Alessandro Manfrin- PhD
- PostDoc at RPTU University of Kaiserslautern-Landau
Alessandro Manfrin
- PhD
- PostDoc at RPTU University of Kaiserslautern-Landau
RTG SystemLink - https://systemlink.uni-landau.de/
Twitter - https://twitter.com/alessandro_manf
About
62
Publications
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Introduction
My scientific interests include theoretical and applied ecology, conservation and evolution, especially related to freshwater and riparian ecosystems. I am fascinated by the flux of allochthonous subsidies that link ecosystems across boundaries and how environmental anthropogenic alterations are likely to affect them.
Current institution
RPTU University of Kaiserslautern-Landau
Current position
- PostDoc
Additional affiliations
August 2017 - present
University of Applied Sciences Trier and University of Duisburg-Essen
Position
- PostDoc Position
April 2017 - August 2017
December 2013 - December 2016
Education
October 2013 - May 2017
Freie Universität Berlin and Queen Mary University of London
Field of study
- Ecology, Biology, Food Webs
September 2006 - May 2010
September 2000 - February 2005
Publications
Publications (62)
Artificial light at night (ALAN) is recognized as a contributor to environmental change and a biodiversity threat on a global scale. Despite its widespread use and numerous potential ecological effects, few studies have investigated the impacts on aquatic ecosystems and primary producers. Light is a source of energy and information for benthic auto...
Artificial light at night (ALAN) is a widespread alteration of the natural environment that can affect the functioning of ecosystems. ALAN can change the movement patterns of freshwater animals that move into the adjacent riparian and terrestrial ecosystems, but the implications for local riparian consumers that rely on these subsidies are still un...
Aquatic and terrestrial ecosystems are linked by fluxes of carbon and nutrients in riparian areas. Processes that alter these fluxes may therefore change the diet and composition of consumer communities. We used stable carbon isotope (δ¹³C) analyses to test whether the increased abundance of aquatic prey observed in a previous study led to a dietar...
Surface waters are known to be polluted by a number of contaminants including synthetic pesticides. As flooding events intensify due to climate change, the flood-mediated transfer of pesticides to terrestrial ecosystems may also increase, potentially resulting in unforeseen exposure for terrestrial food-webs. To assess the uptake and trophic transf...
Artificial light at night (ALAN) is a widespread, human-induced alteration of the landscape that affects insect dispersal and potentially contributes to insect decline. We used an experimental area with streetlights installed adjacent to an agricultural drainage ditch to experimentally assess the abundance and diversity of emerging and flying aquat...
The mosquito control agent Bacillus thuringiensis subsp. israelensis (Bti) is considered environmentally friendly
due to its highly specific mode of action. Nevertheless, adverse effects of Bti have been observed in non-biting
midges of the family Chironomidae. In this study, we applied the maximum field rate of Bti three times from
April to May to...
Agricultural pesticides occurring in surface freshwaters can be transported back to land via various transport routes, such as emerging insects or flooding. However, we lack information about whether flooding events can contribute to this transport in smaller agricultural catchments and the potential cascading effect of these pesticides on the reci...
River networks are complex ecosystems characterized by a continuous exchange of material and energy through longitudinal gradients. These ecosystems are threatened by various human-induced stressors, which frequently co-occur and may interact in complex ways, potentially triggering cascading effects in the river network. Aiming at assessing single...
Scientists have investigated the thermal tolerance of organisms for centuries, yet the field has not lost relevance as the environmental threats of thermal pollution and global change sharpen the need to understand the thermal vulnerability of organisms in landscapes increasingly subjected to multiple stressors. Freshwater fish and especially inver...
Chironomid (Diptera: Chironomidae) larvae play a key role in aquatic food webs as prey for predators like amphibian and dragonfly larvae. This trophic link may be disrupted by anthropogenic stressors such as Bacillus thuringiensis var. israelensis (Bti), a biocide widely used in mosquito control. In a companion study, we recorded a 41% reduction of...
Chironomidae are the most abundant aquatic insect group and are important in providing energy and nutrients to aquatic and terrestrial food webs. The stress response of Chironomidae can vary with environmental conditions and time of pollutant exposure, making it difficult to understand the underlying mechanisms. Here, we use a dynamic population mo...
