David John Murrell

The K function and its related statistics have been an enduring tool in the analysis of spatial point processes, providing an easy to compute and interpret summary statistic for characterising the interactions between points of one type, or between two different types of points. In this paper, we introduce a partial K function, enabling us to account for some of the effects of the other point types when analysing point-point interactions. The partial K function we introduce reduces to the usual K function when the other points are independent of the points of interest and has a similar interpretation. Using examples, we demonstrate how the partial K function can unpick dependence between point types that would otherwise be hidden in the usual K function. We also discuss important bias correction steps and hyperparameter selection. In addition, we introduce an extension to account for other spatial covariates, and demonstrate the methodology on the Lansing Woods dataset.

Speciation is the ultimate source of biodiversity, yet because most species arise in spatial isolation (allopatry), it remains unclear how speciation history shapes patterns of sympatric species richness. Here, we examine how the timing of past speciation events influences the maximum sympatric species richness attained across 40 families of passerine birds. Using a phylogenetic model, we infer that the average waiting time for species to assemble in sympatry is remarkably long (~8 million years), occurring over macroevolutionary timescales comparable to the pace of speciation itself. Consequently, we find that the proportion of species in sympatry varies substantially across families, peaking in ancient or small clades comprised of older species, while remaining low in large, rapidly diversifying clades. Our analysis shows that macroevolutionary delays in colonisation are sufficient for speciation history to leave an indelible legacy on present‐day assemblages, challenging the view that richness is strictly limited by contemporary environmental capacity.

The K function and its related statistics have been an enduring tool in the analysis of spatial point processes, providing an easy to compute and interpret summary statistic for characterising the interactions between points of one type, or between two different types of points. In this paper, introduce a partial K function, enabling us to account for some of the effects of the other point types when analysing point-point interactions. The partial K function we introduce reduces to the usual K function when the other points are independent of the points of interest and has a similar interpretation. Using examples, we demonstrate how the partial K function can unpick dependence between point types that would otherwise be hidden in the usual K function. We also discuss important bias correction steps and hyperparameter selection. In addition, we discuss an extension to account for other spatial covariates, and demonstrate the methodology on the Lansing Woods dataset.

Freshwater ecosystems are globally threatened by habitat loss, pollution, and invasive species, all of which are particularly acute in urban areas. To assess the impacts of urbanization on freshwater biodiversity—specifically the effects of alien species on native primary aquatic vertebrates—we investigated the World Heritage Site, Lake Xochimilco in Mexico City. Focusing on fishes and amphibians, we applied environmental DNA metabarcoding using primer pairs targeting mitochondrial 12S and 16S across the remnant lake and collected 14 aquatic environmental variables for sampled sites. Our survey recovered ca. 60% of Lake Xochimilco's historically recorded fish and amphibian species, including rare species and novel taxa not detected by past traditional surveys. However, our findings imply a severely degraded wetland, with alpha diversity indices indicating a low‐diversity ecosystem dominated by alien fishes. Beta diversity analysis revealed a heterogeneous ecosystem that may be driven partially by the presence of alien fish, particularly cyprinids. Environmental variables linked to pollution predicted the presence of non‐native fish families. We also found evidence that some species prefer to occupy different water bodies within the lake remnant. Despite the ongoing degradation of this ecosystem, native and endemic fauna are persisting, although detections were typically rare. We found no evidence of the Critically Endangered axolotl salamanders (Ambystoma sp.) from wild sites; however, we detected their presence in one wildlife refuge, highlighting the potential of refuges to prevent complete extinction in the wild. We also found evidence of cryptic taxonomic diversity in Lithobates frogs and evidence of endemic genera, including the threatened mexclapique fish (Girardinicthys viviparus). These fishes are considered extirpated, suggesting remnant populations persist undetected by traditional surveys. Despite clear evidence of an ecosystem under extreme decline compared to historical biological records, our study demonstrates the potential for restoration, given the presence of native freshwater species and the success of wildlife refuges.

Species conservation relies heavily on population estimates derived from capture–recapture analyses, which are liable to produce biased results if individual animals are incorrectly identified. Captive and known‐animal studies have shown that supplementing human observation with artificial intelligence (AI) has the potential to reduce these errors. However, no study has directly quantified the relationship between using AI for individual identification and the demographic estimates it produces for a threatened population in situ. We compared the demographic estimates produced by capture–recapture analyses of two distinct encounter histories constructed from the same survey data; one produced using individual identifications made by human observers alone (the ‘human‐only data set’), and one produced using AI software to aid individual identification (the ‘AI‐supplemented data set’). This approach enabled us to address two key questions: (i) does the use of artificial intelligence software for individual identification influence demographic estimates for an in situ conservation programme? (ii) How has the population of our case study species, the critically endangered Kapitia skink, responded following an extreme weather event, cyclone Fehi? We found that, without AI, human observers appeared prone to make reclassification or ‘splitting’ errors, in which a recaptured animal was wrongly assigned as a new individual. Analysis of the AI‐supplemented data set consistently produced lower estimates of population abundance over time, relative to the same analysis of the human‐only data set. This provides new evidence that wild species monitoring efforts may be prone to underestimating the extinction risk of populations if they are dependent on individual identification methodologies with high potential for human errors. Our case study species, the Kapitia skink, demonstrated a positive population trend in the period following cyclone Fehi. While promising, conservation intervention is recommended to address persistent threats. Practical implication. Supplementing human observation with AI software for individual identification could mitigate errors leading to the underestimation of extinction risk for endangered species. We encourage further development of AI software to increase its automation and accessibility and recommend that practitioners consider its use in population monitoring based on the identification of individuals in imagery.

