of tree species in tropical rain forests were used to assess the relevance of this model. When compared with four existing models, the model based on point-process theory provided the best fit to the data and was the most often ranked as the model with the best predictive performance. [ABSTRACT FROM AUTHOR]

Species assemblages vary in structure due to a wide variety of processes operating at ecological and much broader biogeographical scales. Cross-scale studies of assemblage structure are necessary to fully understand this variability. Here, we evaluate the abundance and occupancy patterns of hierarchically sampled coral assemblages in three habitats (reef flat, crest, and slope) and five regions (Indonesia, Papua New Guinea, the Solomon Islands, American Samoa, and the Society Islands) across the west-central Pacific Ocean. Specifically, we compare two alternative models that unify spatial variance and occupancy via the negative binomial distribution. The first assumes a power-law scaling between the mean and variance of abundance; the second assumes a quadratic variance-mean relationship and a constant abundance-invariant aggregation parameter. Surprisingly, the well-established power-law model performs worse than the model assuming abundance-invariant aggregation, for both variance-mean and occupancy-abundance relationships. We also find strong evidence for regional and habitat variation in these relationships and in the levels of aggregation estimated by the abundance-invariant aggregation model. Among habitats, corals on reef flats exhibited lower occupancy and higher levels of aggregation compared to reef crests and slopes. Among regions, low occupancy and high aggregation were most pronounced across all habitats in American Samoa. These patterns may be related to habitat and regional differences in disturbance and recovery processes. Our results suggest that the spatial scaling of abundance and occupancy is sensitive to processes operating among these habitats and at regional scales. However, the consistency of these relationships across species within assemblages suggests that a theoretical unification of spatial variance and occupancy patterns is indeed possible. [ABSTRACT FROM AUTHOR]

The article presents a study which evaluates the performance of intraspecific occupancy-abundance relationship (OAR)-based model and scaling pattern of occupancy (SPO) in extrapolating population size in the avian species of southern Africa. It shows that SPO models exceeded OAR models because the latter assume environmental homogeneity. It notes that SPO models are fitted at larger-scale data because they are based on scale dependence of the range size of the avian species.

Traditional occupancy–abundance and abundance–variance–occupancy models do not take into account zero-inflation, which occurs when sampling rare species or in correlated counts arising from repeated measures. In this paper we propose a novel approach extending occupancy–abundance relationships to zero-inflated count data. This approach involves three steps: (1) selecting distributional assumptions and parsimonious models for the count data, (2) estimating abundance, occupancy and variance parameters as functions of site- and/or time-specific covariates, and (3) modelling the occupancy–abundance relationship using the parameters estimated in step 2. Five count datasets were used for comparing standard Poisson and negative binomial distribution (NBD) occupancy–abundance models. Zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) occupancy–abundance models were introduced for the first time, and these were compared with the Poisson, NBD, He and Gaston's and Wilson and Room's abundance–variance–occupancy models. The percentage of zero counts ranged from 45 to 80% in the datasets analysed. For most of the datasets, the ZINB occupancy–abundance model performed better than the traditional Poisson, NBD and Wilson and Room's model. He and Gaston's model performed better than the ZINB in two out of the five datasets. However, the occupancy predicted by all models increased faster than the observed as density increased resulting in significant mismatch at the highest densities. Limitations of the various models are discussed, and the need for careful choice of count distributions and predictors in estimating abundance and occupancy parameter are indicated. [Copyright &y& Elsevier]

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There have been several attempts to build a unified framework for macroecological patterns. However, these have mostly been based either on questionable assumptions or have had to be parameterized to obtain realistic predictions. Here, we propose a new model explicitly considering patterns of aggregated species distributions on multiple spatial scales, the property which lies behind all spatial macroecological patterns, using the idea we term ‘generalized fractals’. Species’ spatial distributions were modelled by a random hierarchical process in which the original ‘habitat’ patches were randomly replaced by sets of smaller patches nested within them, and the statistical properties of modelled species assemblages were compared with macroecological patterns in observed bird data. Without parameterization based on observed patterns, this simple model predicts realistic patterns of species abundance, distribution and diversity, including fractal-like spatial distributions, the frequency distribution of species occupancies/abundances and the species–area relationship. Although observed macroecological patterns may differ in some quantitative properties, our concept of random hierarchical aggregation can be considered as an appropriate null model of fundamental macroecological patterns which can potentially be modified to accommodate ecologically important variables. [ABSTRACT FROM AUTHOR]

The need for reproductive assurance during dispersal, along with the pressure of local mate competition, means that the importance of frequent or repeated colonization is implicit in the sexual-system evolution literature. However, to date there have been few empirical tests of the association between colonization and the sexual system in plants. Here we provide such a test by comparing occupancy and abundance of populations of the European plant Mercurialis annua across regions characterized by different sexual systems. Specifically, we predicted that monomorphic, hermaphroditic populations, which are thought to have evolved under selection for reproductive assurance during repeated bouts of colonization, would be smaller and their suitable habitat less frequently occupied than dimorphic populations, where males co-occur with either females or hermaphrodites. We show that both of these predictions are upheld. We evaluate our results against competing hypotheses for the occupancy-abundance relationship and conclude that they are most consistent with the metapopulation model for sexual-system variation in M. annua. [ABSTRACT FROM AUTHOR]

The area of occupancy of a species and its abundance are dependent on the spatial scale at which they are measured. However, it is less obvious how the scale of sampling affects their correlation. This study investigated and modeled the effects of sampling unit size and areal extent on the interspecific occupancy-abundance relationships for a tropical tree species assemblage at a local scale and a temperate bird species assemblage at a regional scale. The results showed that both sampling unit size and study extent had profound quantitative effects on the occupancy-abundance relationship, although it remained positive. Several properties of the occupancy-abundance relationship can result from the effects of scale: 1) the linearity of the relationship decreases with the increase of sampling unit size; 2) for a given abundance, the area of occupancy increases with sampling unit size; and 3) variation in the area of occupancy increases with the increase of both sampling unit size and extent, and if the extent is large enough may be sufficient that no occupancy-abundance relationship is observed. Although the occupancy-abundance relationship can be satisfactorily modeled, the parameters depend on the scale used. This suggests that a model derived from one scale cannot be applied to another. In other words, to estimate the rarity or commonness of species using such a model, the estimation must be strictly done using the same sampling scale for all the species. [ABSTRACT FROM AUTHOR]