Incursions of sea lamprey, Petromyzon marinus, and striped bass, Morone saxatilis, in Labrador waters: Episodic events or evidence of a northward range expansion?

We describe observations of sea lamprey (Petromyzon marinus) and striped bass (Morone saxatilis) incursions into Labrador, Canada. While P. marinus have been periodically observed in similar latitudes, their numbers have conspicuously increased in estuarine environments in 2020. In contrast, M. saxatilis were not observed from Labrador until 2017 but appear to be declining after the initial surge in abundance that peaked in 2018. It remains unclear whether spawning populations of either species exist. Given the potential to negatively affect species of commercial and cultural importance through predation, follow-up surveys are warranted.

Global COVID-19 lockdown highlights humans as both threats and custodians of the environment.

The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from 89 different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus, initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness.

Increased population size of fish in a lowland river following restoration of structural habitat.

Most assessments of the effectiveness of river restoration are done at small spatial scales (<10 km) over short time frames (less than three years), potentially failing to capture large-scale mechanisms such as completion of life-history processes, changes to system productivity, or time lags of ecosystem responses. To test the hypothesis that populations of two species of large-bodied, piscivorous, native fishes would increase in response to large-scale structural habitat restoration (reintroduction of 4,450 pieces of coarse woody habitat into a 110-km reach of the Murray River, southeastern Australia), we collected annual catch, effort, length, and tagging data over seven years for Murray cod (Maccullochella peelii) and golden perch (Macquaria ambigua) in a restored "intervention" reach and three neighboring "control" reaches. We supplemented mark-recapture data with telemetry and angler phone-in data to assess the potentially confounding influences of movement among sampled populations, heterogeneous detection rates, and population vital rates. We applied a Bayesian hierarchical model to estimate changes in population parameters including immigration, emigration, and mortality rates. For Murray cod, we observed a threefold increase in abundance in the population within the intervention reach, while populations declined or fluctuated within the control reaches. Golden perch densities also increased twofold in the intervention reach. Our results indicate that restoring habitat heterogeneity by adding coarse woody habitats can increase the abundance of fish at a population scale in a large, lowland river. Successful restoration of poor-quality "sink" habitats for target species relies on connectivity with high-quality "source" habitats. We recommend that the analysis of restoration success across appropriately large spatial and temporal scales can help identify mechanisms and success rates of other restoration strategies such as restoring fish passage or delivering water for environmental outcomes.

The shape of abundance distributions across temperature gradients in reef fishes.

Improving predictions of ecological responses to climate change requires understanding how local abundance relates to temperature gradients, yet many factors influence local abundance in wild populations. We evaluated the shape of thermal-abundance distributions using 98 422 abundance estimates of 702 reef fish species worldwide. We found that curved ceilings in local abundance related to sea temperatures for most species, where local abundance declined from realised thermal 'optima' towards warmer and cooler environments. Although generally supporting the abundant-centre hypothesis, many species also displayed asymmetrical thermal-abundance distributions. For many tropical species, abundances did not decline at warm distribution edges due to an unavailability of warmer environments at the equator. Habitat transitions from coral to macroalgal dominance in subtropical zones also influenced abundance distribution shapes. By quantifying the factors constraining species' abundance, we provide an important empirical basis for improving predictions of community re-structuring in a warmer world.

Identifying residential neighbourhood types from settlement points in a machine learning approach.

Remote sensing techniques are now commonly applied to map and monitor urban land uses to measure growth and to assist with development and planning. Recent work in this area has highlighted the use of textures and other spatial features that can be measured in very high spatial resolution imagery. Far less attention has been given to using geospatial vector data (i.e. points, lines, polygons) to map land uses. This paper presents an approach to distinguish residential settlement types (regular vs. irregular) using an existing database of settlement points locating structures. Nine data features describing the density, distance, angles, and spacing of the settlement points are calculated at multiple spatial scales. These data are analysed alone and with five common remote sensing measures on elevation, slope, vegetation, and nighttime lights in a supervised machine learning approach to classify land use areas. The method was tested in seven provinces of Afghanistan (Balkh, Helmand, Herat, Kabul, Kandahar, Kunduz, Nangarhar). Overall accuracy ranged from 78% in Kandahar to 90% in Nangarhar. This research demonstrates the potential to accurately map land uses from even the simplest representation of structures.

Census-independent population mapping in northern Nigeria.

Although remote sensing has long been used to aid in the estimation of population, it has usually been in the context of spatial disaggregation of national census data, with the census counts serving both as observational data for specifying models and as constraints on model outputs. Here we present a framework for estimating populations from the bottom up, entirely independently of national census data, a critical need in areas without recent and reliable census data. To make observations of population density, we replace national census data with a microcensus, in which we enumerate population for a sample of small areas within the states of Kano and Kaduna in northern Nigeria. Using supervised texture-based classifiers with very high resolution satellite imagery, we produce a binary map of human settlement at 8-meter resolution across the two states and then a more refined classification consisting of 7 residential types and 1 non-residential type. Using the residential types and a model linking them to the population density observations, we produce population estimates across the two states in a gridded raster format, at approximately 90-meter resolution. We also demonstrate a simulation framework for capturing uncertainty and presenting estimates as prediction intervals for any region of interest of any size and composition within the study region. Used in concert with previously published demographic estimates, our population estimates allowed for predictions of the population under 5 in ten administrative wards that fit strongly with reference data collected during polio vaccination campaigns.