A subsidy can directly enter a recipient ecosystem by either being consumed or being recycled to the nutrient pool with both pathways causing multiple indirect and potentially conflicting effects. Subsidy pathways are prone to anthropogenic stressors, which can impact the relative strength of the recycling and direct consumption fluxes. Current stu...
Small lentic water bodies are important emitters of methane (CH4) and carbon dioxide (CO2), but the processes regulating their dynamics and susceptibility to human-induced stressors are not fully understood. Bioturbation by chironomid larvae has been proposed as a potentially important factor controlling the dynamics of both gases in aquatic sedime...
Artificial channels, common features of inland waters, have been suggested as significant contributors to methane (CH4) and carbon dioxide (CO2) dynamics and emissions; however, the magnitude and drivers of their CH4 and CO2 emissions (diffusive and ebullitive) remain unclear. They are characterized by reduced flow compared to the donor river, whic...
Supplement to Ganglo, Caroline; Manfrin, Alessandro; Mendoza-Lera, Clara; Lorke, Andreas (accepted): Effects of chironomids density and mosquito biocide on methane dynamics in freshwater sediments. PLoS ONE
River ecosystems are heavily impacted by multiple stressors, where effects can cascade downstream of point
sources. However, a spatial approach to assess the effects of multiple stressors is missing. We assessed the local
and downstream effects on litter decomposition, and associated invertebrate communities of two stressors: flow
reduction and art...
Insect flight along river corridors is a fundamental process that facilitates sustainable succession and diversity of aquatic and terrestrial insect communities in highly dynamic fluvial environments. This study examines variations in the thickness of the insect boundary layer (i.e., the pre-surface atmosphere layer in which air velocity does not e...
This dataset contains information on species presence in and at lakes in Northern Germany. Most of the lakes are gravel pit lakes. All lakes that contain "fish data" only are natural lakes (N=6). Depending on lake and biological taxa, lakes were sampled several times. This dataset contains information on species presence from the first sampling eve...
Background
Integrative taxonomy is becoming ever more significant in biodiversity research as scientists are tackling increasingly taxonomically challenging groups. Implementing a combined approach not only guarantees more accurate species identification, but also helps overcome limitations that each method presents when applied on its own. In this...
Climate warming can lead to a replacement of species that favour cold temperatures by species that favour warm temperatures. However, the implications of such thermic shifts for the functioning of ecosystems remain poorly understood. Here, we used stream macroinvertebrate biological and ecological traits to quantify the relative contribution of col...
Resource quantity and quality can differ between adjacent ecosystems, and these differences can impact subsidies exchanged between ecosystems. The quantity and quality of subsidies are rapidly changing in response to stressors associated with global environmental change, but while we have models to predict the effects of changes in subsidy quantity...
The biocide Bacillus thuringiensis var. israelensis (Bti) is applied to wetlands to control nuisance by mosquitoes. Amphibians inhabiting these wetlands can be exposed to Bti multiple times, potentially inducing oxidative stress in developing tadpoles. For biochemical stress responses, ambient water temperature plays a key role. Therefore, we expos...
Anthropogenic stressors can affect the emergence of aquatic insects. These insects link aquatic and adjacent terrestrial food webs, serving as high-quality subsidy to terrestrial consumers, such as spiders. While previous studies have demonstrated that changes in the emergence biomass and timing may propagate across ecosystem boundaries, the physio...
SystemLink is an interdisciplinary research training group funded by the German Research Foundation (DFG) involving multiple cohorts of
international doctoral researchers closely cooperating at the iES Landau, Institute for Environmental Sciences, Germany. The goal of
SystemLink is to investigate how anthropogenic stressors on aquatic ecosystems af...
Shallow lentic aquatic ecosystems, such as ponds, are important repositories of carbon (C) and hotspots of C cycling and greenhouse gas emission. Tube-dwelling benthic invertebrates, such as chironomids, may be key players in C dynamics in these water bodies, yet their role in the C-budget at ecosystem level remains unclear. We tested whether a 41...