Infectious diseases spread through international wildlife trade networks, presenting major conservation and welfare challenges. The diseases amphibian chytridiomycosis (caused predominantly by chytrid fungus Batrachochytrium dendrobatidis, Bd) and ranavirosis (caused by iridoviruses in the genus Ranavirus, Rv) are the result of infection by globally distributed pathogens. These pathogens spread internationally through live‐animal trade networks and have driven population declines, mass mortalities, and community collapse for a broad range of amphibian species. Environmental (e)DNA methods may provide highly sensitive and non‐invasive pathogen surveillance for traded or wild amphibians. To investigate the relationship between eDNA detection and environmental pathogen persistence, eDNA degradation rates were quantified across a range of temperatures (15°C–25°C) for both Bd and Ranavirus. Estimated decay rates suggest that overall pathogen eDNA concentration degrades by 99% between 18.9–52.4 h. Low levels of pathogen eDNA remained detectable for the duration of the experiment (> 28 days). Time was found to have a significant negative effect on eDNA concentration for both pathogens (p < 0.001). The negative effect of temperature on eDNA concentration was significant for both pathogens (20°C for Rv, p < 0.05; 25°C for Bd/Rv p < 0.001). We argue that high concentrations of eDNA represent viable pathogen in the environment, demonstrating the usefulness of eDNA for the monitoring of disease status of consignments of traded amphibians.

Speciation is the ultimate source of biodiversity. However, because most species arise in spatial isolation (allopatry), how speciation shapes patterns of co-occurring (sympatry) species richness remains unclear. Here we examine how the history of past speciation events influences the maximum sympatric species richness attained across passerine bird clades (n = 40 families). Using a phylogenetic model, we infer that the rate at which species assemble in sympatry is extremely slow, and on average millions of years after speciation onset. As a result, having accounted for clade size, sympatric species richness varies substantially across families, being highest in small or ancient clades comprised of older species that have had more time to accumulate in sympatry. While our analysis does not test the ecological or evolutionary processes causing the slow build-up of sympatric assemblages, our results show that speciation history has left an indelible legacy on current species richness patterns.

Time series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification (e.g., assigning species to individual bird calls); clustering (e.g., clustering similar responses in population dynamics to abrupt changes in the environment or management interventions); prediction (e.g., accuracy of model predictions to original time series data); and anomaly detection (e.g., detecting possible catastrophic events from population time series). These common tasks in ecological research all rely on the notion of (dis‐) similarity, which can be determined using distance measures. A plethora of distance measures have been described, predominantly in the computer and information sciences, but many have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures for time‐series‐related tasks. Therefore, many potential applications remain unexplored. Here, we describe 16 properties of distance measures that are likely to be of importance to a variety of ecological questions involving time series. We then test 42 distance measures for each property and use the results to develop an objective method to select appropriate distance measures for any task and ecological dataset. We demonstrate our selection method by applying it to a set of real‐world data on breeding bird populations in the UK and discuss other potential applications for distance measures, along with associated technical issues common in ecology. Our real‐world population trends exhibit a common challenge for time series comparisons: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well suited to comparing noisy time series and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit‐for‐purpose but are consistent in their rankings of the population trends. The results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing ecological time series and help us answer new ecological questions.

Spatial point patterns are a commonly recorded form of data in ecology, medicine, astronomy, criminology, epidemiology and many other application fields. One way to understand their second order dependence structure is via their spectral density function. However, unlike time series analysis, for point patterns such approaches are currently underutilized. In part, this is because the interpretation of the spectral representation of the underlying point processes is challenging. In this paper, we demonstrate how to band-pass filter point patterns, thus enabling us to explore the spectral representation of point patterns in space by isolating the signal corresponding to certain sets of wavenumbers.

Aim: The exceptional turnover in biota with elevation and number of species coexisting at any elevation makes tropical mountains hotspots of biodiversity. However, understanding the historical processes through which species arising in geographical isolation (i.e. allopatry) assemble along the same mountain slope (i.e. sympatry) remains a major challenge. Multiple models have been proposed including (1) the sorting of already elevationally divergent species, (2) the displacement of elevation upon secondary contact, potentially followed by convergence, or (3) elevational conservatism, in which ancestral elevational ranges are retained. However, the relative contribution of these processes to generating patterns of elevational overlap and turnover is unknown.Location: Tropical mountains of Central‐ and South‐America.Time Period: The last 12 myr.Major Taxa Studied: Birds.Methods: We collate a dataset of 165 avian sister pairs containing estimates of phylogenetic age, geographical and regional elevational range overlap. We develop a framework based on continuous‐time Markov models to infer the relative frequency of different historical pathways in explaining present‐day overlap and turnover of sympatric species along elevational gradients.Results: We show that turnover of closely related bird species across elevation can predominantly be explained by displacement of elevation ranges upon contact (81%) rather than elevational divergence in allopatry (19%). In contrast, overlap along elevation gradients is primarily (88%) explained by conservatism of elevational ranges rather than displacement followed by elevational expansion (12%).Main Conclusions: Bird communities across elevation gradients are assembled through a mix of processes, including the sorting, displacement and conservatism of species elevation ranges. The dominant role of conservatism in explaining co‐occurrence of species on mountain slopes rejects more complex scenarios requiring displacement followed by expansion. The ability of closely related species to coexist without elevational divergence provides a direct and faster pathway to sympatry and helps explain the exceptional species richness of tropical mountains.