Sub-national mapping of population pyramids and dependency ratios in Africa and Asia.

The age group composition of populations varies substantially across continents and within countries, and is linked to levels of development, health status and poverty. The subnational variability in the shape of the population pyramid as well as the respective dependency ratio are reflective of the different levels of development of a country and are drivers for a country's economic prospects and health burdens. Whether measured as the ratio between those of working age and those young and old who are dependent upon them, or through separate young and old-age metrics, dependency ratios are often highly heterogeneous between and within countries. Assessments of subnational dependency ratio and age structure patterns have been undertaken for specific countries and across high income regions, but to a lesser extent across the low income regions. In the framework of the WorldPop Project, through the assembly of over 100 million records across 6,389 subnational administrative units, subnational dependency ratio and high resolution gridded age/sex group datasets were produced for 87 countries in Africa and Asia.

Mapping poverty using mobile phone and satellite data.

Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.

Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates.

BACKGROUND: Reliable health metrics are crucial for accurately assessing disease burden and planning interventions. Many health indicators are measured through passive surveillance systems and are reliant on accurate estimates of denominators to transform case counts into incidence measures. These denominator estimates generally come from national censuses and use large area growth rates to estimate annual changes. Typically, they do not account for any seasonal fluctuations and thus assume a static denominator population. Many recent studies have highlighted the dynamic nature of human populations through quantitative analyses of mobile phone call data records and a range of other sources, emphasizing seasonal changes. In this study, we use mobile phone data to capture patterns of short-term human population movement and to map dynamism in population densities. METHODS: We show how mobile phone data can be used to measure seasonal changes in health district population numbers, which are used as denominators for calculating district-level disease incidence. Using the example of malaria case reporting in Namibia we use 3.5 years of phone data to investigate the spatial and temporal effects of fluctuations in denominators caused by seasonal mobility on malaria incidence estimates. RESULTS: We show that even in a sparsely populated country with large distances between population centers, such as Namibia, populations are highly dynamic throughout the year. We highlight how seasonal mobility affects malaria incidence estimates, leading to differences of up to 30 % compared to estimates created using static population maps. These differences exhibit clear spatial patterns, with likely overestimation of incidence in the high-prevalence zones in the north of Namibia and underestimation in lower-risk areas when compared to using static populations. CONCLUSION: The results here highlight how health metrics that rely on static estimates of denominators from censuses may differ substantially once mobility and seasonal variations are taken into account. With respect to the setting of malaria in Namibia, the results indicate that Namibia may actually be closer to malaria elimination than previously thought. More broadly, the results highlight how dynamic populations are. In addition to affecting incidence estimates, these changes in population density will also have an impact on allocation of medical resources. Awareness of seasonal movements has the potential to improve the impact of interventions, such as vaccination campaigns or distributions of commodities like bed nets.

New Approaches to Marine Conservation Through the Scaling Up of Ecological Data.

In an era of rapid global change, conservation managers urgently need improved tools to track and counter declining ecosystem conditions. This need is particularly acute in the marine realm, where threats are out of sight, inadequately mapped, cumulative, and often poorly understood, thereby generating impacts that are inefficiently managed. Recent advances in macroecology, statistical analysis, and the compilation of global data will play a central role in improving conservation outcomes, provided that global, regional, and local data streams can be integrated to produce locally relevant and interpretable outputs. Progress will be assisted by (a) expanded rollout of systematic surveys that quantify species patterns, including some carried out with help from citizen scientists; (b) coordinated experimental research networks that utilize large-scale manipulations to identify mechanisms underlying these patterns;

A global synthesis of survival estimates for microbats.

Accurate survival estimates are needed to construct robust population models, which are a powerful tool for understanding and predicting the fates of species under scenarios of environmental change. Microbats make up 17% of the global mammalian fauna, yet the processes that drive differences in demographics between species are poorly understood. We collected survival estimates for 44 microbat species from the literature and constructed a model to determine the effects of reproductive, feeding and demographic traits on survival. Our trait-based model indicated that bat species which produce more young per year exhibit lower apparent annual survival, as do males and juveniles compared with females and adults, respectively. Using 8 years of monitoring data for two Australian species, we demonstrate how knowledge about the effect of traits on survival can be incorporated into Bayesian survival analyses. This approach can be applied to any group and is not restricted to bats or even mammals. The incorporation of informative priors based on traits can allow for more timely construction of population models to support management decisions and actions.

Geographical range, heat tolerance and invasion success in aquatic species.

Species with broader geographical ranges are expected to be ecological generalists, while species with higher heat tolerances may be relatively competitive at more extreme and increasing temperatures. Thus, both traits are expected to relate to increased survival during transport to new regions of the globe, and once there, establishment and spread. Here, we explore these expectations using datasets of latitudinal range breadth and heat tolerance in freshwater and marine invertebrates and fishes. After accounting for the latitude and hemisphere of each species' native range, we find that species introduced to freshwater systems have broader geographical ranges in comparison to native species. Moreover, introduced species are more heat tolerant than related native species collected from the same habitats. We further test for differences in range breadth and heat tolerance in relation to invasion success by comparing species that have established geographically restricted versus extensive introduced distributions. We find that geographical range size is positively related to invasion success in freshwater species only. However, heat tolerance is implicated as a trait correlated to widespread occurrence of introduced populations in both freshwater and marine systems. Our results emphasize the importance of formal risk assessments before moving heat tolerant species to novel locations.