Aquatic micropollutants can be transported to terrestrial systems and their consumers by emergent aquatic insects. However, micropollutants, such as metals, may also affect the flux of physiologically important polyunsaturated fatty acids (PUFAs). As certain PUFAs have been linked to physiological fitness and breeding success of terrestrial consume...
Chironomid larvae often dominate aquatic macroinvertebrate communities and are a key food source for many aquatic predators, such as dragonfly and damselfly larvae (Odonata). Changes in aquatic macroinvertebrate communities may propagate through terrestrial food webs via altered insect emergence. Bacillus thuringiensis israelensis (Bti)-based larvi...
Shallow lentic freshwater aquatic systems are globally important emitters of
methane (CH4), a highly potent greenhouse gas. Previous laboratory studies
indicated that bioturbation by chironomids can reduce CH4 production and
increase CH4 oxidation by enhancing oxygen transport into sediment. Thus,
reduction in chironomid density by application of b...
Ecosystems are complex structures with interacting abiotic and biotic processes evolving with ongoing succession. However, limited knowledge exists on the very initial phase of ecosystem development and colonization. Here, we report results of a comprehensive ecosystem development monitoring for twelve floodplain pond mesocosms (FPM; 23.5 m × 7.5 m...
In a meta-ecosystem, spatially separated ecosystems are linked by biotic and abiotic cross-ecosystem flows. Hence, food webs in a meta-ecosystem are functionally linked. They are susceptible to multiple stressors threatening ecosystem functions and associated services. Although empirical studies can help understand stressor effects on meta-ecosyste...
Intraspecific diet specialization, usually driven by resource availability, competition and predation, is common in natural populations. However, the role of parasites on diet specialization of their hosts has rarely been studied. Eye flukes can impair vision ability of their hosts and have been associated with alterations of fish feeding behavior....
River floodplains hold a central role in supporting the status of water, nature and biodiversity conservation, climate change mitigation, and ecosystem services. They build an important link between rivers and their catchments, mainly through their water retention capacity and the lateral connectivity controlled by flood events and groundwater exch...
Vorliegende Studie erfasst erstmalig sämtliche Standgewässer in Niedersachsen und zeigt, dass künstlich ge-schaffene kleine Baggerseen der dominierende Gewässertyp der Region sind. Zudem legt diese Studie verglei-chende Ergebnisse zur Biodiversität anglerisch bewirtschafteter und unbewirtschafteter Baggerseen vor. Ob-wohl die anglerisch bewirtschaf...
Southern Africa has one of the highest densities of temporary pools and some of the most understudied temporary wetlands in the world. Additionally, the eastern African annual killifishes (Nothobranchius spp.) are rare in southern Africa and found exclusively in temporary habitats. There is a notable lack of literature regarding the diet of these f...
Die heimische Flora und Fauna reagieren auf künstliche Beleuchtung. Die Bewertung der Auswirkungen von künstlicher Beleuchtung auf insbesondere dämmerungs- und nachtaktive Arten und ihre Lebensgemeinschaften ist schwierig, weil Änderungen der Beziehungen und Abhängigkeiten von Arten und Lebensgemeinschaften unter dem Einfluss von künstlichem Licht...
Assessments of river restoration outcomes are mostly based on taxonomic identities of species, which may not be optimal because a direct relationship to river functions remains obscure and results are hardly comparable across biogeographic borders. The use of ecological species trait information instead of taxonomic units may help to overcome these...
The increasing impairment of lotic ecosystems has promoted a growing effort into assessing their ecological status by means of biological indicators. While community-based approaches have proven valuable to assess ecosystem integrity, they mostly reflect long-term changes and might not be suitable for tracking and monitoring short-term events. Resp...
In recent decades, the use of artificial illumination has rapidly increased, transforming nocturnal environments worldwide. Inappropriate and extensive use of artificial illumination results in light pollution: an increase in nocturnal light above natural levels and a disruption of natural light cycles. Light/dark cycles have been stable over geolo...
