Ecology

Deforestation, in places such as the Amazon, contributes to biodiversity loss.Credit: Ivan Valencia/Bloomberg/Getty

Researchers are relieved that a pivotal summit to finalize a new global agreement to save nature will go ahead this year, after two-years of delays because of the pandemic. But they say the hard work of negotiating an ambitious deal lays ahead.The United Nations Convention on Biological Diversity (CBD) announced yesterday that the meeting will move from Kunming in China to Montreal in Canada. The meeting of representatives from almost 200 member states of the CBD — known as COP15 — will now run from 5 to 17 December. China will continue as president of the COP15 and Huang Runqiu, China’s minister of ecology and environment, will continue as chairman.Conservation and biodiversity scientists were growing increasingly concerned that China’s strict ‘zero COVID’ strategy, which uses measures such as lockdowns to quash all infections, would force the host nation to delay the meeting again. Researchers warned that another setback to the agreement, which aims to halt the alarming rate of species extinctions and protect vulnerable ecosystems, would be disastrous for countries’ abilities to meet ambitious targets to protect biodiversity over the next decade.“We are relieved and thankful that we have a firm date for these critically important biodiversity negotiations within this calendar year,” says Andrew Deutz, an expert in biodiversity law and finance at the Nature Conservancy, a conservation group in Virginia, US. “The global community is already behind in agreeing, let alone implementing, a plan to halt and reverse biodiversity loss by 2030,” he says.With the date now set, Anne Larigauderie, executive secretary of the Intergovernmental Platform on Biodiversity and Ecosystem Services, says the key to success in Montreal will be for the new global biodiversity agreement to focus on the direct and indirect drivers of nature loss, and the behaviors that underpin them. “Policy should be led by science, action adequately resourced and change should be transformative,” she adds.New locationThe decision to move the meeting came about after representatives of the global regions who make up the decision-making body of the COP reached a consensus to shift it to Montreal. China and Canada then thrashed out the details of how the move would work. The CBD has provisions that if a host country is unable to hold a COP, the meeting shifts to the home of the convention’s secretariat, Montreal.Announcing the decision, Elizabeth Mrema, executive secretary of the CBD, said in a statement, “I want to thank the government of China for their flexibility and continued commitment to advancing our path towards an ambitious post 2020 Global Biodiversity Framework.”In a statement, Runqiu said, “China would like to emphasize its continued strong commitment, as COP president, to ensure the success of the second part of COP 15, including the adoption of an effective post 2020 Global Biodiversity Framework, and to promote its delivery throughout its presidency.”China also agreed to pay for ministers from the least developed countries and small Island developing states to travel to Montreal to participate in the meeting.Work aheadPaul Matiku, an environmental scientist and head of Nature Kenya, a conservation organization in Nairobi, Kenya, says the move “is a welcome decision” after “the world lost patience after a series of postponements”.But he says that rich nations need to reach deeper into their pockets to help low- and middle-income countries — which are home to much of the world’s biodiversity — to implement the deal, including meeting targets such as protecting at least 30% of the world’s land and seas and reducing the rate of extinction. Disputes over funding already threaten to stall the agreement. At a meeting in Geneva in March, nations failed to make progress on the new deal because countries including Gabon and Kenya argued that the US$10 billion of funding per year proposed in the draft text of the agreement was insufficient. They called for $100 billion per year in aid.“The extent to which the CBD is implemented will depend on the availability of predictable, adequate financial flows from developed nations to developing country parties,” says Matiku.Talks on the agreement are resuming in Nairobi from 21-26 June, where Deutz hopes countries can find common ground on key issues such as financing before heading to Montreal. Having a firm date set for the COP15 will help push negotiations forward, he says.“Negotiators only start to compromise when they are up against a deadline. Now they have one,” he says. More