The increasing impairment of lotic ecosystems has promoted a growing effort into assessing their ecological status by means of biological indicators. Community-based approaches are a valuable way of assessing ecosystem changes, but reflect local extirpation and long-term changes and might not be suitable for tracking and monitoring short-term and s...
The increasing use of artificial light at night (ALAN) has led to exposure of freshwater ecosystems to light pollution worldwide. Simultaneously, the spectral composition of nocturnal illumination is changing, following the current shift in outdoor lighting technologies from traditional light sources to light emitting diodes (LED). LEDs emit broad-...
Freshwaters are increasingly exposed to artificial light at night (ALAN), yet the consequences for aquatic primary producers remain largely unknown. We used stream-side flumes to expose three-week-old periphyton to LED light. Pigment composition was used to infer community changes in LED-lit and control periphyton before and after three weeks of tr...
The increasing impairment of lotic ecosystems has promoted a growing effort into assessing their ecological status by means of biological indicators. While community-based approaches have proven valuable to assess ecosystem integrity, they mostly reflect long-term changes and might not be suitable for tracking and monitoring short-term events. Resp...
Artificial light at night (ALAN) is one of the most obvious hallmarks of human presence in an ecosystem. The rapidly increasing use of artificial light has fundamentally transformed nightscapes throughout most of the globe, although little is known about how ALAN impacts the biodiversity and food webs of illuminated ecosystems. We developed a large...
Although the assembly of stream macroinvertebrates is regulated by environmental heterogeneity at multiple spatial scales, field bioassessment studies that explicitly considered such scale-dependency are rare. Here, we investigated how large scale longitudinal gradients and local microhabitat structure jointly regulate the assembly of macroinverteb...
Although the assembly of stream macroinvertebrates is regulated by environmental heterogeneity at multiple spatial scales, field bioassessment studies that explicitly considered such scale-dependency are rare. Here, we investigated how large scale longitudinal gradients and local microhabitat structure jointly regulate the assembly of macroinverteb...
The main aim of this study was to improve the knowledge about the concordance among macrophytes and
macroinvertebrates to provide complementary information and facilitate the procedures for quality
assessment of river ecosystems. Macrophytes and macroinvertebrates were collected in 11 sampling sites
along a central Apennine calcareous river in Octo...
Several studies on concordance between macrophyte and macroinvertebrate
communities were carried out for decades while any investigation on co-occurrence of single pair of taxa of these two groups was never addressed. Our main aim was to verify the existence of co-occurrence of single macrophyte–macroinvertebrate pair in a Mediterranean river of ce...
Inland waters are constituted by a lot of seriously threatened habitats. The increasing need to safeguard these ecosystems led European Union Member States to propose the Water Framework Directive which decided the creation of homogeneous areas characterized by very similar geology, topography and climate, known as hydroecoregions (HER) and firstly...
River impoundments and waste water discharge are a serious threat to the integrity and biodiversity of river ecosystems, especially in central Italy. Benthic macroinvertebrates were sampled in autumn and summer along the Aniene River to assess the cumulative biological effect of the numerous dams and sewage treatment plants that affect its middle a...
The main aim of this study was to improve the knowledge about the concordance among macrophytes and macroinvertebrates to provide complementary information and facilitate the procedures for quality assessment of river ecosystems. Macrophytes and macroinvertebrates were collected in 11 sampling sites along a central Apennine calcareous river in Octo...
L‘analisi delle minacce costituisce un processo di valutazione che comprende una fase di classificazione, quantificazione, comparazione, ranking di questi eventi. La quantificazione delle minacce può essere effettuata con un approccio expert-based, acquisendo punteggi (scores) da parte di esperti tecnici sia dei target di conservazione che del sito...
Questions
Questions (20)
Dear Colleagues,
I was wondering if you could suggest how to analyze insect community (multivariate) data collected across multiple sites and time points. Specifically, I aim to assess differences in community composition between Treatment and Control conditions.
What are my options regarding:
- Multivariate Time Series modeling?
- Multivariate Mixed-Effect Models?
- Latent Models (e.g., Generalized Linear Latent Variable Models, gllvm)?
- Machine Learning approaches?
I’m aware of Multivariate Time Series analysis applied in fields like Finance and likely many other approaches that could be relevant. However, I am struggling to determine the most appropriate method for my case.
If you have experience in this area or can recommend a good blog, tutorial, or resource, I would greatly appreciate your suggestions.
Thank you for your time!
Dear all,
I would like to start a discussion here on the use of generalised mixed effect (or additive) models to analyse count data over time. I reported here the "few" analyses I know in R for which I found GOOD (things) and LIMITS /DOUBTS. Please feel free to add/ comment further information and additional approaches to analyse such a dataset.
Said that, generalised mixed effect modelling still requires further understanding (at least from me) and that my knowledge is limited, I would like to start here a fruitful discussion including both people which would like to know more about this topic, and people who knows more.
About my specific case: I have counted data (i.e., taxa richness of fish) collected over 30 years in multiple sites (each site collected multiple times). Therefore my idea is to fit a model to predict trends in richness over years using generalised (Poisson) mixed effect models with fixed factor "Year" (plus another couple of environmental factors such as elevation and catchment area) and random factor "Site". I also believe that since I am dealing with data collected over time I would need to account for potential serial autocorrelation (let us leave the spatial correlation aside for the moment!). So here some GOOD (things) and LIMITS I found in using the different approaches:
glmer (lme4):
GOOD: good model residual validation plot (fitted values vs residuals) and good estimation of the richness over years, at least based on the model plot produced.
LIMITS: i) it is not possible to include correction factor (e.g., corARMA) for autocorrelation.
glmmPQL(MASS):
GOOD: possible to include corARMA in the model
LIMITS: i) bad final residual vs fitted validation plot and completely different estimation of the richness over years compared to glmer; ii) How to compare different models e.g., to find the best autocorrelation structure (as far as I know, no AIC or BIC are produced)? iii) I read that glmmPQL it is not recommended for Poisson distributions (?).
gamm (mgcv):
GOOD: Possible to include corARMA, and smoothers for specific dependent variables (e.g., years) to add the non-linear component.
LIMITS (DOUBTS): i) How to obtain residual validation plot (residuals vs fitted)? ii) double output summary ($gam; $lme): which one to report? iii) in $gam output, variables with smoothers are not estimated (only degree of freedom and significance is given)? Is this reported somewhere else?
If you have any comment, please feel free to answer to this question. Also, feel free to suggest different methodologies.
Just try to keep the discussion at a level which is understandable for most of the readers, including not experts.
Thank you and best regards
Dear all,
My question is the following:
I have large datset: 100,000 observations, 20 numerical and 2 categorical variables (i.e. mixed variables)
I need to cluster these observations based on the 22 variables, I have no idea how many clusters/groups a priori I should expect.
As the large dataset I use clara() function in r (based on "pam").
Because of the large number of observations, there is no way to compare distance matrixes (R does not allow such calculations, and is not a problem of RAM), therefore the common way of cluster selection using treeClust() and pamk() and comparison of "silhouette" does not work.
My main quesitons is: can I use factors like total SS, within SS, between SS to have an idea of the best performing Tree (in terms of number of clusters)? Do you have any other idea of how can I select the right number of clusters?
Best regards
Alessandro
Dear all,
I am trying to understand the diet of different fish parasites using stable isotope analysis. I have indications (isospace) that some of them are feeding directly on the fish tissues, but others on other sources (not fish tissues). How do I correct C13 and N15 for fractionation in this case? Are you aware of studies and /or correction factors applied to fish parasites?
Best regards
Alessandro Manfrin
Dear all,
do you know if:
1 - can I run an RDA with negative (taxa) values (as delta Control - Treatment)?
2 - Do I have to use the function decostand function on these delta values before performing the RDA?
3 - Shall I use Bray-Curtis distance (dist='bray") in the RDA function?
Best
Alessandro
Dear all,
I have a dataset of fish collected in different rivers over different years each of them sampled a different number of times during different projects . This different number of observations among rivers in some cases can be important: e.g.