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Mathematical theoryWe consider a biological response (e.g. body size, survival, biodiversity) to two environmental drivers (i.e. any abiotic or biotic factor) but the same idea may be applied to a larger number of drivers. The response depends of a set of predictors consisting in the magnitudes (m1 and m2) and time scales of fluctuation of two drivers (i = 1, 2); in addition, the response is quantified at least once after the fluctuations have been experienced (Fig. 1a).Time is defined using two different frames; chronological (= clock) time (measured by clocks) and biological time. For the “clock” time scales of the fluctuations (t1, t2) there are associated biological times (τ1, τ2). Likewise, for the clock time at which the response is quantified (({t}^{^*})) there is an associated biological time (τ({^*})).Biological time is the proportion of (clock) time needed to reach a life history event (e.g. moulting, maturity). Hence, for t1, t2 and t({^*}) we obtain τi = ti/D and τ({^*}) = t({^*})/D, (D = clock time needed to reach such life history event). We express the τi and τ({^*}) in terms of a function L = 1/D. For instance, for t({^*}) we obtain:$${tau }^{^*}={t}^{^*}cdot{L}$$
(1)
where L = L(ω) = D−1(ω) characterises the timing of a life history event (with units as the inverse of clock time units). L depends on the set of predictors ω associated to the fluctuations; an important set of predictors will be defined by thermal fluctuations (the amplitude and time scales), which in ectotherm species have a strong influence on developmental time32,33. We find by differentiation that L provides the transform function between clock and biological time frames; for instance, if L does not depend on any ti we have L = dτ/dti.The response is expressed as a function of the predictors defined above, as R(m1, m2, t1, t2, t({^*})) = r[m1, m2, τ1 (t1), τ2(t2), τ({^*})]. The contribution of each predictor to the response is better understood by the partial derivatives with respect to each predictor; this defines a system of partial differential equations (PDE; Supplementary note 1) which expressed in matrix form give the following matrix equation.$$left[begin{array}{c}frac{dR}{d{m}_{1}}\ frac{dR}{d{m}_{2}}\ frac{dR}{d{t}_{1}}\ frac{dR}{d{t}_{2}}\ frac{dR}{d{t}^{^*}}end{array}right]=left[begin{array}{ccccc}1& frac{d{m}_{2}}{d{m}_{1}}& frac{d{tau }_{1}}{d{m}_{1}}& frac{d{tau }_{2}}{d{m}_{1}}& frac{d{tau }^{^*}}{d{m}_{1}}\ frac{d{m}_{1}}{d{m}_{2}}& 1& frac{d{tau }_{1}}{d{m}_{2}}& frac{d{tau }_{2}}{d{m}_{2}}& frac{d{tau }^{^*}}{d{m}_{2}}\ frac{d{m}_{1}}{d{t}_{1}}& frac{d{m}_{2}}{d{t}_{1}}& frac{d{tau }_{1}}{d{t}_{1}}& frac{d{tau }_{2}}{d{t}_{1}}& 0\ frac{d{m}_{1}}{d{t}_{2}}& frac{d{m}_{2}}{d{t}_{2}}& frac{d{tau }_{1}}{d{t}_{2}}& frac{d{tau }_{2}}{d{t}_{2}}& 0\ 0& 0& 0& 0& frac{d{tau }^{^*}}{d{t}^{^*}}end{array}right]cdot left[begin{array}{c}frac{dr}{d{m}_{1}}\ frac{dr}{d{m}_{2}}\ frac{dr}{d{tau }_{1}}\ frac{dr}{d{tau }_{2}}\ frac{dr}{d{tau }^{^*}}end{array}right]$$
(2)
In the PDE (Eq. 2), the left-hand side is a vector column of the derivatives of the response in clock time (R), with respect to each predictor; the right-hand side is the standard (= inner) product of a matrix (M) by a vector of the derivatives of the response in biological time (r), i.e. R = Mr. The matrix contains the derivatives of the predictors with respect to each other, with time both expressed in clock or biological scales; one can think of M as an object containing coefficients that transform r into R in the same way as a constant (= 1000) would transform kilometres into meters of distance. The large number of terms in M highlights the considerable diversity and the challenges in quantifying responses to multivariate environmental fluctuations. We show below how to use Eq. (2) to quantify the effect of fluctuating environmental drivers on biological responses, as mediated by biological time.First, we note that M contains three groups of terms: (1) Terms accounting for situations where the magnitude of a driver affects the magnitude of the second driver (e.g. temperature drives oxygen concentration in aquatic habitats): these are dmi/dmj for any i, j = 1, 2. (2) Terms accounting for cases where the magnitudes and time scales of stressors are related: dmi/dtj and dmi/dti. (3) Terms where biological time depends on the magnitude or time scale of the environmental fluctuation dτi/dtj and dτi/dmj. The terms of groups (1) and (2) are zero when they are mutually independent, such as in a factorial experiment with orthogonal manipulation. We will set those to zero in the rest of this analysis.Second, we note that for group (3) there are three scenarios: (3a) biological time does not depend on any environmental driver. This is the trivial case where biological time is proportional to clock time, not considered here; M is simplified to a diagonal matrix, i.e. with constants in the diagonal, and zero’s otherwise leading to a single constant term per equation (3b). Biological time depends on the magnitudes of any or both drivers. In such case, τ1 τ2, and τ({^*}) will be driven by the same equation: if τi = ti · L (m1, m2) we obtain dτi/dtj = dτi/dti = L (m1, m2). (3c) Biological time depends on the time scale of the fluctuations: in such case, differentiating Eq. (1) with respect to time, we obtain dτi/dti = L + ti dL/dti.Here, we explore four special cases where the equations are simplified to highlight the importance of biological time in modifying the responses as compared to clock time. We start with the simplest case where there is a single environmental variable and then we consider cases with two variables. We focus on cases representing the most frequent experiments carried out on multiple driver research, i.e. factorial manipulations where terms of the groups 1 and 2 are zero.Case 1: responses to the magnitude of a single variableWe start with the simplest case i.e. where the response is driven by the magnitude of a single driver, e.g. temperature (= m). Examples of this case are laboratory experiments quantifying the effect of temperature on body mass or survival of a given species, or mesocosm experiments quantifying effects of temperature on species richness where thermal treatments are kept constant over time. Here, the response is quantified at different times, both in the clock and biological frames. In such case we have R(m, t({^*})) = r[m, τ({^*})(m, t({^*}))] and the PDEs simplify to.$$frac{dR}{dm}=frac{partial r}{partial m}+frac{partial r}{partial {tau }^{^*}}cdot frac{d{tau }^{^*}}{dm}$$
(3)
From Eq. (3), and because dR/dm ≠ dr/dm, we see that the response to the magnitude of the driver depends on a component quantifying the effect biological time: as long as dτ({^*})/dm ≠ 0 the time reference frame affects the observed effect of m on the response. The simulation illustrated in Fig. 2 shows a case where there are differences between the observed responses at clock vs biological times. In the simulated experiment, there is a strong effect of the magnitude of the driver on the response at clock time, but such effect is much less pronounced at biological time. By contrast, there is no effect when the response is measured in the biological time frame.Figure 2Case 1: Response to the magnitude of a single variable (m). Horizontal line: measurement taken at clock time t({^*}) = t({^*})c; note that, along the line, the response increase with m (it crosses the colour gradient). Curve with yellow circles: measurements taken at a constant biological time (τ({^*})c = 100); along the curve, the response does not vary with m. The equations used were: R = m(0.5t({^*})), τ({^*}) = t. m giving r = 0.5. τ({^*}) not depending on m.Full size imageEquation (3) (details in Supplementary code 1) captures an obvious but important feature of experiments manipulating temperature over the development of ectotherms, for instance, from birth to metamorphosis; namely that there is no consistent definition of a simultaneous event across the different time frames. Experiments are usually stopped at different clock times because organisms must be sampled at the same biological time. All points located in the horizonal line in Fig. 3 represent simultaneous events, as defined in clock time occurring at different temperatures (e.g. whether an animal is dead or alive); however, simultaneous events occurring in biological time are represented by the points on the curve. Hence, Fig. 2 gives a geometric representation of such fact. Temperature as a driver of developmental rates32 is a central candidate to produce responses that differ at clock vs biological time.