River X = 1 project (1 observation=1 sampling x 1 year);
River Y = 5 projects (15 observations= 1 sampling x 3 years x 5 projects);
River Z= 15 projects (105 observations=1 sampling x 7 years x 15 projects);
I want to calculate in the all region (so not interested in specific rivers) how the abundance is related to Years, Latitude, Altitude and Anthopic pressures (APindex). I thought to use the following model:
lme: Abu~Years+Latitude+Altitude+APindex + (1|river/project) + corrARMA (form = time|River/project).
-What is the influence of RiverZ with its 105 observation compared to the other rivers which have less number of observations?
-Am I accounting for this unbalanced observations in the random structure (1|river/project)?
-Do I have to account in the model for the different number of observations with (weight=1/n observation for each project?)
Thank you
Manfrin et al. 2018. Dietary changes in predators and scavengers in a nocturnally illuminated riparian ecosyst. Oikos. DOI: 10.1111/oik.04696.
Artificial Light is affecting spider diet as a consequence of changes in aquatic-terrestrial insect prey fluxes.
best
Alessandro
Dear all,
I have maybe for the "time series" experts a silly question:
-I have a dataset of European rivers =80
-In 50% of the rivers I have more than 1 project; in the other 50% is 1 river = 1 project
-In 50 % of the projects I have data collected only for 1 year; in the other 50% of the projects data were collected over years (from 2 untill 20 years, depending on the project)
->I want to assess the Fish diversity depending on the altitude, latitude, catchment size.
After exploring data for the model assumption of normality, variance heterogeneity etc..I though to run this model:
mod<-lme(Fish Diversity~log(altitude)+log(latitude)+log(catchment size), random~1|Rivers/Projects, method="ML", data=dati)
When I look at the residuals of model mod and at the acf (residuals(mod) and pacf(residuals(mod), they are pretty good but in acf there is autocorrelation in lag1
and in pacf the line goes slightly over in lag 3. I think I would give it a try with CorAR1 (p=1) correction in lme.
My questions are:
1- Is the model developed in your opinion correct?
2- Can I fit a correlation CorrAR1 in the lme by just looking at the acf and pacf plots from the model mod? As u see I have different project over time that means potentially multiple time series (for each project). Can I just fit a unique AR1 structure looking at the residuals of the model (without CorrAR1) and not at the raw data and assume that the same temporal trend is present in all the projects analysed? How can the acf and pacf know what is the temporal repetion (i.e.
how the acf and pacf biuld the lags in the plots)?
3- if the question number 2 is yes, do I have to organise in the dataframe chronologically in the dataset for each project? (e.g. Project1 from 2000 untill 2008; Project 2 from 1998 untill 2015, and so on?) as dati[order(dati$Project_names, dati$Year_evaluation), ]
and give to the corrAR1 the form structure form=1|Rivers/Project_names
Would this model be ok?
modAR<-lme(Abu~log(altitude)+log(latitude)+log(catchment size), random~1|Rivers/Project_names, method="ML",
correlation=corARMA(form = ~1|Rivers/Project_names, p=1)
Thank you for your time
Alessandro
What is the best way to calculate the delta of a Control vs a Impact condition? In my study I have many rivers restored that are compared to a temporal or spatial unrestored condition. I would like to observe patterns of change between restored vs unrestored rivers over time, For this reason I thought to calculate the effect as DELTA. What do you think is the best way to calculate this?
1- Control - Impact
2- (Control-Impact)/Control
Is there a substantial difference?
Thanks for your consideration
Alessandro
Dear all,
I am struggling to find the best way to analyse my dataset. I have to compare fish diversity in restored and unrestored river sites over time. Fish samples were collected from 80 comparable rivers. However, in some rivers the sampling was conducted only 1 time, in other rivers the sampling was conducted up to 15 times (once per year).
To start I thought to use this simple mixed effect model:
Fish diversity~restoration*years from restoration, random=(1|rivers).
However, I believe I have to run my model accounting for temporal autocorrelation (ARMA correction in lme) as data in many rivers are collected mulitple times over years.
My question is: can I use such a correction when in my daset I have rivers in which the sampling was conducted only 1 year while in other rivers the sampling was conducted multiple years (up to 15)?
Thanks for your consideration
Alessandro