We explore further this case with an example where the response is expressed as a function of time and an instantaneous rate μ(m) quantifying for instance mortality, growth or biomass loss. For this example, we obtain R(m, t({^*})) = r[μ(m), τ({^*})(m, t({^*}))]. By differentiating in both sides, we get:$$frac{dR}{dm}=frac{partial r}{partial mu }cdot frac{dmu }{dm}+frac{partial r}{partial {tau }^{^*}}cdot frac{d{tau }^{^*}}{dm}$$
(4)
Equation (4) shows that m affects the response through two components: the instantaneous rate (dμ/dm) and the biological time (dτ({^*})/dm). We call the first component “eco-physiological” and the second component “phenological” (m drives the timing of a biological event, e.g. time to maturation). Those components are not evident if the response is expressed in clock time; otherwise we would obtain dR/dm = ∂R/∂μ · dμ/dm.In order to better understand Eq. (4), consider an example where the response is biomass loss experienced by an organism during the process of migration (e.g. towards a feeding or reproductive ground); when the access to food during migration is very limited the result should be a decrease in body mass reserves through time. Let biomass (B) be modelled as an exponential decaying function of time and an instantaneous rate of biomass loss μ; let μ depend on temperature (= m) such that, μ = μ(m). In such case we obtain:$$B(m,t)={e}^{-mu left(mright)cdot {t}^{^*}}={e}^{-mu left(mright)cdot {tau }^{^*}left(m,{t}^{^*}right)}$$
(5)
By differentiation in both sides of Eq. (5) we get:$$frac{dB}{dm}={-e}^{-mu left(mright)cdot {tau }^{^*}left(m,{t}^{^*}right)}left{{tau }^{^*}cdot frac{dmu }{dm}+mu cdot frac{d{tau }^{^*}}{dm}right}$$
(6)
Equation (6) shows the eco-physiological (dμ/dm) and phenological components (dτ({^*})/dm) within the brackets. If μ responds linearly to temperature, then dμ/dm would be represented by a constant quantifying the thermal sensitivity of biomass loss; the value of such constant would depend on physiological processes associated to use of reserves to sustain movement and the basal metabolic rate. Likewise, if τ({^*}) responds linearly to temperature, the dτ({^*})/dm would be driven by a constant controlling the sensitivity of developmental time to temperature.Because biomass is a trait that is central to fitness, Eq. (6) gives the indirect contribution of phenological and physiological responses to fitness. Assuming that fitness should be maximised, adaptive responses should involve the mitigation of negative effect of m on both components of Eq. (5), represented by the partial derivative of the right-hand term. For instance, organisms with the ability to minimise the eco-physiological effect (through e.g. a compensatory physiological mechanisms) or the phenological effect (e.g. shortening the exposure time) would complete the migration minimal loss of reserves.By generalization, Eqs. (4–6) help us to provide biological meaning to the terms of the matrix M: any term of the form dτ({^*})/dmj, dτi/dmj or dτi/dtj represents the effect of an environmental driver on the timing of a phenological event; hence, they are phenological components. Terms that contain the effect of an environmental variable on an instantaneous rate are eco-physiological components. By substitution we find that the terms of the matrix in Eq. (2) can be classified in two categories according to whether the component is eco-physiological (E) or phenological (P):$$left[begin{array}{ccccc}E& 0& P& P& P\ 0& E& P& P& P\ 0& 0& P& P& 0\ 0& 0& P& P& 0\ 0& 0& 0& 0& Pend{array}right]$$
(7)
Case 2: multiple driver responsesHere we expand the previous case by looking at a response to the magnitude of two different drivers; i.e. keeping the levels of each driver constant over the duration of the experiment. Examples of this case are experiments quantifying the effect of temperature and nutrient load on body mass (e.g. in a rearing containers) or species richness (e.g. in mesocosms). This case is represented by the terms of first two rows of the matrix and the vectors of Eq. (2), with the terms of the remaining rows set to zero. Here, there are different scenarios, but we focus on the one highlighting the importance of biological time.Consider a case where biological time depends on the magnitude of the first driver while the response is explicitly driven by the magnitude of the second driver (Fig. 4). For instance, the response may be the survival rate of a host organism exposed to different temperature and parasitic load. The response in clock time is described as R(mP, t({^*})). The driver controlling the biological time is temperature (mT) while the parasitic load (mP) controls survival. In such case, dτ({^*})/∂mP = 0, dR/dmP ≠ 0 and dR/dmT = 0. Although by definition the response in clock time does not depend on mT , it will do so in biological time. This is because, applying the matrix multiplication in Eq. (2), we obtain:
$$frac{partial R}{partial {m}_{T}}=frac{partial r}{partial {m}_{T}}+frac{partial r}{partial tau *}cdot frac{dtau *}{d{m}_{T}}$$
(8a)
$$0=frac{partial r}{partial {m}_{T}}+frac{partial r}{partial tau *}cdot frac{dtau *}{d{m}_{T}}$$
(8b)
$$frac{partial r}{partial {m}_{T}}=-frac{partial r}{partial tau *}cdot frac{dtau *}{d{m}_{T}}$$
(8c)
The second right-hand term in Eq. (8a) quantifies the effect of temperature on the response mediated by biological time. In order to better understand the responses, consider a simple linear response: R = R0 − mP·t({^*}) and notice that, for a fixed clock time (t({^*})c) the effect of the magnitude of parasitism is constant (dR/dmP = −t({^*})c); hence, the response can be understood, geometrically, as a flat surface with slope not depending on temperature. Now, note that under the specific conditions of our example, r = R0 − mP·τ({^*})/L(mT). Hence, for a fixed biological time (τ({^*})c) we obtain ∂r/∂mP = −τ({^*})c/L(mT); i.e. the importance of the parasitic effect depends now on temperature. In addition, this example is valid for the case of additive effects of any two environmental drivers: assuming R = R0 − (a1·mP + a2· mT)·t({^*}) (a1, a2 are constants), we obtain dR/dmP = −a1t({^*}); however, ∂r/∂mP = −a1τ({^*})c/L(mT). In words, additive effects observed in clock time become interactive in biological time. This is illustrated in the simulation (Supplementary code 2) depicted in Fig. 4: the response in clock time depends on a single driver (parasite load); however, the response in biological time is interactive, i.e. the effect of parasite load depends on temperature.Figure 3Case 2: Multiple driver responses. (A) Modelled responses (colour scale) at a specific clock (t({^*}) = 40) and biological times (τ({^*}) = 1), showing an interactive effect only in the biological time frame. (B) Interaction plots of the responses for specific levels of temperature and a second driver showing that the effect high temperature mitigates the negative effect of the second driver on the response. The response was modelled with as a sigmoidal function R = exp(−t({^*})φ) with φ = 0.1[1 + exp(m2/2)]−1 to produce a strong gradient in the range of m2 = 25–30 units. The biological time was modelled based on the effect of temperature on the development of marine organisms33 as so that t({^*}) = τ({^*}) exp[−22.47 + 0.64/(k(m1 + 273)], i.e., using the Arrhenius equation with k: Boltzmann constant (≈ 8.617 10–5 eV K−1).Full size imageCase 3: role of clock and biological time scale of fluctuationPrevious examples did not consider, the time scale of the fluctuations as drivers of the response. Here we explore how a biological variable (= survival rate) responds to different levels of magnitude of a driver (= temperature) and to simultaneously changing the time scale of a fluctuation (from clock to biological time) of a second driver (= food limitation). As model, we use larval stages of a crab because there is sufficient information on the effect of temperature and food levels on survival and the timing of moulting33,34.We performed the so-called point-of-reserve-saturation experiment (PRS35), i.e. exposing groups of recently hatched larvae of the crab Hemigrapsus sanguineus to different initial feeding periods (= our time scale of fluctuation), after which they were starved until they either died or moulted to the second larval stage (Supplementary Fig. 1). H. sanguineus is originated from East Asia but has invaded the Atlantic shores of North America and North Europe36,37. This experiment was carried out at 4 temperature levels (15–21 °C), within the range of thermal tolerance of larvae of this species, i.e. where the magnitude of temperature does not affect survival38,39. In addition, because there is a single level of food limitation (= starvation), the magnitude of food limitation (mF) is not considered as a variable in the example.The response variable was the proportion of first stage larvae surviving the moulting event to the second stage, set to biological time τ({^*}) = 1. In response to different starvation periods (preceded by feeding), the survival shows a sigmoidal pattern35, characterised by a parameter, PRS50. This is the point of development where larval reserves are “saturated”; i.e. enough reserves have been accumulated during the previous feeding period to ensure survival and moulting to the next stage.Under the conditions of the experiment, the survival proportion (= R) is driven only by the time scale of a fluctuation (here t1 = t, τ1 = τ for simplicity), characterised by the starvation period; hence, R = R(t) = r[τ(t)] given that there is a single time of observation fixed to τ({^*}) = 1. Because biological time does not depend t, we get L = dτ/dt and:$$frac{dR}{dt}=frac{partial r}{partial tau }cdot mathcal{L}({m}_{2})$$
(9)
Equation (9) is represented in the PDE by the terms of row 3 and column 4 of M multiplied by the term of row 3 of the column vector r; dτ/dt = L(m), m represents the magnitude of temperature.The relationship between biological time and temperature was best explained by a power function D(T) = aTb (Fig. 4A, Supplementary Table 1, Supplementary Fig. 2), in consistence with previous studies36,40. The interaction between starvation time and temperature was weak (Supplementary Fig. 3); best models retained starvation time only at 21 °C where the percentage of explained variance was still low (R2  More

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InvasionWe used freshwater fish biodiversity data collated by and described in Milardi, et al.47. In summary, the dataset included 3777 sites sampled 1999–2014, recorded a total of 99 different fish species (35 of which were exotic and already established, even if some are restricted to areas with thermal springs), spanned  > 11 degrees of longitude (~ 1200 km) and 10 degrees of latitude (~ 1100 km), covering streams at altitudes -2.7–2500 m above sea level. Community turnover was not a relevant factor in our study, because fish communities are typically stable over these timescales and the data was collected in a restricted timeframe within each area29,39. Furthermore, time elapsed since last introductions was sufficient to analyze distribution patterns after major invasions had already occurred see e.g.23,48.Abundance of each species sampled during the monitoring was recorded with Moyle classes (Moyle and Nichols, 1973), which were weighted according to body-size classes in order to obtain a body-mass-corrected abundance, hereafter referred to simply as abundance. We then calculated an invasion degree, i.e. the share of introduced species in freshwater fish communities, based on the abundance of introduced and native species see e.g.9,49. A high invasion degree equals to a high share of introduced species and a low share of native species.We also selected the top 10 invasive species as further response variables, under the assumption that these would be the main components of the invasion degree, but would respond to different invasion drivers based on each species’ ecology. Invasiveness rank was defined through an index obtained by multiplying colonization (share of sites colonized) and prevalence (average relative abundance in the fish community) of each introduced species. The relative abundance of each of these species in the fish community was used as a response variable, being a measure comparable to invasion degree for single species.Invasion driversWe tested a combination of geographical, climate and anthropogenic impact factors as potential drivers of invasion. To avoid temporal mismatches, we chose time periods that overlapped as much as possible with our biological data.We used basin area, altitude and slope (derived from a seamless digital elevation model of the whole Italian territory at 10 m resolution, Tarquini, et al.50) as geographical variables.We derived climate data from available series of long-term national monitoring (http://www.scia.isprambiente.it/). We used daily air temperature (2000–2009), measured at a total of 2266 sites throughout the country, as a proxy for temperature regimes. We also used cumulated annual precipitations, number of annual dry days (precipitation  More